Enrichment and detection of sulfadimethylpyrimidine in food by magnetic molecularly imprinted photonic crystals

Enrichment and detection of sulfadimethylpyrimidine in food by magnetic molecularly imprinted photonic crystals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enrichment and detection of sulfadimethylpyrimidine in food by magnetic molecularly imprinted photonic crystals Yitong Yin, Xin Wang, Huihui Hao, Xiaolei Zhao, Jinxing He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6687241/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract A core-shell structured magnetic molecularly imprinted nanoparticle (MMINP) was developed, capable of forming a photonic crystal (PC) sensor in the presence of a magnetic field for the detection of sulfamethazine (SM2) residues in food. The preparation conditions were meticulously optimized, and the adsorption performance of MMINPs was comprehensively characterized through Adsorption Kinetics Experiments and Equilibrium Binding Experiments. As the concentration of SM2 varied, the structural colors were systematically characterized under a designated magnetic field strength. The experiments revealed that within the range of SM2 concentrations from 1 ng/L to 1 mg/L, there was a shift in the reflected wavelength from 570 nm to 610 nm, and the color changed from green to red-orange. The sensor achieved minimum detection limit of 2.75 μg/L, maximum adsorption capacity of 3.86 mg/g, imprint factor of 1.90, and was reused for at least five adsorption-resolution cycles. The recoveries ranging from 69.97% to 102.68% and RSD <8.73%.The sensor introduces an innovative approach for the rapid visual colorimetric detection of SM2 in complex food matrices. Magnetically responsive photonic crystal Sulfadimethoxine Surface molecular imprinting Sensor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 Figure 23 Figure 24 1. Introduction The improvement of living standards has led to increased attention to food safety from farm to table(Kim et al. 2018 ). Sulfonamides are a class of low-cost synthetic antibiotics with broad-spectrum antimicrobial properties and stable performance(Spielmeyer et al. 2014 ). They are widely used to prevent and treat bacterial infections(Franco et al. 1990 ). Sulfonamides are challenging to dissolve in water and require large and frequent doses, which can result in excessive residues. These compounds can accumulate in the human body through the food chain, posing a risk to health. Allergic reactions, such as skin rashes and drug fever, as well as urinary system diseases, are the main manifestations of sulfonamide accumulation. Animal experimental studies have also shown that some sulfonamides have teratogenic and carcinogenic effects(Chatzimitakos et al. 2020; Fuhrmann et al. 2014 ; Li et al. 2018 ). China and the European Union have set the maximum total residue level of SAs in animal-derived food at 100 µg/kg. Additionally, China has stipulated that the residue level of SM2 as a single drug should not exceed 25 µg/kg(Y. Yang et al. 2022 ). The most common methods for determining SAs in food are high-performance liquid chromatography (HPLC) (Zhao et al. 2014 ), ultra-high liquid chromatography (UHPLC), and liquid chromatography-time-of-flight mass spectrometry (TFMS) (Paoletti et al. 2022 ). Various methods are used for detection, including high-pressure extraction high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) (Yu et al. 2011 ), enzyme-linked immunosorbent assay (ELISA) (Burkin et al. 2018 ; Zhou et al. 2014 ), spectrophotometric methods(Dmitrienko et al. 2015 ), and chemical detection(De La Peña et al. 2007 ). Currently, 4czpre-treatment methods are commonly used in conjunction with ultra-high performance liquid chromatography (UPLC) detection for efficient detection. Although the chromatographic method has a low detection limit and is capable of detecting a wide range of structural analogs, it requires operator training and has a long detection times(Le et al. 2018 ; Wang et al. 2022 ; W. Xu et al. 2010 ; M. Yang et al. 2019 ). Therefore, faster and simpler detection methods are necessary. Molecular imprinting technology (MIT) has rapidly developed since the successful preparation of molecularly imprinted polymers of theophylline in 1993(Ramstroem et al. 1993 ) Molecular imprinting is a technique developed in recent decades for preparing novel polymeric materials with predictable structure, selective recognition, and utility. The theoretical basis of this technique is the specific binding between antigen and antibody in immunology. The process can be divided into three parts. Firstly, the template molecule binds to the functional monomer(Alexander et al. 2006 ; Cai et al. 2010 ; Chen et al. 2011 ; Hunt et al. 2006 ; Vallano et al. 2000). Secondly, under certain conditions such as temperature or light, the initiator triggers a free radical polymerization reaction to produce a highly cross-linked polymer. Finally, the template molecules are eluted in a specific manner to form molecularly imprinted cavities with specificity(Basak et al. 2022 ). Various methods have been developed to synthesize molecularly imprinted polymers to improve their adsorption, stability and functionality. These include native polymerization, precipitation polymerization, suspension polymerization, and reactive/controlled radical polymerization. Molecularly imprinted polymers have been extensively used for identifying hazardous contaminants in food as adsorbent materials and recognition components in sample pretreatment. They have also been used in combination with sensors to form sensing and detection systems(Villa et al. 2021 ). In 1987, E. Yablonovitc(Eli et al. 1987) and S. John(John 1987 ) independently proposed the concept of photonic crystals (PCs) in the United States. PCs are based on the concepts of semiconductor crystals and electronic band gaps. Yablonovitc studied self-emission, while John studied photon localization. Photonic crystals, also known as opal crystals, are periodic dielectric structures with photonic band gap (PBG) properties. They are arranged in a periodic manner to interact with light, displaying different colors on the same length scale as the wavelength of the interacting electromagnetic radiation(J. Wang et al. 2011 ; Yablonovitch 1995 ). Photonic crystals can be classified into opal and anti-opal photonic crystals based on their structural features. Photonic crystals can be classified into one-, two-, and three-dimensional structures based on their repeating periodic structure(Christoph et al. 2014 ). Materials that respond to changes in external magnetic fields are called magnetically responsive photonic crystals (MRPCs) and have the advantage of instantaneous reversible self-assembly(Ge et al. 2008; Y. Hu et al. 2011 ; Luo et al. 2014 ). Magnetic-responsive photonic crystals consist of monodisperse magnetic nanoparticles arranged in a linear one-dimensional structure under the influence of a magnetic field. This arrangement results in structural color changes that are dependent on factors such as magnetic field strength, humidity, spatial potential resistance, and surface charge(Dai et al. 2023 ; Y. Hu et al. 2011 ; Liu et al. 2023 ; H. Wang et al. 2011 ; X. Xu et al. 2002 ). Xu et al. developed moisture-responsive photonic crystals (MRPCs) that exhibit a spectral shift in response to changes in ambient humidity. Specifically, the reflected wavelength changes from 436 nm to 652 nm ,and the sensor color changes from blue-violet to bright orange-yellow when the ambient humidity is changed from 11–93%(J. Xu et al. 2022 ). Numerous studies have reported the use of MRPCs in detection. Xu's group has developed molecularly imprinted magnetic colloidal nanoparticles (MIMCNPs) for detecting melamine (MEL) and bisphenol-A(T. Hu et al. 2022 ; J. Xu et al. 2021 ). Amphiphilic random copolymers were used as emulsifiers and MMINP coatings, while oleic acid-modified magnetite nanoparticles were used as magnetic cores. Template molecules were added and self-assembled in a microemulsion system to simultaneously complete the formation of magnetic nanoparticles and molecular imprinting. The sensor can produce structural color changes as the concentration of the substance to be measured changes. This enables the construction of a simple and rapid colorimetric MIMCNPs sensor for the first time. While existing studies can offer technical support for the preparation of magnetic molecularly imprinted photonic crystal sensors, there are few applications for detecting veterinary drug residues and contaminants in food. Additionally, ensuring strong magnetism, particle size, and effective molecular imprinting of nanoparticles poses significant challenges. In this paper, Fe 3 O 4 nanoparticles with superparamagnetic properties were prepared using the solvothermal method. A layer of SiO 2 was then encapsulated using the sol-gel method. Subsequently, molecularly imprinted layers were synthesized to obtain Fe 3 O 4 @SiO 2 @MIPs. We observed changes in the surface charge of the particles and the dissolution of the molecularly imprinted layers. The concentration of SM2 causes alterations in structural colors under fixed magnetic field strength, enabling visual detection to some extent. To assess the sensor's practicality, water, milk, and chicken samples were examined. Magnetic nanoparticles were easily collected by magnets, simplifying the sample pre-treatment process and providing ideas for rapid on-site screening. 2. Experimental section 2.1 Chemicals and materials SM2, SD, SIZ, EGDMA, and MAA were all purchased from Aladdin Reagent Co., Ltd. (Shanghai, China). AIBN, ethylene glycol, PEG-4000, NH 3 ·H 2 O, tetraethyl orthosilicate (TEOS) and sodium acetate were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Ethanol, methanol, and acetic acid were purchased from Xilong Chemical Co., Ltd. (Guangdong, China). Milk and chicken were purchased from a local supermarket in Jinan, Shandong Province. The water samples were local tap water from Jinan. With the exception of AIBN, all chemicals are of analytical grade and can be used directly without any further purification. 2.2 Instruments Reflectance spectrum measurement was performed on a fiber optic spectrometer (PG 2000, Shanghai Fuxiang Optical Co.). Dual beam ultraviolet–visible spectrophotometry (TU-1901, Beijing General Instrument Co., China), scanning electron microscopy (SEM, S4800, Hitachi, Japan), X-ray diffraction (XRD, Rigaku, Japan), High Performance Liquid Chromatograph (HPLC, SIL-20A, Shimadzu Prominence, Japan), and Vibration Sample Magnetometer (VSM, 8604, Lake Shore Cryotronics Inc, America) were used to characterize the polymers. The Fourier Transform Infrared Spectrometer (NEXUS 670) was obtained from Thermo Nicolet, Inc. Automated Gas Sorption Analyzer (BET, ASAP2460-2MP) was obtained from Georgia, United States. 2.3 Synthesis of Fe 3 O 4 @SiO 2 magnetic nanoparticles Monodisperse spherical magnetic nanoparticles (MNPs) were synthesized by solvothermal method. First, 0.67 g of FeCl 3 ·H 2 O was fully dissolved into 20 mL of ethylene glycol, subsequently, 0.5 g of PEG-4000 and 2.8 g NaAc were added sequentially, and the dissolution was stirred while sonication. Finally, the solution was transferred to a 50 mL PTFE-lined autoclave and reacted at 200℃ for 10 hours. The autoclave was allowed to cool down naturally to room temperature.The black magnetic nanoparticles were then subjected to alternately washing with anhydrous ethanol and deionized (DI) water at least three times, followed by dying in vacuum at 50 ℃ overnight. 0.06g of Fe 3 O 4 was taken separately and added to a round bottom flask containing 2 mL of NH 3 ·H 2 O and 13 mL of water. The mixture was sonicated for 5 minutes and then 67 mL of anhydrous ethanol was added and sonicated for another 5 minutes. 218 µL of TEOS was added drop by drop and the mixture was mechanically stirred for 1 hour to obtain Fe 3 O 4 @SiO 2 . Fe 3 O 4 @SiO 2 was recovered using a magnet, washed with anhydrous ethanol three times, and dried in a vacuum at 50℃ overnight. 2.4 Preparation of the SM2-MMINPs The preparation of SM2-MMINPs is shown in Fig. 1 . Dissolve 0.0278g of SM2 into 20 mL of methanol, add 0.034 mL of MAA, sonicate for 20 minutes and place at 4℃ overnight. Next, add 0.495 mL EGDMA to the above solution, sonicated for 5 minutes. Then, add 10 mg AIBN and 0.05 g Fe 3 O 4 @SiO 2 and sonicate the solution for 20 minutes and remove the O 2 by bubbling with N 2 for 10 minutes. Seal the system with a sealing membrane and mechanically mix at 60°C for 6 hours. The polymer was recovered with a rubidium magnet and washed three times with methanol. Finally, SM2 was eluted from the polymer with acetic acid and 90% methanol (1:9, v/v) until it was no longer detectable by UV-Vis spectroscopy. Excess eluent was washed off with methanol, recovered with magnets and dried in a vacuum at 50℃.The preparation process of magnetic non-molecularly imprinted nanoparticles (MNINPs) was identical to that of MMINPs, except that SM2 was not added. 2.5 Reflection spectrum measurement The dispersion of 1% (w/w) MMINPs or MNINPs was prepared using methanol and sonicated for 5 minutes. The probe of the fibre-optic spectrometer was fixed at the same height and perpendicular to the magnet plane. The changes in the reflectance spectra of the sensors induced by the SM2 methanol solution at concentrations ranging from 10 − 1 to 10 3 µg/L were detected. All experiments were performed under the same conditions to avoid interference. 2.6 Sample preparation 2.6.1 Water samples Water samples are simply filtered through a 0.22 µm membrane to remove solid impurities. 2.6.2 Milk samples The milk sample (5 mL) was centrifuged at 10,000 rpm for 10 minutes to remove the lipids. Next, 5 mL of methanol was added to precipitate the excess proteins. The supernatant was then centrifuged again, and the final supernatant was used for detection using a fibre optic spectrometer. 2.6.3 Chicken samples The chicken sample (5.0 g) was minced and mixed with hydroxylamine hydrochloride (1.5 mL) and ammonium acetate (3.5 ml, 50 mmol/L, pH 4.5). The mixture was vortexed vigorously for 5 minutes. Then, acetonitrile (2 mL) was added and the mixture was sonicated for 5 minutes. The supernatant was collected by centrifugation at 10,000 rpm for 10 minutes and dried with N 2 at room temperature. It was then reconstituted with 5 mL of methanol. 2.7 Detection conditions for HPLC High performance liquid chromatography (HPLC) with a UV detector was used for the determination of SM2, SD and SIZ adsorption in this experiment. The column temperature was 25 ℃, the flow rate was 0.8 mL/min, the wavelength was 270 nm, the mobile phase was water:methanol:acetic acid = 70:30:0.5 (v/v/v) and the injection volume was 20 µL. 2.8 Adsorption kinetics experiment In order to assess the adsorption performance of the sensor, kinetic experiments were conducted. The amount of SM2 adsorbed (Q, mg/g) was calculated using the following equation: \(\:Q=\left({C}_{0}-{C}_{e}\right)V/m\) \(\:\text{(2.1)}\) where Q is the adsorbed amount (mg/g), C 0 is the initial concentration of SM2 solution (mg/L), C e is the concentration of SM2 solution after adsorption (mg/L), V is the volume of SM2 standard methanol solution added (mL), and m is the mass of MMINPs or MNINPs (mg). The kinetic substrate and rate control steps of SM2 adsorption by MMINPs were investigated by fitting the obtained data with quasi-primary and quasi-secondary adsorption kinetic models with the equations shown below: Quasi-primary adsorption kinetic equation: \(\:{Q}_{t}={Q}_{e}\left(1-{e}^{-K1t}\right)\) \(\:\text{(2.2)}\) Quasi-secondary adsorption kinetic equation: \(\:{Q}_{t}={K}_{2}{Q}_{e}^{2}t/(1+{K}_{2}{Q}_{e}t)\) \(\:\text{(2.3)}\) where Q e (mg/g) and Q t (mg/g) are the amount of SM2 adsorbed by MMINPs at adsorption equilibrium and at time t, respectively, and K 1 (min − 1 ) and K 2 (g/(mg·min)) are quasi-primary and quasi-secondary adsorption rate constants. Quasi-primary adsorption kinetics are generally used to characterize the kinetics of the initial phase of adsorption. The quasi-secondary adsorption kinetics can characterize the whole adsorption process, the more imprinted holes on the molecularly imprinted material, the stronger the adsorption capacity, and the quasi-secondary adsorption kinetics generally involves a variety of electron binding energy sites. In addition, the Elovich model is a commonly utilized approach for describing the adsorption kinetic process, with the following equations: \(\:{Q}_{t}=\text{ln}\left(\alpha\:\beta\:\right)/\beta\:+\text{ln}\left(t\right)/\beta\:\) \(\:\text{(2.4)}\) The intra-particle diffusion process is also frequently employed to describe the adsorption kinetic process and is represented by the following linear equation: \(\:{Q}_{t}={K}_{int}{t}^{\frac{1}{2}}+C\) \(\:\text{(2.5)}\) In this context, β denotes the activation energy and degree of manifestation coverage of adsorption (g/mg), α denotes the initial adsorption rate constant (mg/(g·min)), k int is the internal diffusion coefficient of the particles (mg/(g·min 1/2 )), and C is a constant related to the thickness of the boundary layer (mg/g). The Elovich model, which assumes that the adsorption process occurs via chemisorption in an ideal environment, posits that the adsorption process is related to the diffusive movement of the adsorbed substance, the specific adsorption sites of the adsorbent, and the diffusion of the adsorbed substance into the interior of the adsorbent. When the adsorption process involves both surface diffusion and internal diffusion, the adsorption rate can be fitted with an intraparticle diffusion model. When the detected substance, such as SM2, penetrates the interior of the molecularly imprinted polymer, the data can be fitted by the intra-particle diffusion model. In general, the fitting results can be classified into two categories. One such result is a straight line fit of Q t versus t 1/2 , accompanied by a value of parameter C close to 0, indicating that the adsorption process is predominantly internal diffusion. In the alternative scenario, the fitting result is a curve, which indicates that the adsorption process involves multiple adsorption types. If the fitted curve passes through the origin, it can be concluded that the diffusion of the system is controlled by a single rate. Conversely, if the curve does not pass through the origin, it can be inferred that the internal diffusion process is not the sole step controlling the adsorption of SM2 by the sensor. 