Nano-plasmonic sensing for predicting fouling on a reverse osmosis membrane

Nano-plasmonic sensing for predicting fouling on a reverse osmosis membrane | 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 Article Nano-plasmonic sensing for predicting fouling on a reverse osmosis membrane Noa Stein, Mahaveer Halakarni, Roy Bernstein, Moshe Herzberg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6848061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The reuse of municipal wastewater is crucial to the development of new water resources, especially for agriculture. A challenge to the long-term sustainability of this approach is the presence of organic foulants in the feed water. While purification using a reverse osmosis (RO) membrane can effectively desalinate wastewater effluent to produce potable water, the main drawback is fouling of the membrane by the accumulation of a layer of organic matter from the effluent. Therefore, monitoring the propensity of pre-treated feed water to foul the RO membrane is essential for robust continuous RO operation. The silt density index (SDI), turbidity measurement, and side stream membrane modules have been employed to predict fouling. They generally provide either quick but inaccurate assessments or give accurate assessments at timescales too long to be useful in preventing fouling. This study investigated localized surface plasmon resonance (LSPR) sensing as a novel tool for predicting RO membrane fouling. We compared LSPR with predictions using SDI and a recently suggested quartz crystal microbalance with dissipation technique. The LSPR method showed high-sensitivity detection to model and environmental fouling agents by quantifying real-time foulant adsorption to the sensor surface. Our findings demonstrate that LSPR can surpass traditional methods in predicting fouling propensity, likely owing to its high sensitivity to adsorbed material up to tens of nanometers from the sensor surface. LSPR thus offers a precise method of predicting RO membrane fouling that can potentially enable proactive fouling management, enhancing the longevity of membranes and reducing downtime during their operation. Physical sciences/Engineering Physical sciences/Nanoscience and technology LSPR QCM-D Reverse Osmosis Fouling Wastewater Effluent desalination Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Synopsis Continuous wastewater reverse osmosis desalination ensures sustainable water resources, with fouling prediction via LSPR sensing vital for minimizing downtime and optimizing system efficiency Background Municipal wastewater represents a globally significant water resource, particularly for agricultural applications, and its use can help preserve natural water reservoirs. 1 However, using treated wastewater introduces soluble salts and organic compounds from the effluent into the environment, 2 limiting its long-term sustainability. 2 – 5 The desalination of tertiary effluent using reverse osmosis (RO) membranes can remove ions, micro-pollutants, and residual microbial contaminants. 4 However, membrane fouling remains a significant challenge, particularly for membrane-based technologies applied to effluent desalination, leading to plant downtime, increased energy consumption, frequent cleaning and replacement of the RO membranes, and additional operational costs. 5 – 8 Organic fouling remains the main challenge, 9 – 12 followed by fouling from colloids, scaling (mainly by calcium phosphate), and biofouling. 13 – 15 Organic fouling during the desalination of tertiary effluent results primarily from the adsorption onto the membrane surface of effluent organic matter. This includes natural organic matter, synthetic organic compounds from domestic sources, soluble microbial products produced by microorganisms during biological treatment, and extracellular polymeric substances from microbial biofilms. 16 – 19 Besides causing fouling, the organic matter acts as a “conditioning film” that promotes microbial adhesion and biofilm development on membrane surfaces. 25 – 27 Due to the complexity and high variability of tertiary effluent feed water, RO desalination requires a robust pretreatment system to limit fouling and biofouling, and accordingly, rapid real-time monitoring of the quality of the RO feed water is required. Currently, the most common method for predicting the potential of an effluent to cause fouling during RO relies on the silt density index (SDI). 