2.9 Equilibrium binding experiments Five milligrams of MMIMPs or MNINPs were added to two milliliters of a methanol solution of SM2 at varying concentrations (2, 4, 6, 8, 10, 12, and 14 milligrams per liter) and incubated on a shaker while shaking at 100 rotations per minute for one hundred minutes. The magnetic nanoparticles were recovered by magnet adsorption, and the residual solution was aspirated with a syringe and filtered through a 0.45 µm organic membrane. The concentration of SM2 was then detected by HPLC. The adsorption amount of SM2 was calculated using Eq. (2.1). To further elucidate the adsorption mechanism of the SM2 sensor prepared in this experiment, the equilibrium binding experimental data were fitted and analyzed using Langmuir and Freundlich isotherm equations, as illustrated in Equations (2.6) and (2.7), respectively. \(\:{q}_{e}={K}_{L}{q}_{m}{C}_{e}/(1+{K}_{L}{C}_{e})\) \(\:\text{(2.6)}\) \(\:{q}_{e}={K}_{F}{C}_{e}^{\frac{1}{n}}\) \(\:\text{(2.7)}\) The quantity of SM2 adsorbed at the adsorption equilibrium of MMINPs (mg/g) is denoted by q e , while the maximum amount of SM2 adsorbed after the adsorption equilibrium of MMINPs (mg/g) is represented by q m . Finally, C e is the equilibrium concentration of SM2 solution after adsorption (mg/L). Among these parameters, the numerical magnitude of Langmuir's constant, K L (L/mg), is related to the nature of the adsorbent, the nature of the adsorbent substance, and the temperature. It represents the strength of the adsorption capacity. The Freundlich exponent, k f (µg/mg (L/mg) 1/n ), represents the adsorption amount at the concentration in units of C. The value of n represents the surface specificity and strength of adsorption of the adsorbent material. It is a characteristic adsorption parameter, with 1/n typically ranging from 0 to 1. A value of 1/n closer to 0 indicates a greater likelihood of adsorption. The Langmuir equation is frequently employed to analyze the adsorption process of adsorbent materials that are solely present on the surface, and the energy of the active site is homogeneous. The Freundlich model is typically utilized to fit the reversible adsorption process of a non-homogeneous phase system, which has multilayered adsorption on a non-homogeneous surface to the inner layer. 2.10 Scatchard equation analysis The Scatchard equation, shown in (2.8), is a commonly used analytical tool for the analysis of the number and nature of specific binding sites. Q represents the equilibrium adsorption of SM2 by MMINPs (µg/mg), C e is the concentration of SM2 supernatant at equilibrium (mg/L), K d (mg/L) is the dissociation constant of the binding site, and Q max (mg/g) is the apparent maximum binding. The values of K d and Q max can be calculated from the slopes and intercepts of the linear plots of Q/C e versus Q. \(\:Q/{C}_{e}={(Q}_{max}-Q)/{K}_{d}\) \(\:\text{(2.8)}\) When the Scatchard equation is fitted to the data with a single straight line, it can be concluded that the prepared material contains only one type of binding site. However, when the fit shows two straight lines, it indicates that the material contains both types of binding sites. The maximum adsorption amount, Q max , and the dissociation constant, K d , were calculated in order to identify the binding site. The K d value reflects the affinity of the adsorbent for the analyte. A smaller K d value indicates a higher affinity. 3. Results and discussion 3.1 SEM characterization of MMINPs In this experiment, the qualitative and quantitative detection of SM2 is achieved through the use of a photonic crystal material as an optical signal conversion device. The rapid detection of SM2 concentration is possible according to the offset value of the Bragg diffraction peak. The synthesis of nanoparticle microspheres with uniform particle size is a necessary prerequisite for the preparation of high-performance photonic crystal sensors. The morphology of MMINPs was characterized using SEM. As illustrated in Fig. 2 (A), the dried MMINPs exhibited an overall spherical morphology with a more uniform particle size. As illustrated in Fig. 2 (B), the particle size of MMINPs is approximately 200 nm. The MMINPs prepared by this method can be assembled rapidly under the influence of magnets, thus effectively circumventing the potential interference of complex food matrices. 3.2 Optimization of Fe 3 O 4 @SiO 2 additions In this experiment, Fe 3 O 4 @SiO 2 is employed as the magnetic core, and its addition determines the quality of the prepared MMINPs. The effect of Fe 3 O 4 @SiO 2 addition on the adsorption performance of MMINPs was evaluated by calculating the adsorption amount and imprinting factor. As illustrated in Fig. 3 , As the mass of Fe 3 O 4 @SiO 2 increases, the imprinting factor gradually rises, reaching a maximum value at an additional amount of 50 mg, after which it gradually decreases. In the event that a certain polymerization time is maintained, insufficient quantities of Fe 3 O 4 @SiO 2 will fail to fully utilize the template molecules, functional monomers, and cross-linkers present in the prepolymerization solution, thereby resulting in waste. Conversely, excess quantities of Fe 3 O 4 @SiO 2 will result in an imprinted layer that is too thin, thereby reducing the adsorption performance. Consequently, the quantity of Fe 3 O 4 @SiO 2 employed in the experiment was 50 mg. 3.3 Optimization of aggregation time The polymerization time can be adjusted in order to regulate the thickness of the imprinted layer. Figure 4 illustrates the change in adsorption amount with polymerization time. As the polymerization time increases, the imprinting factor gradually increases until reaching a maximum value of 1.92 at 6 hours. Both MMINPs and MNINPs formed by this polymerization time exhibit a light-green photonic crystal structure color under the action of a magnetic field, which aligns with the fundamental conditions for sensor preparation. Therefore, the 6-hour polymerization time is selected as the optimal time for the reaction. 3.4 Optimization of adsorption solvents In order to investigate the optimal adsorption conditions of MMINPs, they were dispersed in SM2 solutions of different solvents for adsorption. Five milligrams of MMINPs were added to SM2 methanol, ethanol, water, and acetonitrile solutions at a concentration of 10 mg/L, respectively, and incubated with shaking at 100 rpm for 100 minutes. After the recovery of MMINPs by magnet adsorption, the concentration of SM2 in the supernatant was quantified by HPLC, and the adsorption amount was calculated. The results are presented in Fig. 5 . The adsorption rate of SM2 by MMINPs was higher when methanol was used as the solvent, thus indicating that methanol was the optimal solvent for the adsorption of SM2. 3.5 Optimization of elution conditions In order to achieve complete elution of SM2 from MMINPs, the eluent ratios were optimized. The eluent was configured with acetic acid:methanol volume ratios of 0:10, 1:9, 2:8, 4:6, and 1:9 (90%), and the concentration of SM2 in the eluent was detected using HPLC to calculate the recovery. As illustrated in Fig. 6 , the acetic acid: methanol ratio of 1:9 (v/v) yielded the highest recovery in the initial four groups. However, this increased volume percentage of acetic acid resulted in a subsequent decline in recovery. This may be attributed to the gradual protonation of the amino group of SM2 due to the addition of excessive acetic acid, which results in the strong polar ammonia atoms on the amino group combining with the hydrogen atoms in the system, thereby reducing the number of recognition groups, which in turn makes it challenging for SM2 to elute. As illustrated in the accompanying figure, the elution effect was enhanced when 90% methanol was used to prepare distilled water for the configuration of the eluent. This was due to the addition of water, which regulated the polarity of the eluent, thereby creating a more favorable environment for the template molecules to be eluted. Consequently, the eluent was configured using acetic acid:methanol in a 1:9 ratio (90% v/v) to elute MMINPs. In order to confirm the degree of elution, a UV spectrophotometer was employed to detect the presence of the UV absorption peak of SM2 in the eluate following each elution. As illustrated in Fig. 7 , the UV absorption peak of SM2 was observed at 268 nm. With the increase in the number of elutions, the absorbance exhibited a gradual decline, and the presence of SM2 could no longer be detected after approximately the fifth elution, indicating that the elution process was complete. 3.6 Optimization of dispersant solvent pH The MMINPs were incubated in 1 mg/L of SM2 methanol solution for 100 minutes, after which they were dispersed in methanol configured as a 1% (w/w) dispersion system adjusted to different pH values (5, 6, 7, 8, and 9) with 1 mol/L of HCl or NaOH solution. Ultrasonication was then performed for 5 minutes. Figure 8 illustrates the changes in reflected wavelengths at varying pH levels. The reflection wavelength shift is more pronounced when pH = 7, while the pH of methanol is typically in the range of 6.8 to 7. Therefore, methanol was employed as the dispersing solution for MMIMPs in the experiment. 3.7 Characterization of Materials by FTIR Spectrometer The infrared spectrum of the sample is presented in Fig. 9 . The Fe-O stretching vibration peak, which is the characteristic peak of Fe 3 O 4 , is observed at 565 cm − 1 .The figure shows that the Si-O bending vibration peak is at 465 cm − 1 and the Si-O-Si symmetric telescopic vibration peak is at 795 cm − 1 , which are characteristic peaks of silica. Additionally, the Si-O-Si antisymmetric telescopic vibration peak is at 1066 cm − 1 and the Si-OH bending vibration absorption peak is at 950 cm − 1 . These results confirm the successful synthesis of Fe 3 O 4 @ SiO 2 . The 2981 cm − 1 peak corresponds to the -OH peak in MAA, while the 1725 cm − 1 peak corresponds to the carbonyl C = O absorption peak in MAA and EGDMA. Additionally, the 1253 cm − 1 peak corresponds to the C-O-C absorption peak in EGDMA. The 880 cm − 1 peak corresponds to the C-H wobble vibration peak on the pyrimidine ring, while the 1459 cm − 1 peak corresponds to the C-H stretching vibration peak in SM2. Finally, the 1047 cm − 1 peak corresponds to the peak of R-S(O)-O-R'. The peak at 3350 cm − 1 corresponds to the -NH group of SM2 in the synthesized molecularly imprinted polymer layer. 3.8 Magnetic evaluation of magnetic nanoparticle materials Figure 10 shows the VSM characterisation of Fe 3 O 4 , Fe 3 O 4 @SiO 2 , MMINPs and MNINPs. The hysteresis loop is depicted in Fig. 10 . The coercivity of the magnetic intensity curves of each sample is equal to zero, indicating the absence of hysteresis and remanent magnetization. The saturation magnetic strengths of Fe₃O₄, Fe₃O₄@SiO₂, MMINPs, and MNINPs were calculated to be 91.2 emu/g, 63.3 emu/g, 47.5 emu/g, and 43.9 emu/g, respectively. It was observed that the magnetic properties decreased gradually with the modification of the shell layer. 3.9 Nitrogen adsorption and desorption experiment The specific surface area and pore size of MMINPs were analyzed by a fully automated gas analyzer. As illustrated in Fig. 11 , the hysteresis loop type was identified as H 3 , and the MMINPs exhibited a weak adsorption interaction with SM2, indicating that the sample possessed a mesoporous structure. According to the Brunauer-Emmett-Teller theory, the specific surface area of MMINPs was calculated to be 28.54 m 2 /g, and the average pore size was 9.25 nm. The results demonstrated that MMINPs are a type of microporous material with a considerable specific surface area, theoretically capable of adsorbing template molecules to bind to specific sites and induce the diffusion of SM2 to the interior of the molecularly imprinted polymer layer. 3.10 XRD characterization of materials In order to verify the physical phase composition of the synthesized materials, the crystalline structures of Fe 3 O 4 , Fe 3 O 4 @SiO 2 , and MMINPs were characterized by X-ray diffraction. As illustrated in Fig. 12 , the diffraction peaks observed in the XRD spectrum correspond to the (220), (311), (400), (422), (511) and (440) crystal planes, respectively. A comparison and analysis of the diffraction peaks with the standard diffraction card JCODS NO:19–0629 of anti-spinel type Fe 3 O 4 revealed a high degree of agreement. The shape of the diffraction peaks of Fe 3 O 4 is narrow and sharp, indicating that the crystal grains are complete and the degree of crystallization is good. Figure 12 illustrates that the crystalline structures of Fe 3 O 4 , Fe 3 O 4 @ SiO 2 , and MMINPs are all cubic antispinel. The thickening of the shell layer results in a reduction in the peak intensity of Fe 3 O 4 @ SiO 2 , MNINPs, and MMINPs. Nevertheless, the crystal structure of their Fe 3 O 4 cores remains unaltered. 3.11 Adsorption kinetics experiment To assess the adsorption performance of the MMINPs sensor, kinetic experiments were conducted. 5 mg of MMINPs or MNINPs were added to 15 mg/L of SM2 solution and placed on a shaker at 100 rpm for full incubation, and the concentration of SM2 in the supernatant was measured every 20 minutes to observe the time required to reach adsorption equilibrium. As shown in Fig. 13 , with the increase of time at 100 minutes, the adsorption amount of both MMINPs and MNINPs reached the maximum value and leveled off, which means that both of them reached the adsorption equilibrium. In order to investigate the mass transfer mechanism and rate-limiting step of MMINPs and MNINPs, quasi-primary and quasi-secondary adsorption kinetic models were employed to fit the data. The magnitude of the R 2 values allowed for the determination of the most suitable model for the materials. The quasi-primary adsorption kinetic model describes the interaction force between the SM2 template molecules and the molecularly imprinted layer, as well as the adsorption rate with time. The arrival of the solute from the solution to the sensor surface is controlled by a diffusion step. Furthermore, there is only one binding site on the surface of the adsorbent, which is often suitable for describing the kinetic process at the initial stage of adsorption. The quasi-secondary model postulates that the adsorption rate is under the control of chemisorption and not the substance transport step. Furthermore, it assumes that SM2 first forms a monolayer on the sensor surface and then diffuses into the solid interior. As illustrated in Fig. 14 and Table 1 , the R 2 of MMINPs is greater than 0.996, which is more consistent with the quasi-secondary kinetic equation. Meanwhile, the discrepancy between the experimental maximum adsorption (3.84 mg/g) and the calculated theoretical value (3.84 mg/g) is not substantial, indicating that the prepared materials align more closely with the quasi-secondary adsorption kinetic model and that chemical bond adsorption is the primary type of adsorption. Table 1 Parameters for fitting kinetic equations for pseudo first order and pseudo second order adsorption of MMINPs. Pseudo-first-order Pseudo-second-order Samples Q e (exp) Q e (cal) K 1 R 2 Q e (cal) K 2 R 2 (mg/g) (mg/g) (min − 1 ) (mg/g) (g/(mg·min)) MMINPs 3.84 3.80 0.05 0.983 3.84 0.02 0.996 Fig.s 15 and 16 illustrate the intra-particle diffusion model and Elovich model fitting of MMINPs, respectively. Table 2 presents the fitted parameters of the intra-particle diffusion model and Elovich equation. As illustrated in Fig. 15 , both sets of fits to the intra-particle diffusion model do not pass through the coordinate origin, indicating that the internal diffusion process is not the sole step in controlling the adsorption of SM2 by the sensor. The adsorption of SM2 by the MMINPs can be roughly divided into two stages: a fast adsorption stage and a slow adsorption stage. At the initial stage of adsorption, a substantial number of SM2 molecularly imprinted cavities are present on the surface of MMINPs, exhibiting a high degree of site-specific binding affinity. In the late stage of adsorption, with the decrease of sites, SM2 was affected by mass transfer to diffuse inside the sensor, K First > K Second , indicating that the adsorption sites were gradually occupied by SM2, and the adsorption rate was slowed down and finally reached equilibrium. Table 2 Adsorption kinetic constants for Elovich and intraparticle diffusion equation. Elovich equation Intra-particle diffusion equation Samples α β R 2 K 1 K 2 C R 2 (mg/(g·min)) (g/mg) (mg/(g·min 1/2 )) (mg/g) First Second MMINPs 21.45 1.30 0.980 0.23 0.17 1.80 0.990 0.650 3.12 Equilibrium binding experiments As illustrated in Fig. 17 , the isotherms of MMINPs and MNINPs binding SM2 were presented, and the adsorption equilibrium of MMINPs and MNINPs was gradually achieved with the increase of the initial concentration of SM2. Furthermore, the adsorption of SM2 by MMINPs was found to be significantly higher than that of MNINPs, indicating that the prepared materials have excellent adsorption properties. The maximum adsorption amount of MMINPs was determined to be 3.86 mg/g with an imprint factor of 1.90. Figure 18 illustrates the equilibrium binding experimental fits of MMINPs and MNINPs to SM2. The experimental data were fitted by Langmuir and Freundlich isothermal models, and the fitting parameters are shown in Table 3 . All the R 2 values in the MMINPs group were larger than those in the MNINPs group. The R 2 value (0.992) of the data fitted by the Freundlich equation was slightly larger, which suggests that the adsorption of SM2 by the MMINPs was both monolayers and multilayers and preferred multilayer adsorption. Table 3 Fitting parameters for the Langmuir and Freundlich equations. Langmuir Freundlich Samples T(℃) Q m (mg/g) K L (L/mg) R 2 K F (mg/g(L/mg) 1/n ) n R 2 MMINPs 25 4.03 0.08 0.970 0.78 1.70 0.992 MNINPs 25 3.41 0.13 0.969 0.56 2.08 0.907 3.13 Scatchard equation analysis of MMINPs Scatchard plot analysis was used to assess the heterogeneity of the binding sites of the prepared materials. As shown in Fig. 19 , the Scatchard plot of MMINPs consists of two linear sections with different slopes, which indicates the presence of heterogeneous binding sites. The linear regression equation for the left part of the curve was Q/C e = -6.315x + 24.5275 with R 2 = 0.981. Table 4 shows the fitted parameters of the Scatchard equation. The K d and Q max of the dried MMINPs were calculated to be 0.16 mg/L and 3.79 mg/g. The linear regression equation for the right-hand portion of the curve was Q/C e = -2.5867x + 10.5544, R 2 = 0.997. The K d and Qmax of the dried polymers were calculated to be 0.39 mg/L and 4.07 mg/g. As can be seen from the figure, the fitting result is two intersecting straight lines, indicating the existence of two different types of binding sites in the MMIMPs; the left fitting equation corresponds to the adsorption process in the low concentration range, and the right equation corresponds to the fitting of the adsorption process in the high concentration. Table 4 shows that K d high affinity capacity < K d low affinity capacity , the binding site expressed by the left side equation has higher affinity, and its adsorption capacity is approximately 2.44 times higher than that of the low-affinity binding site on the right side, and the maximum adsorption capacity of the high affinity binding site is 4.07 mg/g. Therefore, the specific binding of SM2 on the surface of MMIMPs is the main adsorption process. Table 4 Scatchard equation fitting parameters for MMINPs. Parameters of the Scatchard fitting curve High affinity Low affinity K d (mg/L) 0.16 0.39 Q max (mg/g) 3.79 4.07 R 2 0.981 0.997 3.14 Selectivity of MMINPs In order to investigate the specificity of the prepared MMIMPs and MNINPs for SM2 adsorption, a series of mixed solutions were prepared to contain varying concentrations of SM2, SD and SIZ (50, 100, 150, 200 and 250 mg/L). These solutions were used for adsorption experiments. Twenty milligrams of MMIMPs and MNINPs were added to two milliliters of the aforementioned solutions and incubated for a period of time. The MMIMPs and MNINPs were then recovered with magnets, and the adsorption of the materials to SM2, SD, and SIZ was calculated by HPLC. Figure 20 illustrates that the adsorption amount of MMIMPs on SM2 is considerably greater than that of SD and SIZ. Additionally, the adsorption amount of MMIMPs on SM2 is considerably greater than that of MNINPs. This indicates that the material exhibits excellent selectivity for SM2. As illustrated in Table 5 , the adsorption amount of MMINPs on SM2 was 1.66 times that of SD and 1.93 times that of SIZ. There was no significant difference in the adsorption effect of MNINPs on SM2 and its structural analogues, indicating that they do not possess the ability to selectively recognize template molecules. This further demonstrates the role of MMINPs in the selective recognition of SM2. Table 5 Selective Parameters of Sensors for SM2. Analysts Adsorption capacity (Q, mg/g) K d K K' IF MIP NIP MIP NIP MIP NIP SD 2.32 1.99 0.03 0.03 2.10 1.92 1.09 1.17 SM2 3.85 1.99 0.06 0.05 — — — 1.93 SIZ 2.00 1.90 0.03 0.02 2.52 2.09 1.21 1.05 Note: K d is the partition coefficient, K d = Q/C f , C f is the final analyte concentration; K is the selectivity coefficient, K = K d (SM2)/K d (analyte); K' is the relative selectivity coefficient, K' = K MMINPs /K MNINPs ; IF = Q MMINPs / Q MNINPs 3.15 Characterization of color change properties of MMINPs Figure 21 presents a set of fiber optic spectra obtained from the experiment. MMINPs were employed to adsorb a series of SM2 solutions at a range of concentration gradients, with a fixed magnetic field applied via a magnet. As the concentration of SM2 solutions increased, the reflection peaks gradually redshifted, resulting in a structural color change from green to red. The maximum absorption peak before the adsorption of the MMINPs was located at 470 nm, and its maximum reflection peak shifted by 40 nm as the concentration increased. The differences in diffraction wavelength shifts brought about by the adsorption of SM2 solutions with concentrations of 10 2 ng/L to 1 mg/L by MMINPs and MNINPs are shown in Fig. 22 . The wavelength shifts of MMINPs and MNINPs were 40 nm and 16 nm, respectively. The wavelength shifts of MMIMPs were significantly larger than those of MNINPs, which confirms that the MMIMPs can selectively adsorb SM2 and that the synthesis of MMIMPs was successful. Additionally, the results indicate that MNINPs exhibit some degree of physical adsorption on SM2. 3.16 Establishment of standard curves Five sets of SM2 standards with exponentially increasing concentrations were analysed using MMINPs. Figure 23 shows a clear linear relationship. In the equation Δλ = 8.4272 lg (C) + 14.6509, R 2 = 0.995 and C is the concentration of the SM2 solution. The linear range was from 10 − 1 µg/L to 1 mg/L with a detection limit of 2.75 µg/L (3σ/k, n = 9). 3.17 Reusability of sensors The MMINPs were dispersed in SM2 solution at a concentration of 1 mg/L for the purpose of conducting reusability experiments. Once equilibrium was reached, a fiber-optic spectrometer was employed to detect any changes in diffraction wavelength. Subsequently, the SM2 molecules were thoroughly eluted with an elution solution, and the spectral detection was repeated. As illustrated in Fig. 24 , repeated cycles of adsorption and elution result in minor fluctuations in the maximum and minimum wavelengths detected by the sensor. However, after five adsorption-elution cycles, there was no discernible change in the wavelength range covered by the spectral detection of the MMIMPs, indicating that the MMINPs are highly reusable and can be utilized continuously for at least five cycles with an RSD of < 11% change in sensor detection range. 3.18 Application to real samples To assess the usefulness of MMINPs, they were utilized in experiments to test laboratory tap water, milk, and chicken samples procured from supermarkets in the vicinity of the educational institution. As illustrated in Table 6 , recovery experiments were conducted by analyzing SM2-spiked samples at four concentrations (5, 10, 15, and 20 µg/L). The recoveries of the sensor for water samples exhibited a range of 86.60–102.68%, with an RSD of < 8.30%. For milk samples, the recoveries were found to be in the range of 72.68–80.73%, with an RSD of < 6.34%. For chicken samples, the recoveries were found to be in the range of 69.97–77.00%, with an RSD of < 8.73%. The development of this sensor offers insights into the potential for rapid on-site optical detection of veterinary drug residues. Table 6 Testing of actual samples. Samples 5 µg/L 10 µg/L 15 µg/L 20 µg/L Recovery (%) RSD(%) Recovery (%) RSD(%) Recovery (%) RSD(%) Recovery (%) RSD(%) Water 98.00 8.30 86.60 4.61 102.66 3.06 102.68 3.44 Milk 79.12 5.35 72.68 6.22 74.10 6.34 80.73 5.31 Chicken 76.63 1.20 71.45 8.73 69.97 5.94 77.00 2.65 3.19 Comparison with other analytical methods As illustrated in Table 7 , while the present assay does not exhibit superior sensitivity compared to other rapid detection methods, its linear range is more expansive and more widely applicable. Concurrently, the visualization detection and preparation of MMINPs offer a novel approach for the expeditious on-site screening of SM2. Table 7 Comparison between SM2 molecularly imprinted photonic crystal sensors and other SM2 detection methods. Methods of analysis Samples Linear range (ng/mL) LOD (ng/mL) Recovery rate(%) Reference Fluorescent aptamer sensors Water, soil 1.25ཞ40 0.82 94.40 ~ 108.80 [17] Enzyme-linked aptamer colourimetric sensors chicken 10 − 2 ཞ10 2 0.05 80.50 ~ 92.30 [15] Enzyme-linked immunosorbent sensor for metal-organic skeletons (MOFs) Milk 10 2 ཞ5×10 3 0.05 82.30 ~ 90.12 [16] Electrochemical Immunosensors Water 0.45ཞ43.19 0.07 79.02 ~ 118.39 [18] Magnetic molecularly imprinted photonic crystal sensors Water,Milk,chicken 10 − 1 ཞ10 3 2.75 72.68 ~ 102.68 This work 4. Conclusions In this experiment, MMINPs were synthesised for the detection of SM2.The sensor is superparamagnetic and can be enriched in samples and then recovered by magnets and detected by a fibre-optic spectrometer, which reduces the detection time. The logarithm of the SM2 concentration showed a good linear relationship with the wavelength change in the range of 10 − 1 µg/L ~ 1 mg/L. The LOD value was 2.75 µg/L (3σ/k, n = 9). The recoveries in water, milk and chicken samples ranged from 69.97–102.68% with RSD < 8.73%. The sensor can be reused at least five times and can achieve a certain level of visual detection, providing ideas for rapid on-site screening of veterinary drugs in complex food matrices Declarations Acknowledgment This work was supported by the Key R&D Plan of Shandong Province (Major Science and Technology Innovation Project), China (grant number 2023CXGC010712), and the Shandong Provincial Natural Science Foundation, China (grant number ZR2021MC187). Author contributions Investigation: Xin Wang; Formal analysis and Methodology: Xin Wang and Xiaolei Zhao; Resources: Xin Wang, Huihui Hao; Writing-original draft preparation: Yitong Yin; Writing-review and editing: Jinxing He; Funding acquisition: Jinxing He; Project administration: Jinxing He; Supervision: Jinxing He. Conflicts of Interest Disclosure The authors declare no conflicts of interest. Compliance with ethics requirements This article does not contain any studies with human participants or animals performed by any of the authors. References Alexander C, Andersson HS, Andersson LI, Ansell RJ, Kirsch N, Nicholls IA, O'Mahony J, Whitcombe MJ. (2006). Molecular imprinting science and technology: a survey of the literature for the years up to and including 2003. Journal of Molecular Recognition: An Interdisciplinary Journal. 19(2): 106-180. https://doi.org/10.1002/jmr.2347. Basak S, Venkatram R, Singhal RS. (2022). Recent advances in the application of molecularly imprinted polymers (MIPs) in food analysis. Food Control. 139: 109074. https://doi.org/10.1016/j.foodcont.2022.109074. Burkin MA, Lapa GB, Galvidis IA, Burkin Konstantin M, Zubkov AV, Eremin SA. (2018). Three steps improving the sensitivity of sulfonamide immunodetection in milk. Analytical Methods. 10(48): 5773-5782. https://doi.org/10.1039/C8AY01904E. Cai D, Ren L, Zhao H, Xu C, Zhang L, Yu Y, Wang H, Lan Y, Roberts MF, Chuang JH. (2010). A molecular-imprint nanosensor for ultrasensitive detection of proteins. Nature nanotechnology. 5(8): 597-601. https://doi.org/10.1038/nnano.2010.114. Chatzimitakos T, Stalikas C. (2020). Zinc ferrite as a magnetic sorbent for the dispersive micro solid-phase extraction of sulfonamides and their determination by HPLC. Microchemical Journal. 155: 104670. https://doi.org/10.1016/j.microc.2020.104670. Chen L, Xu S, Li J. (2011). Recent advances in molecular imprinting technology: current status, challenges and highlighted applications. Chemical Society Reviews. 40(5): 2922-2942. https://doi.org/10.1039/C0CS00084A. Christoph, Fenzl, Thomas, Hirsch, Otto, S., Wolfbeis. (2014). Photonic Crystals for Chemical Sensing and Biosensing. Angewandte Chemie International Edition. 53(13): 3318-3335. https://doi.org/10.1002/anie.201307828. Dai Z, Gu Z, Yang Y, Yao L, Li M, Pu Y, Ying Y, Hong X. (2023). A novel sulfhydryl-modified superparamagnetic photonic crystal sensing material for the simultaneous heavy metal ions detection and adsorption treatment. Materials Today Chemistry. 34: 101824. https://doi.org/10.1016/j.mtchem.2023.101824. de la Peña AM, Diez NM, García MM, Gil DB, Cañada-Cañada F. (2007). A chemometric sensor for determining sulphaguanidine residues in honey samples. Talanta. 73(2): 304-313. https://doi.org/10.1016/j.talanta.2007.03.047. Dmitrienko SG, Kochuk EV, Tolmacheva VV, Apyari VV, Zolotov YA. (2015). Determination of the total content of some sulfonamides in milk using solid-phase extraction coupled with off-line derivatization and spectrophotometric detection. Food Chemistry. 188: 51-56. https://doi.org/10.1016/j.foodchem.2015.04.123. Eli, Yablonovitch. (1987). Inhibited Spontaneous Emission in Solid-State Physics and Electronics. Physical Review Letters. https://doi.org/10.1103/PhysRevLett.58.2059. Franco DA, Webb J, Taylor CE. (1990). Antibiotic and sulfonamide residues in meat: Implications for human health. Journal of food protection. 53(2): 178-185. https://doi.org/10.4315/0362-028X-53.2.178. Fuhrmann A, Gans O, Weiss S, Haberhauer G, Gerzabek MH. (2014). Determination of Bentazone, Chloridazon and Terbuthylazine and Some of Their Metabolites in Complex Environmental Matrices by Liquid Chromatography–Electrospray Ionization–Tandem Mass Spectrometry Using a Modified QuEChERS Method: an Optimization and Validation Study. Water, Air, & Soil Pollution. 225(5): 1-15. https://doi.org/10.1007/s11270-014-1944-7. Ge J, Yin Y. (2008). Magnetically responsive colloidal photonic crystals. Journal of Materials Chemistry. 18(42): 5041-5045. https://doi.org/10.1039/B809958H. Hu T, Xu J, Shang M, Zhao Q, Cao Y. (2022). Photonic crystal sensor for melamine based on magnetic molecularly imprinted nanoparticles self-assembled with an amphiphilic random copolymer. Microchimica Acta. 189(6): 215. https://doi.org/10.1007/s00604-022-05300-x. Hu Y, He L, Yin Y. (2011). Magnetically responsive photonic nanochains. Angewandte Chemie. 123(16): 3831-3834. https://doi.org/10.1002/anie.201100290. Hunt CE, Pasetto P, Ansell RJ, Haupt K. (2006). A fluorescence polarisation molecular imprint sorbent assay for 2, 4-D: A non-separation pseudo-immunoassay. Chemical Communications. (16): 1754-1756. https://doi.org/10.1039/B516194K. John S. (1987). Strong Localization of Photons in Certain Disordered Dielectric Super Lattices. Physical Review Letters. 58(23): 2486-2489. https://doi.org/10.1103/PhysRevLett.58.2486. Kim C, Ryu HD, Chung EG, Kim Y, Lee JK. (2018). A review of analytical procedures for the simultaneous determination of medically important veterinary antibiotics in environmental water: Sample preparation, liquid chromatography, and mass spectrometry. Journal of Environmental Management. 217(JUL.1): 629-645. https://doi.org/10.1016/j.jenvman.2018.04.006. Le T, Sun Q, Xie Y, Shu L, Liu J, Xu J, Xiong J, Cao X. (2018). A highly sensitive aptasensor for sulfamethazine detection using an enzyme-linked aptamer assay. Food analytical methods. 11: 2778-2787. https://doi.org/10.1007/s12161-018-1258-2. Li L, Lin Z, Huang Z, Peng A. (2018). Rapid detection of sulfaguanidine in fish by using a photonic crystal molecularly imprinted polymer. Food Chemistry. https://doi.org/10.1016/j.foodchem.2018.12.073. Liu Y, Luo W, Fan Q, Ma H, Yin Y, Long Y, Guan J. (2023). Polyphenol‐Mediated Synthesis of Superparamagnetic Magnetite Nanoclusters for Highly Stable Magnetically Responsive Photonic Crystals. Advanced Functional Materials. 2303470. https://doi.org/10.1002/adfm.202303470. Luo W, Ma H, Mou F, Zhu M, Yan J, Guan J. (2014). Steric‐Repulsion‐Based Magnetically Responsive Photonic Crystals. Advanced Materials. 26(7): 1058-1064. https://doi.org/10.1002/adma.201304134. Paoletti F, Sdogati S, Barola C, Giusepponi D, Moretti S, Galarini R. (2022). Two-procedure approach for multiclass determination of 64 antibiotics in honey using liquid chromatography coupled to time-of-flight mass spectrometry. Food Control. (136-): 136. https://doi.org/10.1016/j.foodcont.2022.108893. Ramstroem O, Andersson LI, Mosbach K. (1993). Recognition sites incorporating both pyridinyl and carboxy functionalities prepared by molecular imprinting. The Journal of Organic Chemistry. 58(26): 7562-7564. https://doi.org/10.1021/jo00078a041. Spielmeyer A, Ahlborn J, Hamscher G. (2014). Simultaneous determination of 14 sulfonamides and tetracyclines in biogas plants by liquid-liquid-extraction and liquid chromatography tandem mass spectrometry. Analytical and Bioanalytical Chemistry. 406(11): 2513-2524. https://doi.org/10.1007/s00216-014-7649-3. Vallano PT, Remcho VT. (2000). Highly selective separations by capillary electrochromatography: molecular imprint polymer sorbents. Journal of Chromatography A. 887(1-2): 125-135. https://doi.org/10.1016/S0021-9673(99)01199-1. Villa CC, Sánchez LT, Valencia GA, Ahmed S, Gutiérrez TJ. (2021). Molecularly imprinted polymers for food applications: A review. Trends in Food Science & Technology. 