20 – 22 The SDI assesses membrane clogging based on the potential of a 0.45 µm microfiltration membrane to be fouled by colloids and suspended particles 23 – 26 and therefore, does not accurately predict the fouling of RO membranes. 24 , 27 – 30 Other properties of the foulants and associated hydrodynamics strongly affect the RO membrane fouling process, but are not considered by SDI analysis; these factors include the hydraulic resistance of the fouling layer, the affinity of the foulant to the RO active layer, and transport of the foulant to the membrane due to permeate flux. Other prediction methods include using a side stream with small membrane units or different types of membrane fouling simulators 31 and rapid sensing using printed electrodes for electrochemical impedance spectroscopy integrated into the membrane module. 32 Membrane fouling simulators can provide accurate information by mimicking both the hydraulic and membrane properties, but can be slow to yield information. 33 Electrochemical impedance spectroscopy, on the other hand, is a powerful tool with a proven record in RO membrane fouling detection and can be practically applied using a printed miniaturized electrode. The design and material of the electrodes and optimization of their printing process are critical to the prompt and accurate prediction of RO fouling process. 32 In comparison with these existing techniques, methods based on a quartz crystal microbalance with dissipation (QCM-D) have recently been suggested as more advanced ways to analyze feed water. 34 However, QCM-D analysis does not always correlate a membrane’s actual fouling and reduced performance with the observations for a similar layer adsorbed on the QCM-D sensor. This is because the measured mechanical load on QCM-D sensor that is affected by the amount, shape, conformation, and viscoelastic properties of the foulants adsorbed to the sensor, does not necessarily link to the effects of the accumulated foulant on membrane filtration performance. 34 – 39 This study proposes utilizing localized surface plasmon resonance (LSPR) sensing to predict potential RO membrane fouling and considers various model foulants that are commonly found in the feed water for effluent desalination by RO. LSPR signals are generated by the interactions of incident light with discrete metallic nanostructures, which cause electrons in the metallic conduction band to oscillate, amplifying the electromagnetic field near the nanostructure’s surface. 40 – 42 Near a locally enhanced field, small changes in the local dielectric environment, which can be caused by the adsorption of molecules, are manifested as changes in the nanoparticles’ optical extinction. The wavelength of the extinction peak, at the plasmon resonant frequency, depends on the refractive index of the surrounding medium and is the basis for LSPR sensing. Accordingly, the adsorption interactions of a variety of organic macromolecules, 43 inorganic precipitation, 44 conformational changes of macromolecules, 45 and structural transformations of adsorbates can be detected close to the sensor’s surface. While LSPR has been used mainly in medical research to assess the binding affinity of biomolecules, 46 – 52 its potential use in water technology in general, and membrane-based processes in particular, remains largely untapped. It represents a robust technique for monitoring alterations in the local environment of adsorbed molecules and nanoparticles, making it particularly relevant to monitoring membrane fouling. Using LSPR to explore fouling potential offers benefits in that the adsorbed foulant concentration can be sensed accurately up to tens of nanometers from the surface, providing immediate, real-time, information about the intimate interactions of adsorbed foulants from the feed water with an LSPR sensor that mimics the RO membrane surface. In addition, LSPR monitors only the “dry” material adsorbed to the surface, and it neglects hydration and viscoelastic effects on the detected mass as analyzed by QCM-D. Our previous study explored the relationship between RO fouling by hydrophobic fractions of organic matter in a tertiary effluent of municipal wastewater and LSPR sensing of the dissolved fractions. The insight identified a specific proxy for characterizing foulant accumulation on a membrane and the associated effect of each fraction on the membrane’s performance. 39 This study introduces a novel application: Developing LSPR for RO membrane fouling prediction. We also compare the results with those obtained by QCM-D and SDI measurements. Materials and Methods Model foulants and secondary wastewater effluent Three model RO-membrane foulants were tested for their RO membrane fouling potential and for assessing LSPR as tool for predicting fouling: (i) very-low-viscosity sodium alginate (Sigma-Aldrich, Rehovot, Israel), (ii) humic acid (Sigma-Aldrich, Rehovot, Israel), and (iii) athletes’ protein powder (87% pea protein, maltodextrin, flavorings, silicone dioxide, sucralose; “Vegan One”, Sommer Laboratories Ltd, Rosh Ha’ayin, Israel). Each fouling experiment used 100 mg/L model foulant dissolved in one of three background solutions: 10 mM NaCl at pH 5, 10 mM NaCl at pH 7, and 8.5 mM NaCl + 0.5 mM CaCl 2 (providing a total ionic strength of 10 mM). Solutions were filtered in a 0.22-µm syringe filter (Millipore, PVDF) before each experiment. Fouling by secondary wastewater effluent with a dissolved organic carbon (DOC) concentration of 10.7 ± 0.2 mg/L was also tested. The effluents were collected from the Yeruham wastewater treatment plant, Israel, which employs conventional activated sludge treatment followed by direct coagulation, sand filtration, and hypochlorite disinfection. The effluents were kept in the dark at 4°C until use. The stages before (baseline) and after fouling measurements, with secondary effluents, in both the QCM-D and LSPR experiments used a synthetic background solution with a mineral composition similar to the effluent; this solution was prepared according to the ion composition measured by inductively coupled plasma emission spectroscopy (Table S1 ). QCM-D sensor preparation and fouling experiments The degree of adsorption of the model foulants and organic matter dissolved in secondary wastewater effluent to a polyamide layer was determined by coating gold-titanium-covered piezoelectric sensors (AW sensors, Valencia, Spain) using nylon 6–6. Nylon 6–6 as measured, has a mid-hydrophobic surface and safely mimics a close zeta-potential of -20 – -40 mV as the active layer RO membrane, at pH 7 and ionic strength of ~ 10 mM. 53 , 54 For QCM-D sensor preparation, a titanium-gold-covered piezoelectric sensor (AW sensors, Valencia, Spain) was washed according to the following protocol: (i) immersion in a 2% SDS solution for 30 min followed by (ii) rinsing with double distilled water (DDW), (iii) drying in a jet stream of nitrogen gas (medical grade), and (iv) exposure to UV radiation for 10 min in a UV/ozone cleaner (Pro cleaner plus, Bioforce Nanoscience, USA). The QCM-D sensor was coated with nylon 6–6 as follows: 80 µL 0.5% nylon 6–6 solution in 99% formic acid filtered through a 0.22-µm syringe filter (Millipore, PVDF) was spin-coated on the sensor surface at 2400 rev/s for 60 s with a 40 s acceleration time (WS400-6NPP, Laurell Technologies Corporation, North Wales, PA, USA). Fouling experiments were conducted using a four-channel QCM-D device (Q-Sense Analyzer, Biolin Scientific, Sweden) like our previous work 55 , and the 3rd, 5th, 7th, 9th, 11th, and 13th overtones were recorded. These adsorption experiments involved five steps: (i) DDW injection at a flow rate of 100 µL min − 1 for over 12 h to establish a baseline with a frequency fluctuation, Δ f , below 0.5 Hz·h − 1 ; (ii) background solution injection for 30 min to establish a baseline; (iii) foulant injection at a rate of 100 µl·min − 1 for 180 min, followed by (iv) background solution washing for 30 min and (v) DDW washing for 30 min. The shear modulus, shear viscosity, and hydrated thickness of the adsorbed layer on the QCM-D coated crystal were calculated using Dfind software (Q-Sence, v. 1.2.7.; Biolin Scientific, Sweden) based on the Voigt model. 