111: 642-669. https://doi.org/10.1016/j.tifs.2021.03.003. Wang H, Chen Q-W, Yu Y-F, Cheng K, Sun Y-B. (2011). Size-and solvent-dependent magnetically responsive optical diffraction of carbon-encapsulated superparamagnetic colloidal photonic crystals. The Journal of Physical Chemistry C. 115(23): 11427-11434. https://doi.org/10.1021/jp201893z. Wang J, Zhang Y, Wang S. (2011). Biofnspired Colloidal Photonic Crystals with Controllable Wettability. Accounts of Chemical Research. (6): 44. https://doi.org/10.1021/ar1001236. Wang S, Wang Z, Zhang L, Xu Y, Xiong J, Zhang H, He Z, Zheng Y, Jiang H, Shen J. (2022). Adsorption and convenient ELISA detection of sulfamethazine in milk based on MOFs pretreatment. Food Chemistry. 374: 131712. https://doi.org/10.1016/j.foodchem.2021.131712. Xu J, Hu T, Zhao Q, Chen X, Cao Y. (2022). Fe 3 O 4 @ SiO 2 /PAM/Glycerol photonic crystal film as a long-term effective sensor for ambient humidity. Materials Research Bulletin. 153: 111895. https://doi.org/10.1016/j.materresbull.2022.111895. Xu J, Shang M, Liu J, Chen X, Cao Y. (2021). Simultaneous self-assembly of molecularly imprinted magnetic nanoparticles to construct a magnetically responsive photonic crystals sensor for bisphenol A. Sensors and Actuators B: Chemical. 338: 129858. https://doi.org/10.1016/j.snb.2021.129858. Xu W, Su S, Jiang P, Wang H, Dong X, Zhang M. (2010). Determination of sulfonamides in bovine milk with column-switching high performance liquid chromatography using surface imprinted silica with hydrophilic external layer as restricted access and selective extraction material. Journal of Chromatography A. 1217(46): 7198-7207. https://doi.org/10.1016/j.chroma.2010.09.035. Xu X, Friedman G, Humfeld KD, Majetich SA, Asher SA. (2002). Synthesis and utilization of monodisperse superparamagnetic colloidal particles for magnetically controllable photonic crystals. Chemistry of Materials. 14(3): 1249-1256. https://doi.org/10.1021/cm010811h. Yablonovitch E. 1995. Photonic band-gap structures. Springer US. https://doi.org/10.1364/JOSAB.10.000283. Yang M, Wu X, Hu X, Wang K, Zhang C, Gyimah E, Yakubu S, Zhang Z. (2019). Electrochemical immunosensor based on Ag + -dependent CTAB-AuNPs for ultrasensitive detection of sulfamethazine. Biosensors and Bioelectronics. 144: 111643. https://doi.org/10.1016/j.bios.2019.111643. Yang Y, Zhang H, Zhou G, Zhang S, Chen J, Deng X, Qu X, Chen Q, Niu B. (2022). Risk Assessment of Veterinary Drug Residues in Pork on the Market in the People's Republic of China. Journal of food protection. 85(5): 815-827. https://doi.org/10.4315/JFP-21-411. Yu H, Tao Y, Chen D, Wang Y, Huang L, Peng D, Dai M, Liu Z, Wang X, Yuan Z. (2011). Development of a high performance liquid chromatography method and a liquid chromatography–tandem mass spectrometry method with the pressurized liquid extraction for the quantification and confirmation of sulfonamides in the foods of animal origin. Journal of Chromatography B. 879(25): 2653-2662. https://doi.org/10.1016/j.aca.2010.09.032. Zhao H, Ding M, Gao Y, Deng W. (2014). Determination of Sulfonamides in Pork, Egg, and Chicken Using Multiwalled Carbon Nanotubes as a Solid-Phase Extraction Sorbent Followed by Ultra-Performance Liquid Chromatography/Tandem Mass Spectrometry. Journal of AOAC International. (97-5). https://doi.org/10.5740/jaoacint.13-133. Zhou Q, Peng D, Wang Y, Pan Y, Wan D, Zhang X, Yuan Z. (2014). A novel hapten and monoclonal-based enzyme-linked immunosorbent assay for sulfonamides in edible animal tissues. Food Chemistry. 154: 52-62. https://doi.org/10.1016/j.foodchem.2014.01.016. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 30 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers invited by journal 19 Jun, 2025 Editor assigned by journal 18 May, 2025 Submission checks completed at journal 18 May, 2025 First submitted to journal 17 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6687241","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474267101,"identity":"2cd056d7-a6b0-4974-879b-c076d148bd1f","order_by":0,"name":"Yitong Yin","email":"","orcid":"","institution":"Qilu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yitong","middleName":"","lastName":"Yin","suffix":""},{"id":474267103,"identity":"b8b9baa7-4014-4d52-ad08-6666f4ee1195","order_by":1,"name":"Xin Wang","email":"","orcid":"","institution":"Qilu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":474267104,"identity":"4f47192b-7b45-40af-ba5d-94bb3c7b132e","order_by":2,"name":"Huihui Hao","email":"","orcid":"","institution":"Qilu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Huihui","middleName":"","lastName":"Hao","suffix":""},{"id":474267105,"identity":"ec71015e-171f-4236-95ea-62de3f90ce50","order_by":3,"name":"Xiaolei Zhao","email":"","orcid":"","institution":"Qilu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaolei","middleName":"","lastName":"Zhao","suffix":""},{"id":474267106,"identity":"dab4da7b-fb13-4f41-bf51-27ca1abaccbe","order_by":4,"name":"Jinxing He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACxmYGNgaGCgYZEEeCBC1nGHiI1wIEbAyMbaRoYW7nPfbg4zxrHoMDzAdv8zDY5RHhML50w5nb0oFa2JKteRiSi4nQwmMmzbvtMFALkMHDcCCxgSgtf+eAtPB/I0ELYwPYFjbitUj2HEvnkTzMZmw5xyCZsBbD/jNmEj9qrOX4jjc/vPGmwo4ILRAVzGDEwGBASD0QyDPAtIyCUTAKRsEowAUA/Hcx0m5gv2sAAAAASUVORK5CYII=","orcid":"","institution":"Qilu University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jinxing","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-05-17 13:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6687241/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6687241/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85202567,"identity":"c0ab1076-2663-497e-8294-bc4d8a0aac6e","added_by":"auto","created_at":"2025-06-23 10:49:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172804,"visible":true,"origin":"","legend":"\u003cp\u003ePreparation flow of MMINPs\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/e315857751199f57d1f496b5.png"},{"id":85202574,"identity":"c32b1fe2-89ae-4ba9-9810-d15b17ece7bf","added_by":"auto","created_at":"2025-06-23 10:49:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":511365,"visible":true,"origin":"","legend":"\u003cp\u003eSEM image of MMINPs\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/f7980b7c35cd3dee12066ccb.png"},{"id":85203513,"identity":"36780efa-2047-4dae-adf5-fce76c72915d","added_by":"auto","created_at":"2025-06-23 10:57:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47511,"visible":true,"origin":"","legend":"\u003cp\u003eFe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e The effect of addition amount on the adsorption performance of MMINPs\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/b1038efadc873f341e5f1241.png"},{"id":85202569,"identity":"e157e37e-26fd-41a2-a2b0-4c1823df534c","added_by":"auto","created_at":"2025-06-23 10:49:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42299,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of polymerization time on the adsorption performance of MMINPs\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/4e73d06b9bdff30ab79342ef.png"},{"id":85202565,"identity":"71489bc1-2dc5-4645-810d-0776785877f6","added_by":"auto","created_at":"2025-06-23 10:49:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33209,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of solvents for the adsorption of SM2 by MMINPs\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/0b71226982bd6f04ca8eae8e.png"},{"id":85202571,"identity":"fe4ea16a-6e85-457e-ab09-94bcf453ad3d","added_by":"auto","created_at":"2025-06-23 10:49:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":31439,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of eluent\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/72465ecc5698dd69e556d4cd.png"},{"id":85203512,"identity":"f0910122-0af9-44b7-8dd3-0c82474695fa","added_by":"auto","created_at":"2025-06-23 10:57:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":73307,"visible":true,"origin":"","legend":"\u003cp\u003eUV detection of eluates of MMINPs\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/e92aacf3833cc8204cd112f7.png"},{"id":85203519,"identity":"a4477380-2301-4ba7-b846-cfb0f3dff6cf","added_by":"auto","created_at":"2025-06-23 10:57:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":84649,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of pH for MMINPs dispersion\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/44e5681398daf01dc9ac75fd.png"},{"id":85202576,"identity":"3bb974f5-88eb-4433-89d4-d31fe8531410","added_by":"auto","created_at":"2025-06-23 10:49:28","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":93175,"visible":true,"origin":"","legend":"\u003cp\u003eFourier transform infrared spectra of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e, Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e, MNINPs, MMINPs and SM2\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/48398d8d4ca8fe9dd81b524e.png"},{"id":85202577,"identity":"81121e3d-8826-453c-820a-ecf3ae0ea1fd","added_by":"auto","created_at":"2025-06-23 10:49:28","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":55212,"visible":true,"origin":"","legend":"\u003cp\u003eHysteresis loops of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e, Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e, MNINPs and MMINPs\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/095efb6de67c454aa3962c67.png"},{"id":85203517,"identity":"b69c93c3-e776-4ab9-a632-655c5a2998e6","added_by":"auto","created_at":"2025-06-23 10:57:28","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":89233,"visible":true,"origin":"","legend":"\u003cp\u003eNitrogen adsorption desorption isotherm and pore size distribution of MMINPs\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/977713d6cc1359a43fcf1dbd.png"},{"id":85203711,"identity":"c5da0e80-a150-4678-bc5e-91ac019aa5a7","added_by":"auto","created_at":"2025-06-23 11:05:29","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":65521,"visible":true,"origin":"","legend":"\u003cp\u003eXRD patterns of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e, Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e, MNINPs and MMINPs\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/019e03b34d01b45e173d2830.png"},{"id":85202594,"identity":"f1e554cb-7b3a-4a0e-ac2b-75b0000b4b42","added_by":"auto","created_at":"2025-06-23 10:49:29","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":53601,"visible":true,"origin":"","legend":"\u003cp\u003eAdsorption kinetics curves of MMINPs and MNINPs\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/2533eff03aa22cab70c81a7f.png"},{"id":85202628,"identity":"66090665-7f88-4fa5-802a-465db5bdf55e","added_by":"auto","created_at":"2025-06-23 10:49:30","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":61690,"visible":true,"origin":"","legend":"\u003cp\u003eFitting of quasi first and quasi second order kinetic models for MMINPs\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/6bafa4b48bcdc116e40b0d9a.png"},{"id":85202591,"identity":"041b06fb-f21e-4263-86ee-b1a314efe221","added_by":"auto","created_at":"2025-06-23 10:49:29","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":51471,"visible":true,"origin":"","legend":"\u003cp\u003eElovich equation fitting of MMINPs\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/7f6a1472cb52aa38df024aa4.png"},{"id":85203522,"identity":"fd819ba6-d61c-438c-bb44-d9a18018265c","added_by":"auto","created_at":"2025-06-23 10:57:29","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":46611,"visible":true,"origin":"","legend":"\u003cp\u003eFitting of intra particle diffusion model for MMINPs\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/a44e8014f0c900c5623499b5.png"},{"id":85202595,"identity":"4cb3b50b-2783-4447-88d1-02d76ca1dfd9","added_by":"auto","created_at":"2025-06-23 10:49:29","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":49348,"visible":true,"origin":"","legend":"\u003cp\u003eBinding isotherms of MMINPs and MNINPs\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/1f97b1b8dd8db8edbef8100d.png"},{"id":85203526,"identity":"789b8f67-4113-4f1d-a502-cdec30c8626e","added_by":"auto","created_at":"2025-06-23 10:57:29","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":117683,"visible":true,"origin":"","legend":"\u003cp\u003eAdsorption fitting isotherms of SM2 on MMINPs (A) and MNINPs (B)\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/8e2359d0287808383c413932.png"},{"id":85202592,"identity":"e0799ad7-33d5-44f9-ae25-a2687dbe0c32","added_by":"auto","created_at":"2025-06-23 10:49:29","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":54153,"visible":true,"origin":"","legend":"\u003cp\u003eScatchard analysis plot of MMINPs\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/1eb5eb435d1e8e66a7b46361.png"},{"id":85203528,"identity":"2a277f7c-199d-4098-b445-9f99de01ddd5","added_by":"auto","created_at":"2025-06-23 10:57:29","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":100861,"visible":true,"origin":"","legend":"\u003cp\u003eScatchard analysis plot of MMINPs\u003c/p\u003e","description":"","filename":"20.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/6ab15b5d3697e8ede70c82af.png"},{"id":85202601,"identity":"50ba888c-ec62-4670-8ab9-001299051406","added_by":"auto","created_at":"2025-06-23 10:49:29","extension":"png","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":122704,"visible":true,"origin":"","legend":"\u003cp\u003eReflection peak changes caused by the adsorption of SM2 solution by MMINPs\u003c/p\u003e","description":"","filename":"21.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/29a670f292438251c1775df0.png"},{"id":85202616,"identity":"adefaaac-44d4-4229-a194-3437de71302c","added_by":"auto","created_at":"2025-06-23 10:49:30","extension":"png","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":35218,"visible":true,"origin":"","legend":"\u003cp\u003eDifference in diffraction wavelength shift caused by SM2 solution with adsorption concentrations of 10\u003csup\u003e2\u003c/sup\u003e ng/L~1 mg/L for MMINPs and MNINPs\u003c/p\u003e","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/32bdc207ec6755be24ebc4e7.png"},{"id":85202598,"identity":"a7b284bc-9848-4dca-b344-609dc997624f","added_by":"auto","created_at":"2025-06-23 10:49:29","extension":"png","order_by":23,"title":"Figure 23","display":"","copyAsset":false,"role":"figure","size":39321,"visible":true,"origin":"","legend":"\u003cp\u003eStandard curve for SM2 detectio\u003c/p\u003e","description":"","filename":"23.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/13faadd777dadbfc0c591f68.png"},{"id":85203537,"identity":"75c3d481-5425-4f66-9d38-993ba4c55984","added_by":"auto","created_at":"2025-06-23 10:57:30","extension":"png","order_by":24,"title":"Figure 24","display":"","copyAsset":false,"role":"figure","size":77049,"visible":true,"origin":"","legend":"\u003cp\u003eRepetitive practicality detection of sensors\u003c/p\u003e","description":"","filename":"24.png","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/6ac4b1c77bf74d0cb033a85b.png"},{"id":85204888,"identity":"aaa21766-3ea8-41d5-a828-2b549afbdacc","added_by":"auto","created_at":"2025-06-23 11:21:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3393498,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6687241/v1/a7a692a2-c30f-41a6-b251-17e372ef557a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enrichment and detection of sulfadimethylpyrimidine in food by magnetic molecularly imprinted photonic crystals","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe improvement of living standards has led to increased attention to food safety from farm to table(Kim et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Sulfonamides are a class of low-cost synthetic antibiotics with broad-spectrum antimicrobial properties and stable performance(Spielmeyer et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). They are widely used to prevent and treat bacterial infections(Franco et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Sulfonamides are challenging to dissolve in water and require large and frequent doses, which can result in excessive residues. These compounds can accumulate in the human body through the food chain, posing a risk to health. Allergic reactions, such as skin rashes and drug fever, as well as urinary system diseases, are the main manifestations of sulfonamide accumulation. Animal experimental studies have also shown that some sulfonamides have teratogenic and carcinogenic effects(Chatzimitakos et al. 2020; Fuhrmann et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). China and the European Union have set the maximum total residue level of SAs in animal-derived food at 100 \u0026micro;g/kg. Additionally, China has stipulated that the residue level of SM2 as a single drug should not exceed 25 \u0026micro;g/kg(Y. Yang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The most common methods for determining SAs in food are high-performance liquid chromatography (HPLC) (Zhao et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), ultra-high liquid chromatography (UHPLC), and liquid chromatography-time-of-flight mass spectrometry (TFMS) (Paoletti et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Various methods are used for detection, including high-pressure extraction high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) (Yu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), enzyme-linked immunosorbent assay (ELISA) (Burkin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), spectrophotometric methods(Dmitrienko et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and chemical detection(De La Pe\u0026ntilde;a et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Currently, 4czpre-treatment methods are commonly used in conjunction with ultra-high performance liquid chromatography (UPLC) detection for efficient detection. Although the chromatographic method has a low detection limit and is capable of detecting a wide range of structural analogs, it requires operator training and has a long detection times(Le et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; W. Xu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; M. Yang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, faster and simpler detection methods are necessary.\u003c/p\u003e \u003cp\u003eMolecular imprinting technology (MIT) has rapidly developed since the successful preparation of molecularly imprinted polymers of theophylline in 1993(Ramstroem et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) Molecular imprinting is a technique developed in recent decades for preparing novel polymeric materials with predictable structure, selective recognition, and utility. The theoretical basis of this technique is the specific binding between antigen and antibody in immunology. The process can be divided into three parts. Firstly, the template molecule binds to the functional monomer(Alexander et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Cai et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hunt et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vallano et al. 2000). Secondly, under certain conditions such as temperature or light, the initiator triggers a free radical polymerization reaction to produce a highly cross-linked polymer. Finally, the template molecules are eluted in a specific manner to form molecularly imprinted cavities with specificity(Basak et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Various methods have been developed to synthesize molecularly imprinted polymers to improve their adsorption, stability and functionality. These include native polymerization, precipitation polymerization, suspension polymerization, and reactive/controlled radical polymerization. Molecularly imprinted polymers have been extensively used for identifying hazardous contaminants in food as adsorbent materials and recognition components in sample pretreatment. They have also been used in combination with sensors to form sensing and detection systems(Villa et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 1987, E. Yablonovitc(Eli et al. 1987) and S. John(John \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) independently proposed the concept of photonic crystals (PCs) in the United States. PCs are based on the concepts of semiconductor crystals and electronic band gaps. Yablonovitc studied self-emission, while John studied photon localization. Photonic crystals, also known as opal crystals, are periodic dielectric structures with photonic band gap (PBG) properties. They are arranged in a periodic manner to interact with light, displaying different colors on the same length scale as the wavelength of the interacting electromagnetic radiation(J. Wang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yablonovitch \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Photonic crystals can be classified into opal and anti-opal photonic crystals based on their structural features. Photonic crystals can be classified into one-, two-, and three-dimensional structures based on their repeating periodic structure(Christoph et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Materials that respond to changes in external magnetic fields are called magnetically responsive photonic crystals (MRPCs) and have the advantage of instantaneous reversible self-assembly(Ge et al. 2008; Y. Hu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Magnetic-responsive photonic crystals consist of monodisperse magnetic nanoparticles arranged in a linear one-dimensional structure under the influence of a magnetic field. This arrangement results in structural color changes that are dependent on factors such as magnetic field strength, humidity, spatial potential resistance, and surface charge(Dai et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y. Hu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; H. Wang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; X. Xu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Xu et al. developed moisture-responsive photonic crystals (MRPCs) that exhibit a spectral shift in response to changes in ambient humidity. Specifically, the reflected wavelength changes from 436 nm to 652 nm ,and the sensor color changes from blue-violet to bright orange-yellow when the ambient humidity is changed from 11\u0026ndash;93%(J. Xu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Numerous studies have reported the use of MRPCs in detection. Xu's group has developed molecularly imprinted magnetic colloidal nanoparticles (MIMCNPs) for detecting melamine (MEL) and bisphenol-A(T. Hu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; J. Xu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Amphiphilic random copolymers were used as emulsifiers and MMINP coatings, while oleic acid-modified magnetite nanoparticles were used as magnetic cores. Template molecules were added and self-assembled in a microemulsion system to simultaneously complete the formation of magnetic nanoparticles and molecular imprinting. The sensor can produce structural color changes as the concentration of the substance to be measured changes. This enables the construction of a simple and rapid colorimetric MIMCNPs sensor for the first time.\u003c/p\u003e \u003cp\u003eWhile existing studies can offer technical support for the preparation of magnetic molecularly imprinted photonic crystal sensors, there are few applications for detecting veterinary drug residues and contaminants in food. Additionally, ensuring strong magnetism, particle size, and effective molecular imprinting of nanoparticles poses significant challenges. In this paper, Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e nanoparticles with superparamagnetic properties were prepared using the solvothermal method. A layer of SiO\u003csub\u003e2\u003c/sub\u003e was then encapsulated using the sol-gel method. Subsequently, molecularly imprinted layers were synthesized to obtain Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e@MIPs. We observed changes in the surface charge of the particles and the dissolution of the molecularly imprinted layers. The concentration of SM2 causes alterations in structural colors under fixed magnetic field strength, enabling visual detection to some extent. To assess the sensor's practicality, water, milk, and chicken samples were examined. Magnetic nanoparticles were easily collected by magnets, simplifying the sample pre-treatment process and providing ideas for rapid on-site screening.\u003c/p\u003e"},{"header":"2. Experimental section","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Chemicals and materials\u003c/h2\u003e \u003cp\u003eSM2, SD, SIZ, EGDMA, and MAA were all purchased from Aladdin Reagent Co., Ltd. (Shanghai, China). AIBN, ethylene glycol, PEG-4000, NH\u003csub\u003e3\u003c/sub\u003e\u0026middot;H\u003csub\u003e2\u003c/sub\u003eO, tetraethyl orthosilicate (TEOS) and sodium acetate were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Ethanol, methanol, and acetic acid were purchased from Xilong Chemical Co., Ltd. (Guangdong, China). Milk and chicken were purchased from a local supermarket in Jinan, Shandong Province. The water samples were local tap water from Jinan. With the exception of AIBN, all chemicals are of analytical grade and can be used directly without any further purification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Instruments\u003c/h2\u003e \u003cp\u003eReflectance spectrum measurement was performed on a fiber optic spectrometer (PG 2000, Shanghai Fuxiang Optical Co.). Dual beam ultraviolet\u0026ndash;visible spectrophotometry (TU-1901, Beijing General Instrument Co., China), scanning electron microscopy (SEM, S4800, Hitachi, Japan), X-ray diffraction (XRD, Rigaku, Japan), High Performance Liquid Chromatograph (HPLC, SIL-20A, Shimadzu Prominence, Japan), and Vibration Sample Magnetometer (VSM, 8604, Lake Shore Cryotronics Inc, America) were used to characterize the polymers. The Fourier Transform Infrared Spectrometer (NEXUS 670) was obtained from Thermo Nicolet, Inc. Automated Gas Sorption Analyzer (BET, ASAP2460-2MP) was obtained from Georgia, United States.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Synthesis of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e magnetic nanoparticles\u003c/h2\u003e \u003cp\u003eMonodisperse spherical magnetic nanoparticles (MNPs) were synthesized by solvothermal method. First, 0.67 g of FeCl\u003csub\u003e3\u003c/sub\u003e\u0026middot;H\u003csub\u003e2\u003c/sub\u003eO was fully dissolved into 20 mL of ethylene glycol, subsequently, 0.5 g of PEG-4000 and 2.8 g NaAc were added sequentially, and the dissolution was stirred while sonication. Finally, the solution was transferred to a 50 mL PTFE-lined autoclave and reacted at 200℃ for 10 hours. The autoclave was allowed to cool down naturally to room temperature.The black magnetic nanoparticles were then subjected to alternately washing with anhydrous ethanol and deionized (DI) water at least three times, followed by dying in vacuum at 50 ℃ overnight.\u003c/p\u003e \u003cp\u003e0.06g of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e was taken separately and added to a round bottom flask containing 2 mL of NH\u003csub\u003e3\u003c/sub\u003e\u0026middot;H\u003csub\u003e2\u003c/sub\u003eO and 13 mL of water. The mixture was sonicated for 5 minutes and then 67 mL of anhydrous ethanol was added and sonicated for another 5 minutes. 218 \u0026micro;L of TEOS was added drop by drop and the mixture was mechanically stirred for 1 hour to obtain Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e. Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e was recovered using a magnet, washed with anhydrous ethanol three times, and dried in a vacuum at 50℃ overnight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Preparation of the SM2-MMINPs\u003c/h2\u003e \u003cp\u003eThe preparation of SM2-MMINPs is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Dissolve 0.0278g of SM2 into 20 mL of methanol, add 0.034 mL of MAA, sonicate for 20 minutes and place at 4℃ overnight. Next, add 0.495 mL EGDMA to the above solution, sonicated for 5 minutes. Then, add 10 mg AIBN and 0.05 g Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e and sonicate the solution for 20 minutes and remove the O\u003csub\u003e2\u003c/sub\u003e by bubbling with N\u003csub\u003e2\u003c/sub\u003e for 10 minutes. Seal the system with a sealing membrane and mechanically mix at 60\u0026deg;C for 6 hours. The polymer was recovered with a rubidium magnet and washed three times with methanol. Finally, SM2 was eluted from the polymer with acetic acid and 90% methanol (1:9, v/v) until it was no longer detectable by UV-Vis spectroscopy. Excess eluent was washed off with methanol, recovered with magnets and dried in a vacuum at 50℃.The preparation process of magnetic non-molecularly imprinted nanoparticles (MNINPs) was identical to that of MMINPs, except that SM2 was not added.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Reflection spectrum measurement\u003c/h2\u003e \u003cp\u003eThe dispersion of 1% (w/w) MMINPs or MNINPs was prepared using methanol and sonicated for 5 minutes. The probe of the fibre-optic spectrometer was fixed at the same height and perpendicular to the magnet plane. The changes in the reflectance spectra of the sensors induced by the SM2 methanol solution at concentrations ranging from 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 10\u003csup\u003e3\u003c/sup\u003e \u0026micro;g/L were detected. All experiments were performed under the same conditions to avoid interference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Sample preparation\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 Water samples\u003c/h2\u003e \u003cp\u003eWater samples are simply filtered through a 0.22 \u0026micro;m membrane to remove solid impurities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Milk samples\u003c/h2\u003e \u003cp\u003eThe milk sample (5 mL) was centrifuged at 10,000 rpm for 10 minutes to remove the lipids. Next, 5 mL of methanol was added to precipitate the excess proteins. The supernatant was then centrifuged again, and the final supernatant was used for detection using a fibre optic spectrometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.6.3 Chicken samples\u003c/h2\u003e \u003cp\u003eThe chicken sample (5.0 g) was minced and mixed with hydroxylamine hydrochloride (1.5 mL) and ammonium acetate (3.5 ml, 50 mmol/L, pH 4.5). The mixture was vortexed vigorously for 5 minutes. Then, acetonitrile (2 mL) was added and the mixture was sonicated for 5 minutes. The supernatant was collected by centrifugation at 10,000 rpm for 10 minutes and dried with N\u003csub\u003e2\u003c/sub\u003e at room temperature. It was then reconstituted with 5 mL of methanol.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Detection conditions for HPLC\u003c/h2\u003e \u003cp\u003eHigh performance liquid chromatography (HPLC) with a UV detector was used for the determination of SM2, SD and SIZ adsorption in this experiment. The column temperature was 25 ℃, the flow rate was 0.8 mL/min, the wavelength was 270 nm, the mobile phase was water:methanol:acetic acid\u0026thinsp;=\u0026thinsp;70:30:0.5 (v/v/v) and the injection volume was 20 \u0026micro;L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Adsorption kinetics experiment\u003c/h2\u003e \u003cp\u003eIn order to assess the adsorption performance of the sensor, kinetic experiments were conducted. The amount of SM2 adsorbed (Q, mg/g) was calculated using the following equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Q=\\left({C}_{0}-{C}_{e}\\right)V/m\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{(2.1)}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003ewhere Q is the adsorbed amount (mg/g), C\u003csub\u003e0\u003c/sub\u003e is the initial concentration of SM2 solution (mg/L), C\u003csub\u003ee\u003c/sub\u003e is the concentration of SM2 solution after adsorption (mg/L), V is the volume of SM2 standard methanol solution added (mL), and m is the mass of MMINPs or MNINPs (mg).\u003c/p\u003e \u003cp\u003eThe kinetic substrate and rate control steps of SM2 adsorption by MMINPs were investigated by fitting the obtained data with quasi-primary and quasi-secondary adsorption kinetic models with the equations shown below:\u003c/p\u003e \u003cp\u003eQuasi-primary adsorption kinetic equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{t}={Q}_{e}\\left(1-{e}^{-K1t}\\right)\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{(2.2)}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eQuasi-secondary adsorption kinetic equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{t}={K}_{2}{Q}_{e}^{2}t/(1+{K}_{2}{Q}_{e}t)\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{(2.3)}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003ewhere Q\u003csub\u003ee\u003c/sub\u003e (mg/g) and Q\u003csub\u003et\u003c/sub\u003e (mg/g) are the amount of SM2 adsorbed by MMINPs at adsorption equilibrium and at time t, respectively, and K\u003csub\u003e1\u003c/sub\u003e (min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and K\u003csub\u003e2\u003c/sub\u003e (g/(mg\u0026middot;min)) are quasi-primary and quasi-secondary adsorption rate constants. Quasi-primary adsorption kinetics are generally used to characterize the kinetics of the initial phase of adsorption. The quasi-secondary adsorption kinetics can characterize the whole adsorption process, the more imprinted holes on the molecularly imprinted material, the stronger the adsorption capacity, and the quasi-secondary adsorption kinetics generally involves a variety of electron binding energy sites.\u003c/p\u003e \u003cp\u003eIn addition, the Elovich model is a commonly utilized approach for describing the adsorption kinetic process, with the following equations:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{t}=\\text{ln}\\left(\\alpha\\:\\beta\\:\\right)/\\beta\\:+\\text{ln}\\left(t\\right)/\\beta\\:\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{(2.4)}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe intra-particle diffusion process is also frequently employed to describe the adsorption kinetic process and is represented by the following linear equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{t}={K}_{int}{t}^{\\frac{1}{2}}+C\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{(2.5)}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eIn this context, β denotes the activation energy and degree of manifestation coverage of adsorption (g/mg), α denotes the initial adsorption rate constant (mg/(g\u0026middot;min)), k\u003csub\u003eint\u003c/sub\u003e is the internal diffusion coefficient of the particles (mg/(g\u0026middot;min\u003csup\u003e1/2\u003c/sup\u003e)), and C is a constant related to the thickness of the boundary layer (mg/g). The Elovich model, which assumes that the adsorption process occurs via chemisorption in an ideal environment, posits that the adsorption process is related to the diffusive movement of the adsorbed substance, the specific adsorption sites of the adsorbent, and the diffusion of the adsorbed substance into the interior of the adsorbent. When the adsorption process involves both surface diffusion and internal diffusion, the adsorption rate can be fitted with an intraparticle diffusion model. When the detected substance, such as SM2, penetrates the interior of the molecularly imprinted polymer, the data can be fitted by the intra-particle diffusion model. In general, the fitting results can be classified into two categories. One such result is a straight line fit of Q\u003csub\u003et\u003c/sub\u003e versus t\u003csup\u003e1/2\u003c/sup\u003e, accompanied by a value of parameter C close to 0, indicating that the adsorption process is predominantly internal diffusion. In the alternative scenario, the fitting result is a curve, which indicates that the adsorption process involves multiple adsorption types. If the fitted curve passes through the origin, it can be concluded that the diffusion of the system is controlled by a single rate. Conversely, if the curve does not pass through the origin, it can be inferred that the internal diffusion process is not the sole step controlling the adsorption of SM2 by the sensor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Equilibrium binding experiments\u003c/h2\u003e \u003cp\u003eFive milligrams of MMIMPs or MNINPs were added to two milliliters of a methanol solution of SM2 at varying concentrations (2, 4, 6, 8, 10, 12, and 14 milligrams per liter) and incubated on a shaker while shaking at 100 rotations per minute for one hundred minutes. The magnetic nanoparticles were recovered by magnet adsorption, and the residual solution was aspirated with a syringe and filtered through a 0.45 \u0026micro;m organic membrane. The concentration of SM2 was then detected by HPLC. The adsorption amount of SM2 was calculated using Eq.