56 Best-fit values for each parameter were calculated by modeling the frequency and dissipation shifts in each experiment for different overtones (n = 5, 7, 9, and 11). Fouling analysis using LSPR The dry masses of the model foulants and organic matter from the secondary wastewater effluent adsorbed on a polyamide surface were determined by nano-plasmonic sensing (NPS) using an XNano LSPR device (Insplorion AB, Goteborg, Sweden). Uncoated SiO 2 -based sensors (Insplorion AB, Goteborg, Sweden) covered by gold nano-disc plasmons (NPS structures) were silanized with 3-aminopropyl triethoxysilane (APTES) and spin-coated with nylon 6–6. Before coating, residual organic matter was removed from each sensor’s surface by 10 min of sonication with 2-propanol followed by 10 min of sonication with DDW, drying with 99.99% N 2 , and irradiation for 10 min in a UV/ozone chamber (BioFORCE Nanoscience, Ames, IA, USA). The contact angle of a sessile water drop was measured to determine the surface hydrophobicity throughout the procedure (OCA 15, DataPhysics Instruments, Filderstadt, Germany). The sensors were coated as follows: (i) 40 µL 1% APTES solution in HPLC-grade absolute ethanol was uniformly distributed on the sensor surface in a fume hood for 4 h at room temperature; (ii) the sensor was immersed in clean HPLC-grade absolute ethanol for 12 h at room temperature, followed by (iii) contact angle measurement to ensure hydrophobicity was elevated to a corresponding water drop contact angle of ~ 56°; (iv) after an additional wash with HPLC-grade absolute ethanol and drying with 99.99% N 2 , (v) 0.5% nylon 6–6 solution was prepared in 99% formic acid filtered through a 0.22-µm syringe filter (Millipore, PVDF) and (vi) spin-coated (80 µL) on the sensor surface at 2400 rev/s for 60 s with 40 s acceleration time (WS400-6NPP, Laurell Technologies Corporation, North Wales, PA, USA); (vii) the sessile water drop contact angle on the surface was measured again to ensure hydrophobicity was elevated to a corresponding water drop contact angle of ~ 73°. The XNano flow system was operated as follows: (i) DDW injection at a flow rate of 100 µL·min − 1 for 30 min to establish a DDW baseline; (ii) background solution (10 mM NaCl at pH 7, 10 mM NaCl at pH 5, or 8.5 mM NaCl + 0.5 mM CaCl 2 at pH 7) injection for 30 min to establish a background solution baseline before applying the foulants to the system; (iii) injection of model foulants (100 mg/L) and 0.22-µm filtered secondary wastewater effluents through the system at a flow rate of 100 µl·min − 1 for 120 min; and (iv) background solution injection for 30 min followed by DDW injection for 30 min. The dry thickness, d S (nm), of the foulants was estimated from the change in maximum light extinction with respect to wavelength (i.e., the NPS response, Δ λ NPS ). A refractive index ( n s ) of 1.37 was assumed for all foulants, similar to that described previously for an alginate layer, 57 and a value ( n a ) of 1.33 was assumed for the background solution. 58 The sensor decay length parameter, L z (30 nm), was provided by Insplorion AB. The sensor selectivity, S 0 , was measured before each experiment by passing ethylene glycol in different concentrations (5%, 10%, 15%, and 20%) through the system and creating a calibration curve of the maximum wavelength, λ max , of each concentration’s centroid peak, and the refractive index of each concentration tested. The layer thickness, d S (nm), was calculated according to Eq. 1 . $$\:\varDelta\:{\lambda\:}_{NPS}={S}_{0}({n}_{s}-{n}_{a})\bullet\:\left(1-{e}^{\frac{2{d}_{S}}{{L}_{z}}}\right)$$ 1 The dry thickness of foulant ( d S ) was assumed to be on the same order of magnitude as the probe depth ( L Z = 30 nm), according to the size and shape of the Au nanostructures 59 as well as information from Insplorion AB. The relationship between a foulant’s mass surface concentration, Γ S (g·cm − 2 ), and layer thickness, d s , was calculated as follows based on the refractive index increment, dn s / dc (cm 3 g − 1 ), of the foulant (Eq. 2 ). 60 – 63 The dn s / dc value was derived from refractive index values of the foulant measured at known concentrations. Figure S1 provides the dn s / dc values for two foulants, as used to calculate the mass surface concentration, Γ S (g·cm − 2 ), on the LSPR sensors (Eq. 2 ). The values, 0.261 and 0.1392 for humic acid and alginate, respectively, were determined from the linear relation between refractive index and foulant concentration (Figure S1 ) tested in an ATR refractometer (SCHMIDT + HAENSCH GmbH & Co, Germany). However, limitations related to the sensitivity of the refractometer’s measurement of refractive index did not allow the calculation of dn s / dc for athletes’ protein. Also, the high concentration of salt in the secondary effluent precluded a linear correlation between refractive index and the different concentrations of dissolved organic matter in the effluent. Therefore, a value of dn s / dc = 0.2 was assumed for both athletes’ protein powder solution and secondary effluent, as this value is commonly used for proteins and polysaccharides. 60 – 62 $$\:{\varGamma\:}_{S}={d}_{S}\bullet\:\frac{\varDelta\:{\lambda\:}_{NPS}}{{S}_{0}\bullet\:\left(1-{e}^{\frac{2{d}_{S}}{{L}_{z}}}\right)\bullet\:\frac{d{n}_{S}}{dc}}$$ 2 . RO filtration experiments The degree of membrane fouling caused by the model foulants and secondary wastewater effluent was measured in RO crossflow desalination experiments using a laboratory-scale RO flow cell (CF042, Sterlitech) under constant pressure (10 bar) and crossflow velocity (0.079 m·s − 1 ) at 25°C (Figure S2). The degree of fouling was estimated from the reduction in permeate flux. A low-pressure high-flux RO membrane (Hydranautics ESPA1 membrane, Nitto Group) with an effective surface area of 42.09 cm 2 was used. Fouling experiments commenced after compacting each membrane with DDW for 2 h under a constant pressure of 12 bar, followed by stabilization using DDW for 1 h under a constant pressure of 10 bar, and stabilization using background solution for another 1 h. When a permeate flux baseline was established, (i) background solution containing the model foulant or (ii) secondary effluent were desalinated for 2 h. The model foulants were each used at 100 mg/L for all experiments and were filtered through a 0.22-µm syringe filter (Millipore, PVDF) prior to desalination. A 1:10 diluted background solution (based on a measured salt rejection of ~ 90% for this system’s operating conditions) was added to the feed tank during the experiments at a rate similar to the permeate flux, to maintain similar aqueous conditions in the feed to the RO unit. SDI measurement The SDI was measured for the different foulants according to the SDI 5-min test by Lenntech. 63 Briefly, the procedure involves timing the filtration of 500 ml foulant solution through a 0.45 µm pore size microfiltration membrane (diameter, 47 mm) in a dead-end system under constant pressure of 2 bar, first for a clean membrane and again after 5 min of filtration. The SDI value is then calculated as follows: $$\:SDI=\frac{\left(1-\frac{{t}_{i}}{{t}_{f}}\right)\bullet\:100}{T}$$ 3 , where t i is the initial time (s) required to collect a 500 ml sample, t f is the time (s) required to collect a 500 ml sample after 5 min of filtration, and T is the total test time (min). Results and Discussion Fouling of RO membranes by model foulants and secondary wastewater effluent Figure 1 a–d presents the decline of RO permeate flux due to fouling in a crossflow system (Figure S2) of the three model foulants (alginate, humic acid, and athletes’ protein powder) under different aqueous conditions (pH 5 and 7 and with calcium cations) and secondary effluent. Figure 1 e summarizes the total flux decline for each foulant. The results highlight the distinct effect of calcium cations on fouling by alginate and humic acid, which caused flux declines of 8.8% and 5.4%, respectively, in the absence of calcium cations and 38.7% and 35.9%, respectively, in the presence of calcium cations. Changing the pH from 7 to 5 slightly increased the flux decline, although not at the same extent as the addition of calcium cations: the declines at pH 5 were 9.07% and 10.5% for alginate and humic acid, respectively. None of the tested aqueous conditions affected the decline of flux during the treatment of athletes’ protein powder solution (whose main ingredients were 87% pea protein, food stabilizers, anticaking agents, flavorings, and polysaccharides). Interactions between the different components in the athletes’ protein powder might have obscured the effects of calcium addition or reducing the pH, in agreement with our previous study. 