\u0026nbsp;(2.1).\u003c/p\u003e \u003cp\u003eTo further elucidate the adsorption mechanism of the SM2 sensor prepared in this experiment, the equilibrium binding experimental data were fitted and analyzed using Langmuir and Freundlich isotherm equations, as illustrated in Equations (2.6) and (2.7), respectively.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{q}_{e}={K}_{L}{q}_{m}{C}_{e}/(1+{K}_{L}{C}_{e})\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{(2.6)}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{q}_{e}={K}_{F}{C}_{e}^{\\frac{1}{n}}\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{(2.7)}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe quantity of SM2 adsorbed at the adsorption equilibrium of MMINPs (mg/g) is denoted by q\u003csub\u003ee\u003c/sub\u003e, while the maximum amount of SM2 adsorbed after the adsorption equilibrium of MMINPs (mg/g) is represented by q\u003csub\u003em\u003c/sub\u003e. Finally, C\u003csub\u003ee\u003c/sub\u003e is the equilibrium concentration of SM2 solution after adsorption (mg/L). Among these parameters, the numerical magnitude of Langmuir's constant, K\u003csub\u003eL\u003c/sub\u003e (L/mg), is related to the nature of the adsorbent, the nature of the adsorbent substance, and the temperature. It represents the strength of the adsorption capacity. The Freundlich exponent, k\u003csub\u003ef\u003c/sub\u003e (\u0026micro;g/mg (L/mg)\u003csup\u003e1/n\u003c/sup\u003e), represents the adsorption amount at the concentration in units of C. The value of n represents the surface specificity and strength of adsorption of the adsorbent material. It is a characteristic adsorption parameter, with 1/n typically ranging from 0 to 1. A value of 1/n closer to 0 indicates a greater likelihood of adsorption. The Langmuir equation is frequently employed to analyze the adsorption process of adsorbent materials that are solely present on the surface, and the energy of the active site is homogeneous. The Freundlich model is typically utilized to fit the reversible adsorption process of a non-homogeneous phase system, which has multilayered adsorption on a non-homogeneous surface to the inner layer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Scatchard equation analysis\u003c/h2\u003e \u003cp\u003eThe Scatchard equation, shown in (2.8), is a commonly used analytical tool for the analysis of the number and nature of specific binding sites. Q represents the equilibrium adsorption of SM2 by MMINPs (\u0026micro;g/mg), C\u003csub\u003ee\u003c/sub\u003e is the concentration of SM2 supernatant at equilibrium (mg/L), K\u003csub\u003ed\u003c/sub\u003e (mg/L) is the dissociation constant of the binding site, and Q\u003csub\u003emax\u003c/sub\u003e (mg/g) is the apparent maximum binding. The values of K\u003csub\u003ed\u003c/sub\u003e and Q\u003csub\u003emax\u003c/sub\u003e can be calculated from the slopes and intercepts of the linear plots of Q/C\u003csub\u003ee\u003c/sub\u003e versus Q.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Q/{C}_{e}={(Q}_{max}-Q)/{K}_{d}\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{(2.8)}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eWhen the Scatchard equation is fitted to the data with a single straight line, it can be concluded that the prepared material contains only one type of binding site. However, when the fit shows two straight lines, it indicates that the material contains both types of binding sites. The maximum adsorption amount, Q\u003csub\u003emax\u003c/sub\u003e, and the dissociation constant, K\u003csub\u003ed\u003c/sub\u003e, were calculated in order to identify the binding site. The K\u003csub\u003ed\u003c/sub\u003e value reflects the affinity of the adsorbent for the analyte. A smaller K\u003csub\u003ed\u003c/sub\u003e value indicates a higher affinity.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 SEM characterization of MMINPs\u003c/h2\u003e \u003cp\u003eIn this experiment, the qualitative and quantitative detection of SM2 is achieved through the use of a photonic crystal material as an optical signal conversion device. The rapid detection of SM2 concentration is possible according to the offset value of the Bragg diffraction peak. The synthesis of nanoparticle microspheres with uniform particle size is a necessary prerequisite for the preparation of high-performance photonic crystal sensors. The morphology of MMINPs was characterized using SEM. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(A), the dried MMINPs exhibited an overall spherical morphology with a more uniform particle size. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(B), the particle size of MMINPs is approximately 200 nm. The MMINPs prepared by this method can be assembled rapidly under the influence of magnets, thus effectively circumventing the potential interference of complex food matrices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Optimization of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e additions\u003c/h2\u003e \u003cp\u003eIn this experiment, Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e is employed as the magnetic core, and its addition determines the quality of the prepared MMINPs. The effect of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e addition on the adsorption performance of MMINPs was evaluated by calculating the adsorption amount and imprinting factor. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, As the mass of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e increases, the imprinting factor gradually rises, reaching a maximum value at an additional amount of 50 mg, after which it gradually decreases. In the event that a certain polymerization time is maintained, insufficient quantities of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e will fail to fully utilize the template molecules, functional monomers, and cross-linkers present in the prepolymerization solution, thereby resulting in waste. Conversely, excess quantities of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e will result in an imprinted layer that is too thin, thereby reducing the adsorption performance. Consequently, the quantity of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e employed in the experiment was 50 mg.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Optimization of aggregation time\u003c/h2\u003e \u003cp\u003eThe polymerization time can be adjusted in order to regulate the thickness of the imprinted layer. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the change in adsorption amount with polymerization time. As the polymerization time increases, the imprinting factor gradually increases until reaching a maximum value of 1.92 at 6 hours. Both MMINPs and MNINPs formed by this polymerization time exhibit a light-green photonic crystal structure color under the action of a magnetic field, which aligns with the fundamental conditions for sensor preparation. Therefore, the 6-hour polymerization time is selected as the optimal time for the reaction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Optimization of adsorption solvents\u003c/h2\u003e \u003cp\u003eIn order to investigate the optimal adsorption conditions of MMINPs, they were dispersed in SM2 solutions of different solvents for adsorption. Five milligrams of MMINPs were added to SM2 methanol, ethanol, water, and acetonitrile solutions at a concentration of 10 mg/L, respectively, and incubated with shaking at 100 rpm for 100 minutes. After the recovery of MMINPs by magnet adsorption, the concentration of SM2 in the supernatant was quantified by HPLC, and the adsorption amount was calculated. The results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The adsorption rate of SM2 by MMINPs was higher when methanol was used as the solvent, thus indicating that methanol was the optimal solvent for the adsorption of SM2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Optimization of elution conditions\u003c/h2\u003e \u003cp\u003eIn order to achieve complete elution of SM2 from MMINPs, the eluent ratios were optimized. The eluent was configured with acetic acid:methanol volume ratios of 0:10, 1:9, 2:8, 4:6, and 1:9 (90%), and the concentration of SM2 in the eluent was detected using HPLC to calculate the recovery. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the acetic acid: methanol ratio of 1:9 (v/v) yielded the highest recovery in the initial four groups. However, this increased volume percentage of acetic acid resulted in a subsequent decline in recovery. This may be attributed to the gradual protonation of the amino group of SM2 due to the addition of excessive acetic acid, which results in the strong polar ammonia atoms on the amino group combining with the hydrogen atoms in the system, thereby reducing the number of recognition groups, which in turn makes it challenging for SM2 to elute. As illustrated in the accompanying figure, the elution effect was enhanced when 90% methanol was used to prepare distilled water for the configuration of the eluent. This was due to the addition of water, which regulated the polarity of the eluent, thereby creating a more favorable environment for the template molecules to be eluted. Consequently, the eluent was configured using acetic acid:methanol in a 1:9 ratio (90% v/v) to elute MMINPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to confirm the degree of elution, a UV spectrophotometer was employed to detect the presence of the UV absorption peak of SM2 in the eluate following each elution. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the UV absorption peak of SM2 was observed at 268 nm. With the increase in the number of elutions, the absorbance exhibited a gradual decline, and the presence of SM2 could no longer be detected after approximately the fifth elution, indicating that the elution process was complete.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Optimization of dispersant solvent pH\u003c/h2\u003e \u003cp\u003eThe MMINPs were incubated in 1 mg/L of SM2 methanol solution for 100 minutes, after which they were dispersed in methanol configured as a 1% (w/w) dispersion system adjusted to different pH values (5, 6, 7, 8, and 9) with 1 mol/L of HCl or NaOH solution. Ultrasonication was then performed for 5 minutes. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the changes in reflected wavelengths at varying pH levels. The reflection wavelength shift is more pronounced when pH\u0026thinsp;=\u0026thinsp;7, while the pH of methanol is typically in the range of 6.8 to 7. Therefore, methanol was employed as the dispersing solution for MMIMPs in the experiment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Characterization of Materials by FTIR Spectrometer\u003c/h2\u003e \u003cp\u003eThe infrared spectrum of the sample is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The Fe-O stretching vibration peak, which is the characteristic peak of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e, is observed at 565 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.The figure shows that the Si-O bending vibration peak is at 465 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and the Si-O-Si symmetric telescopic vibration peak is at 795 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which are characteristic peaks of silica. Additionally, the Si-O-Si antisymmetric telescopic vibration peak is at 1066 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and the Si-OH bending vibration absorption peak is at 950 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. These results confirm the successful synthesis of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@ SiO\u003csub\u003e2\u003c/sub\u003e. The 2981 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e peak corresponds to the -OH peak in MAA, while the 1725 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e peak corresponds to the carbonyl C\u0026thinsp;=\u0026thinsp;O absorption peak in MAA and EGDMA. Additionally, the 1253 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e peak corresponds to the C-O-C absorption peak in EGDMA. The 880 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e peak corresponds to the C-H wobble vibration peak on the pyrimidine ring, while the 1459 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e peak corresponds to the C-H stretching vibration peak in SM2. Finally, the 1047 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e peak corresponds to the peak of R-S(O)-O-R'. The peak at 3350 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e corresponds to the -NH group of SM2 in the synthesized molecularly imprinted polymer layer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Magnetic evaluation of magnetic nanoparticle materials\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the VSM characterisation of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e, Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e, MMINPs and MNINPs. The hysteresis loop is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The coercivity of the magnetic intensity curves of each sample is equal to zero, indicating the absence of hysteresis and remanent magnetization. The saturation magnetic strengths of Fe₃O₄, Fe₃O₄@SiO₂, MMINPs, and MNINPs were calculated to be 91.2 emu/g, 63.3 emu/g, 47.5 emu/g, and 43.9 emu/g, respectively. It was observed that the magnetic properties decreased gradually with the modification of the shell layer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Nitrogen adsorption and desorption experiment\u003c/h2\u003e \u003cp\u003eThe specific surface area and pore size of MMINPs were analyzed by a fully automated gas analyzer. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the hysteresis loop type was identified as H\u003csub\u003e3\u003c/sub\u003e, and the MMINPs exhibited a weak adsorption interaction with SM2, indicating that the sample possessed a mesoporous structure. According to the Brunauer-Emmett-Teller theory, the specific surface area of MMINPs was calculated to be 28.54 m\u003csup\u003e2\u003c/sup\u003e/g, and the average pore size was 9.25 nm. The results demonstrated that MMINPs are a type of microporous material with a considerable specific surface area, theoretically capable of adsorbing template molecules to bind to specific sites and induce the diffusion of SM2 to the interior of the molecularly imprinted polymer layer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.10 XRD characterization of materials\u003c/h2\u003e \u003cp\u003eIn order to verify the physical phase composition of the synthesized materials, the crystalline structures of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e, Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@SiO\u003csub\u003e2\u003c/sub\u003e, and MMINPs were characterized by X-ray diffraction. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, the diffraction peaks observed in the XRD spectrum correspond to the (220), (311), (400), (422), (511) and (440) crystal planes, respectively. A comparison and analysis of the diffraction peaks with the standard diffraction card JCODS NO:19\u0026ndash;0629 of anti-spinel type Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e revealed a high degree of agreement. The shape of the diffraction peaks of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e is narrow and sharp, indicating that the crystal grains are complete and the degree of crystallization is good. Figure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e illustrates that the crystalline structures of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e, Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@ SiO\u003csub\u003e2\u003c/sub\u003e, and MMINPs are all cubic antispinel. The thickening of the shell layer results in a reduction in the peak intensity of Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@ SiO\u003csub\u003e2\u003c/sub\u003e, MNINPs, and MMINPs. Nevertheless, the crystal structure of their Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e cores remains unaltered.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Adsorption kinetics experiment\u003c/h2\u003e \u003cp\u003eTo assess the adsorption performance of the MMINPs sensor, kinetic experiments were conducted. 5 mg of MMINPs or MNINPs were added to 15 mg/L of SM2 solution and placed on a shaker at 100 rpm for full incubation, and the concentration of SM2 in the supernatant was measured every 20 minutes to observe the time required to reach adsorption equilibrium. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, with the increase of time at 100 minutes, the adsorption amount of both MMINPs and MNINPs reached the maximum value and leveled off, which means that both of them reached the adsorption equilibrium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to investigate the mass transfer mechanism and rate-limiting step of MMINPs and MNINPs, quasi-primary and quasi-secondary adsorption kinetic models were employed to fit the data. The magnitude of the R\u003csup\u003e2\u003c/sup\u003e values allowed for the determination of the most suitable model for the materials. The quasi-primary adsorption kinetic model describes the interaction force between the SM2 template molecules and the molecularly imprinted layer, as well as the adsorption rate with time. The arrival of the solute from the solution to the sensor surface is controlled by a diffusion step. Furthermore, there is only one binding site on the surface of the adsorbent, which is often suitable for describing the kinetic process at the initial stage of adsorption. The quasi-secondary model postulates that the adsorption rate is under the control of chemisorption and not the substance transport step. Furthermore, it assumes that SM2 first forms a monolayer on the sensor surface and then diffuses into the solid interior. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the R\u003csup\u003e2\u003c/sup\u003e of MMINPs is greater than 0.996, which is more consistent with the quasi-secondary kinetic equation. Meanwhile, the discrepancy between the experimental maximum adsorption (3.84 mg/g) and the calculated theoretical value (3.84 mg/g) is not substantial, indicating that the prepared materials align more closely with the quasi-secondary adsorption kinetic model and that chemical bond adsorption is the primary type of adsorption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters for fitting kinetic equations for pseudo first order and pseudo second order adsorption of MMINPs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePseudo-first-order\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u003csub\u003ee\u003c/sub\u003e(exp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003csub\u003ee\u003c/sub\u003e(cal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ\u003csub\u003ee\u003c/sub\u003e(cal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(g/(mg\u0026middot;min))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMINPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFig.s 15 and 16 illustrate the intra-particle diffusion model and Elovich model fitting of MMINPs, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the fitted parameters of the intra-particle diffusion model and Elovich equation. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e, both sets of fits to the intra-particle diffusion model do not pass through the coordinate origin, indicating that the internal diffusion process is not the sole step in controlling the adsorption of SM2 by the sensor. The adsorption of SM2 by the MMINPs can be roughly divided into two stages: a fast adsorption stage and a slow adsorption stage. At the initial stage of adsorption, a substantial number of SM2 molecularly imprinted cavities are present on the surface of MMINPs, exhibiting a high degree of site-specific binding affinity. In the late stage of adsorption, with the decrease of sites, SM2 was affected by mass transfer to diffuse inside the sensor, K\u003csub\u003eFirst\u003c/sub\u003e \u0026gt; K\u003csub\u003eSecond\u003c/sub\u003e, indicating that the adsorption sites were gradually occupied by SM2, and the adsorption rate was slowed down and finally reached equilibrium.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdsorption kinetic constants for Elovich and intraparticle diffusion equation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eElovich equation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eIntra-particle diffusion equation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eK\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(mg/(g\u0026middot;min))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(g/mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e(mg/(g\u0026middot;min\u003csup\u003e1/2\u003c/sup\u003e))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFirst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMINPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.12 Equilibrium binding experiments\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e, the isotherms of MMINPs and MNINPs binding SM2 were presented, and the adsorption equilibrium of MMINPs and MNINPs was gradually achieved with the increase of the initial concentration of SM2. Furthermore, the adsorption of SM2 by MMINPs was found to be significantly higher than that of MNINPs, indicating that the prepared materials have excellent adsorption properties. The maximum adsorption amount of MMINPs was determined to be 3.86 mg/g with an imprint factor of 1.90.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e illustrates the equilibrium binding experimental fits of MMINPs and MNINPs to SM2. The experimental data were fitted by Langmuir and Freundlich isothermal models, and the fitting parameters are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All the R\u003csup\u003e2\u003c/sup\u003e values in the MMINPs group were larger than those in the MNINPs group. The R\u003csup\u003e2\u003c/sup\u003e value (0.992) of the data fitted by the Freundlich equation was slightly larger, which suggests that the adsorption of SM2 by the MMINPs was both monolayers and multilayers and preferred multilayer adsorption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFitting parameters for the Langmuir and Freundlich equations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT(℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003csub\u003em\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003csub\u003eL\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(L/mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eK\u003csub\u003eF\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(mg/g(L/mg)\u003csup\u003e1/n\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMINPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNINPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.13 Scatchard equation analysis of MMINPs\u003c/h2\u003e \u003cp\u003eScatchard plot analysis was used to assess the heterogeneity of the binding sites of the prepared materials. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e19\u003c/span\u003e, the Scatchard plot of MMINPs consists of two linear sections with different slopes, which indicates the presence of heterogeneous binding sites. The linear regression equation for the left part of the curve was Q/C\u003csub\u003ee\u003c/sub\u003e = -6.315x\u0026thinsp;+\u0026thinsp;24.5275 with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.981. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the fitted parameters of the Scatchard equation. The K\u003csub\u003ed\u003c/sub\u003e and Q\u003csub\u003emax\u003c/sub\u003e of the dried MMINPs were calculated to be 0.16 mg/L and 3.79 mg/g. The linear regression equation for the right-hand portion of the curve was Q/C\u003csub\u003ee\u003c/sub\u003e = -2.5867x\u0026thinsp;+\u0026thinsp;10.5544, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.997. The K\u003csub\u003ed\u003c/sub\u003e and Qmax of the dried polymers were calculated to be 0.39 mg/L and 4.07 mg/g. As can be seen from the figure, the fitting result is two intersecting straight lines, indicating the existence of two different types of binding sites in the MMIMPs; the left fitting equation corresponds to the adsorption process in the low concentration range, and the right equation corresponds to the fitting of the adsorption process in the high concentration. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that K\u003csub\u003ed high affinity capacity\u003c/sub\u003e \u0026lt; K\u003csub\u003ed low affinity capacity\u003c/sub\u003e, the binding site expressed by the left side equation has higher affinity, and its adsorption capacity is approximately 2.44 times higher than that of the low-affinity binding site on the right side, and the maximum adsorption capacity of the high affinity binding site is 4.07 mg/g. Therefore, the specific binding of SM2 on the surface of MMIMPs is the main adsorption process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScatchard equation fitting parameters for MMINPs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters of the Scatchard fitting curve\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh affinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow affinity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csub\u003ed\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ\u003csub\u003emax\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.14 Selectivity of MMINPs\u003c/h2\u003e \u003cp\u003eIn order to investigate the specificity of the prepared MMIMPs and MNINPs for SM2 adsorption, a series of mixed solutions were prepared to contain varying concentrations of SM2, SD and SIZ (50, 100, 150, 200 and 250 mg/L). These solutions were used for adsorption experiments. Twenty milligrams of MMIMPs and MNINPs were added to two milliliters of the aforementioned solutions and incubated for a period of time. The MMIMPs and MNINPs were then recovered with magnets, and the adsorption of the materials to SM2, SD, and SIZ was calculated by HPLC. Figure\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e20\u003c/span\u003e illustrates that the adsorption amount of MMIMPs on SM2 is considerably greater than that of SD and SIZ. Additionally, the adsorption amount of MMIMPs on SM2 is considerably greater than that of MNINPs. This indicates that the material exhibits excellent selectivity for SM2. As illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the adsorption amount of MMINPs on SM2 was 1.66 times that of SD and 1.93 times that of SIZ. There was no significant difference in the adsorption effect of MNINPs on SM2 and its structural analogues, indicating that they do not possess the ability to selectively recognize template molecules. This further demonstrates the role of MMINPs in the selective recognition of SM2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelective Parameters of Sensors for SM2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAdsorption capacity\u003c/p\u003e \u003cp\u003e(Q, mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eK\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eK'\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: K\u003csub\u003ed\u003c/sub\u003e is the partition coefficient, K\u003csub\u003ed\u003c/sub\u003e = Q/C\u003csub\u003ef\u003c/sub\u003e, C\u003csub\u003ef\u003c/sub\u003e is the final analyte concentration; K is the selectivity coefficient, K\u0026thinsp;=\u0026thinsp;K\u003csub\u003ed\u003c/sub\u003e (SM2)/K\u003csub\u003ed\u003c/sub\u003e(analyte); K' is the relative selectivity coefficient, K' = K\u003csub\u003eMMINPs\u003c/sub\u003e/K\u003csub\u003eMNINPs\u003c/sub\u003e; IF\u0026thinsp;=\u0026thinsp;Q\u003csub\u003eMMINPs\u003c/sub\u003e/ Q\u003csub\u003eMNINPs\u003c/sub\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.15 Characterization of color change properties of MMINPs\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e21\u003c/span\u003e presents a set of fiber optic spectra obtained from the experiment. MMINPs were employed to adsorb a series of SM2 solutions at a range of concentration gradients, with a fixed magnetic field applied via a magnet. As the concentration of SM2 solutions increased, the reflection peaks gradually redshifted, resulting in a structural color change from green to red. The maximum absorption peak before the adsorption of the MMINPs was located at 470 nm, and its maximum reflection peak shifted by 40 nm as the concentration increased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe differences in diffraction wavelength shifts brought about by the adsorption of SM2 solutions with concentrations of 10\u003csup\u003e2\u003c/sup\u003e ng/L to 1 mg/L by MMINPs and MNINPs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e22\u003c/span\u003e. The wavelength shifts of MMINPs and MNINPs were 40 nm and 16 nm, respectively. The wavelength shifts of MMIMPs were significantly larger than those of MNINPs, which confirms that the MMIMPs can selectively adsorb SM2 and that the synthesis of MMIMPs was successful. Additionally, the results indicate that MNINPs exhibit some degree of physical adsorption on SM2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.16 Establishment of standard curves\u003c/h2\u003e \u003cp\u003eFive sets of SM2 standards with exponentially increasing concentrations were analysed using MMINPs. Figure\u0026nbsp;\u003cspan refid=\"Fig23\" class=\"InternalRef\"\u003e23\u003c/span\u003e shows a clear linear relationship. In the equation Δλ\u0026thinsp;=\u0026thinsp;8.4272 lg (C)\u0026thinsp;+\u0026thinsp;14.6509, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.995 and C is the concentration of the SM2 solution. The linear range was from 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026micro;g/L to 1 mg/L with a detection limit of 2.75 \u0026micro;g/L (3σ/k, n\u0026thinsp;=\u0026thinsp;9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e3.17 Reusability of sensors\u003c/h2\u003e \u003cp\u003eThe MMINPs were dispersed in SM2 solution at a concentration of 1 mg/L for the purpose of conducting reusability experiments. Once equilibrium was reached, a fiber-optic spectrometer was employed to detect any changes in diffraction wavelength. Subsequently, the SM2 molecules were thoroughly eluted with an elution solution, and the spectral detection was repeated. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig24\" class=\"InternalRef\"\u003e24\u003c/span\u003e, repeated cycles of adsorption and elution result in minor fluctuations in the maximum and minimum wavelengths detected by the sensor. However, after five adsorption-elution cycles, there was no discernible change in the wavelength range covered by the spectral detection of the MMIMPs, indicating that the MMINPs are highly reusable and can be utilized continuously for at least five cycles with an RSD of \u0026lt;\u0026thinsp;11% change in sensor detection range.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e3.18 Application to real samples\u003c/h2\u003e \u003cp\u003eTo assess the usefulness of MMINPs, they were utilized in experiments to test laboratory tap water, milk, and chicken samples procured from supermarkets in the vicinity of the educational institution. As illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, recovery experiments were conducted by analyzing SM2-spiked samples at four concentrations (5, 10, 15, and 20 \u0026micro;g/L). The recoveries of the sensor for water samples exhibited a range of 86.60\u0026ndash;102.68%, with an RSD of \u0026lt;\u0026thinsp;8.30%. For milk samples, the recoveries were found to be in the range of 72.68\u0026ndash;80.73%, with an RSD of \u0026lt;\u0026thinsp;6.34%. For chicken samples, the recoveries were found to be in the range of 69.97\u0026ndash;77.00%, with an RSD of \u0026lt;\u0026thinsp;8.73%. The development of this sensor offers insights into the potential for rapid on-site optical detection of veterinary drug residues.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTesting of actual samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5 \u0026micro;g/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10 \u0026micro;g/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e15 \u0026micro;g/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e20 \u0026micro;g/L\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecovery (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRSD(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecovery (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRSD(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRecovery (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRSD(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRecovery (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRSD(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e102.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e3.19 Comparison with other analytical methods\u003c/h2\u003e \u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, while the present assay does not exhibit superior sensitivity compared to other rapid detection methods, its linear range is more expansive and more widely applicable. Concurrently, the visualization detection and preparation of MMINPs offer a novel approach for the expeditious on-site screening of SM2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between SM2 molecularly imprinted photonic crystal sensors and other SM2 detection methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethods of analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinear range (ng/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003cp\u003e(ng/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecovery rate(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluorescent aptamer sensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater, soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25ཞ40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.40\u0026thinsp;~\u0026thinsp;108.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003e[17]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnzyme-linked aptamer colourimetric sensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eཞ10\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.50\u0026thinsp;~\u0026thinsp;92.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003e[15]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnzyme-linked immunosorbent sensor for metal-organic skeletons (MOFs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e2\u003c/sup\u003eཞ5\u0026times;10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.30\u0026thinsp;~\u0026thinsp;90.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003e[16]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrochemical Immunosensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45ཞ43.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.02\u0026thinsp;~\u0026thinsp;118.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003e[18]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnetic molecularly imprinted photonic crystal sensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater,Milk,chicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eཞ10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.68\u0026thinsp;~\u0026thinsp;102.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis work\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this experiment, MMINPs were synthesised for the detection of SM2.The sensor is superparamagnetic and can be enriched in samples and then recovered by magnets and detected by a fibre-optic spectrometer, which reduces the detection time. The logarithm of the SM2 concentration showed a good linear relationship with the wavelength change in the range of 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026micro;g/L\u0026thinsp;~\u0026thinsp;1 mg/L. The LOD value was 2.75 \u0026micro;g/L (3σ/k, n\u0026thinsp;=\u0026thinsp;9). The recoveries in water, milk and chicken samples ranged from 69.97\u0026ndash;102.68% with RSD\u0026thinsp;\u0026lt;\u0026thinsp;8.73%. The sensor can be reused at least five times and can achieve a certain level of visual detection, providing ideas for rapid on-site screening of veterinary drugs in complex food matrices\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key R\u0026amp;D Plan of Shandong Province (Major Science and Technology Innovation Project), China (grant number 2023CXGC010712), and the Shandong Provincial Natural Science Foundation, China (grant number ZR2021MC187).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigation: Xin Wang; Formal analysis and Methodology: Xin Wang and Xiaolei Zhao; Resources: Xin Wang, Huihui Hao; Writing-original draft preparation: Yitong Yin; Writing-review and editing: Jinxing He; Funding acquisition: Jinxing He; Project administration: Jinxing He; Supervision: Jinxing He.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethics requirements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexander C, Andersson HS, Andersson LI, Ansell RJ, Kirsch N, Nicholls IA, O\u0026apos;Mahony J, Whitcombe MJ. (2006). Molecular imprinting science and technology: a survey of the literature for the years up to and including 2003. Journal of Molecular Recognition: An Interdisciplinary Journal. 19(2): 106-180. https://doi.org/10.1002/jmr.2347.\u003c/li\u003e\n\u003cli\u003eBasak S, Venkatram R, Singhal RS. (2022). Recent advances in the application of molecularly imprinted polymers (MIPs) in food analysis. Food Control. 139: 109074. https://doi.org/10.1016/j.foodcont.2022.109074.\u003c/li\u003e\n\u003cli\u003eBurkin MA, Lapa GB, Galvidis IA, Burkin Konstantin M, Zubkov AV, Eremin SA. (2018). Three steps improving the sensitivity of sulfonamide immunodetection in milk. Analytical Methods. 10(48): 5773-5782. https://doi.org/10.1039/C8AY01904E.\u003c/li\u003e\n\u003cli\u003eCai D, Ren L, Zhao H, Xu C, Zhang L, Yu Y, Wang H, Lan Y, Roberts MF, Chuang JH. (2010). A molecular-imprint nanosensor for ultrasensitive detection of proteins. Nature nanotechnology. 5(8): 597-601. https://doi.org/10.1038/nnano.2010.114.\u003c/li\u003e\n\u003cli\u003eChatzimitakos T, Stalikas C. (2020). Zinc ferrite as a magnetic sorbent for the dispersive micro solid-phase extraction of sulfonamides and their determination by HPLC. Microchemical Journal. 155: 104670. https://doi.org/10.1016/j.microc.2020.104670.\u003c/li\u003e\n\u003cli\u003eChen L, Xu S, Li J. (2011). Recent advances in molecular imprinting technology: current status, challenges and highlighted applications. Chemical Society Reviews. 40(5): 2922-2942. https://doi.org/10.1039/C0CS00084A.\u003c/li\u003e\n\u003cli\u003eChristoph, Fenzl, Thomas, Hirsch, Otto, S., Wolfbeis. (2014). Photonic Crystals for Chemical Sensing and Biosensing. Angewandte Chemie International Edition. 53(13): 3318-3335. https://doi.org/10.1002/anie.201307828.\u003c/li\u003e\n\u003cli\u003eDai Z, Gu Z, Yang Y, Yao L, Li M, Pu Y, Ying Y, Hong X. (2023). A novel sulfhydryl-modified superparamagnetic photonic crystal sensing material for the simultaneous heavy metal ions detection and adsorption treatment. Materials Today Chemistry. 34: 101824. https://doi.org/10.1016/j.mtchem.2023.101824.\u003c/li\u003e\n\u003cli\u003ede la Pe\u0026ntilde;a AM, Diez NM, Garc\u0026iacute;a MM, Gil DB, Ca\u0026ntilde;ada-Ca\u0026ntilde;ada F. (2007). A chemometric sensor for determining sulphaguanidine residues in honey samples. Talanta. 73(2): 304-313. https://doi.org/10.1016/j.talanta.2007.03.047.\u003c/li\u003e\n\u003cli\u003eDmitrienko SG, Kochuk EV, Tolmacheva VV, Apyari VV, Zolotov YA. (2015). Determination of the total content of some sulfonamides in milk using solid-phase extraction coupled with off-line derivatization and spectrophotometric detection. Food Chemistry. 188: 51-56. https://doi.org/10.1016/j.foodchem.2015.04.123.\u003c/li\u003e\n\u003cli\u003eEli, Yablonovitch. (1987). Inhibited Spontaneous Emission in Solid-State Physics and Electronics. Physical Review Letters. https://doi.org/10.1103/PhysRevLett.58.2059.\u003c/li\u003e\n\u003cli\u003eFranco DA, Webb J, Taylor CE. (1990). Antibiotic and sulfonamide residues in meat: Implications for human health. Journal of food protection. 53(2): 178-185. https://doi.org/10.4315/0362-028X-53.2.178.\u003c/li\u003e\n\u003cli\u003eFuhrmann A, Gans O, Weiss S, Haberhauer G, Gerzabek MH. (2014). Determination of Bentazone, Chloridazon and Terbuthylazine and Some of Their Metabolites in Complex Environmental Matrices by Liquid Chromatography\u0026ndash;Electrospray Ionization\u0026ndash;Tandem Mass Spectrometry Using a Modified QuEChERS Method: an Optimization and Validation Study. Water, Air, \u0026amp; Soil Pollution. 225(5): 1-15. https://doi.org/10.1007/s11270-014-1944-7.\u003c/li\u003e\n\u003cli\u003eGe J, Yin Y. (2008). Magnetically responsive colloidal photonic crystals. Journal of Materials Chemistry. 18(42): 5041-5045. https://doi.org/10.1039/B809958H.\u003c/li\u003e\n\u003cli\u003eHu T, Xu J, Shang M, Zhao Q, Cao Y. (2022). Photonic crystal sensor for melamine based on magnetic molecularly imprinted nanoparticles self-assembled with an amphiphilic random copolymer. Microchimica Acta. 189(6): 215. https://doi.org/10.1007/s00604-022-05300-x.\u003c/li\u003e\n\u003cli\u003eHu Y, He L, Yin Y. (2011). Magnetically responsive photonic nanochains. Angewandte Chemie. 123(16): 3831-3834. https://doi.org/10.1002/anie.201100290.\u003c/li\u003e\n\u003cli\u003eHunt CE, Pasetto P, Ansell RJ, Haupt K. (2006). A fluorescence polarisation molecular imprint sorbent assay for 2, 4-D: A non-separation pseudo-immunoassay. Chemical Communications. (16): 1754-1756. https://doi.org/10.1039/B516194K.\u003c/li\u003e\n\u003cli\u003eJohn S. (1987). Strong Localization of Photons in Certain Disordered Dielectric Super Lattices. Physical Review Letters. 58(23): 2486-2489. https://doi.org/10.1103/PhysRevLett.58.2486.\u003c/li\u003e\n\u003cli\u003eKim C, Ryu HD, Chung EG, Kim Y, Lee JK. (2018). A review of analytical procedures for the simultaneous determination of medically important veterinary antibiotics in environmental water: Sample preparation, liquid chromatography, and mass spectrometry. Journal of Environmental Management. 217(JUL.1): 629-645. https://doi.org/10.1016/j.jenvman.2018.04.006.\u003c/li\u003e\n\u003cli\u003eLe T, Sun Q, Xie Y, Shu L, Liu J, Xu J, Xiong J, Cao X. (2018). A highly sensitive aptasensor for sulfamethazine detection using an enzyme-linked aptamer assay. Food analytical methods. 11: 2778-2787. https://doi.org/10.1007/s12161-018-1258-2.\u003c/li\u003e\n\u003cli\u003eLi L, Lin Z, Huang Z, Peng A. (2018). Rapid detection of sulfaguanidine in fish by using a photonic crystal molecularly imprinted polymer. Food Chemistry. https://doi.org/10.1016/j.foodchem.2018.12.073.\u003c/li\u003e\n\u003cli\u003eLiu Y, Luo W, Fan Q, Ma H, Yin Y, Long Y, Guan J. (2023). Polyphenol‐Mediated Synthesis of Superparamagnetic Magnetite Nanoclusters for Highly Stable Magnetically Responsive Photonic Crystals. Advanced Functional Materials. 2303470. https://doi.org/10.1002/adfm.202303470.\u003c/li\u003e\n\u003cli\u003eLuo W, Ma H, Mou F, Zhu M, Yan J, Guan J. (2014). Steric‐Repulsion‐Based Magnetically Responsive Photonic Crystals. Advanced Materials. 26(7): 1058-1064. https://doi.org/10.1002/adma.201304134.\u003c/li\u003e\n\u003cli\u003ePaoletti F, Sdogati S, Barola C, Giusepponi D, Moretti S, Galarini R. (2022). Two-procedure approach for multiclass determination of 64 antibiotics in honey using liquid chromatography coupled to time-of-flight mass spectrometry. Food Control. (136-): 136. https://doi.org/10.1016/j.foodcont.2022.108893.\u003c/li\u003e\n\u003cli\u003eRamstroem O, Andersson LI, Mosbach K. (1993). Recognition sites incorporating both pyridinyl and carboxy functionalities prepared by molecular imprinting. The Journal of Organic Chemistry. 58(26): 7562-7564. https://doi.org/10.1021/jo00078a041.\u003c/li\u003e\n\u003cli\u003eSpielmeyer A, Ahlborn J, Hamscher G. (2014). Simultaneous determination of 14 sulfonamides and tetracyclines in biogas plants by liquid-liquid-extraction and liquid chromatography tandem mass spectrometry. Analytical and Bioanalytical Chemistry. 406(11): 2513-2524. https://doi.org/10.1007/s00216-014-7649-3.\u003c/li\u003e\n\u003cli\u003eVallano PT, Remcho VT. (2000). Highly selective separations by capillary electrochromatography: molecular imprint polymer sorbents. Journal of Chromatography A. 887(1-2): 125-135. https://doi.org/10.1016/S0021-9673(99)01199-1.\u003c/li\u003e\n\u003cli\u003eVilla CC, S\u0026aacute;nchez LT, Valencia GA, Ahmed S, Guti\u0026eacute;rrez TJ. (2021). Molecularly imprinted polymers for food applications: A review. Trends in Food Science \u0026amp; Technology. 111: 642-669. https://doi.org/10.1016/j.tifs.2021.03.003.\u003c/li\u003e\n\u003cli\u003eWang H, Chen Q-W, Yu Y-F, Cheng K, Sun Y-B. (2011). Size-and solvent-dependent magnetically responsive optical diffraction of carbon-encapsulated superparamagnetic colloidal photonic crystals. The Journal of Physical Chemistry C. 115(23): 11427-11434. https://doi.org/10.1021/jp201893z.\u003c/li\u003e\n\u003cli\u003eWang J, Zhang Y, Wang S. (2011). Biofnspired Colloidal Photonic Crystals with Controllable Wettability. Accounts of Chemical Research. (6): 44. https://doi.org/10.1021/ar1001236.\u003c/li\u003e\n\u003cli\u003eWang S, Wang Z, Zhang L, Xu Y, Xiong J, Zhang H, He Z, Zheng Y, Jiang H, Shen J. (2022). Adsorption and convenient ELISA detection of sulfamethazine in milk based on MOFs pretreatment. Food Chemistry. 374: 131712. https://doi.org/10.1016/j.foodchem.2021.131712.\u003c/li\u003e\n\u003cli\u003eXu J, Hu T, Zhao Q, Chen X, Cao Y. (2022). Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@ SiO\u003csub\u003e2\u003c/sub\u003e/PAM/Glycerol photonic crystal film as a long-term effective sensor for ambient humidity. Materials Research Bulletin. 153: 111895. https://doi.org/10.1016/j.materresbull.2022.111895.\u003c/li\u003e\n\u003cli\u003eXu J, Shang M, Liu J, Chen X, Cao Y. (2021). Simultaneous self-assembly of molecularly imprinted magnetic nanoparticles to construct a magnetically responsive photonic crystals sensor for bisphenol A. Sensors and Actuators B: Chemical. 338: 129858. https://doi.org/10.1016/j.snb.2021.129858.\u003c/li\u003e\n\u003cli\u003eXu W, Su S, Jiang P, Wang H, Dong X, Zhang M. (2010). Determination of sulfonamides in bovine milk with column-switching high performance liquid chromatography using surface imprinted silica with hydrophilic external layer as restricted access and selective extraction material. Journal of Chromatography A. 1217(46): 7198-7207. https://doi.org/10.1016/j.chroma.2010.09.035.\u003c/li\u003e\n\u003cli\u003eXu X, Friedman G, Humfeld KD, Majetich SA, Asher SA. (2002). Synthesis and utilization of monodisperse superparamagnetic colloidal particles for magnetically controllable photonic crystals. Chemistry of Materials. 14(3): 1249-1256. https://doi.org/10.1021/cm010811h.\u003c/li\u003e\n\u003cli\u003eYablonovitch E. 1995. Photonic band-gap structures. Springer US. https://doi.org/10.1364/JOSAB.10.000283.\u003c/li\u003e\n\u003cli\u003eYang M, Wu X, Hu X, Wang K, Zhang C, Gyimah E, Yakubu S, Zhang Z. (2019). Electrochemical immunosensor based on Ag\u003csup\u003e+\u003c/sup\u003e-dependent CTAB-AuNPs for ultrasensitive detection of sulfamethazine. Biosensors and Bioelectronics. 144: 111643. https://doi.org/10.1016/j.bios.2019.111643.\u003c/li\u003e\n\u003cli\u003eYang Y, Zhang H, Zhou G, Zhang S, Chen J, Deng X, Qu X, Chen Q, Niu B. (2022). Risk Assessment of Veterinary Drug Residues in Pork on the Market in the People\u0026apos;s Republic of China. Journal of food protection. 85(5): 815-827. https://doi.org/10.4315/JFP-21-411.\u003c/li\u003e\n\u003cli\u003eYu H, Tao Y, Chen D, Wang Y, Huang L, Peng D, Dai M, Liu Z, Wang X, Yuan Z. (2011). Development of a high performance liquid chromatography method and a liquid chromatography\u0026ndash;tandem mass spectrometry method with the pressurized liquid extraction for the quantification and confirmation of sulfonamides in the foods of animal origin. Journal of Chromatography B. 879(25): 2653-2662. https://doi.org/10.1016/j.aca.2010.09.032.\u003c/li\u003e\n\u003cli\u003eZhao H, Ding M, Gao Y, Deng W. (2014). Determination of Sulfonamides in Pork, Egg, and Chicken Using Multiwalled Carbon Nanotubes as a Solid-Phase Extraction Sorbent Followed by Ultra-Performance Liquid Chromatography/Tandem Mass Spectrometry. Journal of AOAC International. (97-5). https://doi.org/10.5740/jaoacint.13-133.\u003c/li\u003e\n\u003cli\u003eZhou Q, Peng D, Wang Y, Pan Y, Wan D, Zhang X, Yuan Z. (2014). A novel hapten and monoclonal-based enzyme-linked immunosorbent assay for sulfonamides in edible animal tissues. Food Chemistry. 154: 52-62. https://doi.org/10.1016/j.foodchem.2014.01.016.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Magnetically responsive photonic crystal, Sulfadimethoxine, Surface molecular imprinting, Sensor","lastPublishedDoi":"10.21203/rs.3.rs-6687241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6687241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA core-shell structured magnetic molecularly imprinted nanoparticle (MMINP) was developed, capable of forming a photonic crystal (PC) sensor in the presence of a magnetic field for the detection of sulfamethazine (SM2) residues in food. The preparation conditions were meticulously optimized, and the adsorption performance of MMINPs was comprehensively characterized through Adsorption Kinetics Experiments and Equilibrium Binding Experiments. As the concentration of SM2 varied, the structural colors were systematically characterized under a designated magnetic field strength. The experiments revealed that within the range of SM2 concentrations from 1 ng/L to 1 mg/L, there was a shift in the reflected wavelength from 570 nm to 610 nm, and the color changed from green to red-orange. The sensor achieved minimum detection limit of 2.75 μg/L, maximum adsorption capacity of 3.86 mg/g, imprint factor of 1.90, and was reused for at least five adsorption-resolution cycles. The recoveries ranging from 69.97% to 102.68% and RSD \u0026lt;8.73%.The sensor introduces an innovative approach for the rapid visual colorimetric detection of SM2 in complex food matrices.\u003c/p\u003e","manuscriptTitle":"Enrichment and detection of sulfadimethylpyrimidine in food by magnetic molecularly imprinted photonic crystals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 10:49:23","doi":"10.21203/rs.3.rs-6687241/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-24T08:01:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T14:03:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T18:19:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318423997567587666892084491149161173222","date":"2025-07-01T13:47:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-30T13:48:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290293967952205000989347059008419139249","date":"2025-06-30T12:42:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226553720981494046540601550686875655159","date":"2025-06-20T19:27:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-19T14:52:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T00:57:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-19T00:54:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food Analytical Methods","date":"2025-05-17T13:12:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fc1b070e-d3f2-4dbe-a11f-0b6c3a602534","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-02T09:53:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 10:49:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6687241","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6687241","identity":"rs-6687241","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}