64 Also in agreement with prior works is the present observation of calcium cations greatly strengthening the reduction of the flux due to alginate fouling through the formation of a crossed-linked alginate gel layer caused by intermolecular bridging among alginate molecules. 65 , 66 Previous studies have also reported minor variations in the decline of permeate flux in the studied pH range 67 due to the protonation of alginate at pH 5 70 and consequent reduced electrostatic repulsion between humic acid molecules and the membrane surface. 65 , 67 , 71 – 75 Fouling by humic acid showed an increase in the presence of calcium cations 65 , 67 , 71 – 77 due to the interactions of calcium cations with humic acid carboxyl moieties forming a compact fouling layer with elevated hydraulic resistance, 78 , 79 as explored further in this study. Comparison of the decline in RO permeate flux caused by secondary effluent with the declines by each of the model foulant (Fig. 1 a–d) revealed that the secondary effluent had an effect closest to that of the athletes’ protein powder. The effect of secondary effluent on permeate flux was slightly higher than those of alginate or humic acid in the absence of calcium cations (Fig. 1 ). The hydraulic resistance of each of the fouling layers was assessed by comparing the hydraulic resistance of the pristine membrane and the fouled membrane, tested with DDW. The hydraulic resistance of the secondary effluent deposits was slightly lower than those of the other model foulants in the absence of calcium cations (Figure S3). In the presence of calcium cations, the hydraulic resistance of alginate and humic acid increased greatly to the same high level for both. In contrast, calcium cations had little effect on the hydraulic resistance of the layer formed by the athletes’ protein powder. As expected, these results correlate with the results of the RO crossflow fouling experiments, showing similar trends in the presence and absence of calcium cations. The high hydraulic resistance for humic acid and alginate layers in the presence of calcium cations can be explained by cationic bridging between the molecules leading to a compact fouling layer. 66 , 79 – 81 LSPR prediction of RO membrane fouling We tested LSPR and QCM-D as tools for predicting fouling and used them to explore the links between various treatments and the associated fouling mechanisms. Accordingly, we measured the adsorption of foulants onto LSPR and QCM-D membrane-mimetic sensors and compared the results with those obtained from the RO fouling experiments. LSPR sensing quantifies the adsorbed “dry” mass, whereas QCM-D measures the adsorbed hydrated molecular mass on a membrane-mimetic surface under parallel flow conditions. Figure 2 depicts the preparation of a polyamide membrane-mimetic LSPR sensor. As previously described, 39 , 62 silanization of the LSPR sensors with APTES was essential to provide a hydrophobic surface prior to polyamide spin coating (Fig. 2 a). During sensor preparation (Fig. 2 b and c), surface hydrophobicity increased, and the maximum extinction peak shifted. Finally, the sensitivity of the sensor, S 0 , was measured before each set of experiments by exposing the system to four elevated concentrations of ethylene glycol (5%, 10%, 15%, and 20%), and the shifts in the maximum wavelength peak position, λ max , were acquired (Fig. 2 d). S 0 was calculated by linear fitting of λ max versus concentration (inset, Fig. 2 d) and then used to calculate the dry mass accumulated on the LSPR sensor (Eqs. 1 and 2 ). The dry-molecular-mass surface concentrations ( Γ S ) of the model foulants and dissolved organic matter from secondary effluent adsorbed on the polyamide surface were determined by observing the shift in light-extinction maximum, Δ λ , of the LSPR sensor (Figure S4). The dry mass results (calculated using Eqs. 1 and 2 ) provided the accumulation rate and the final adsorbed mass of the foulants (Fig. 3 ). Humic acid and alginate exhibited their highest adsorbed masses in the presence of calcium cations (130.71 ± 4.75 and 129.45 ± 47.7 ng/cm 2 , respectively; Fig. 3 ), mirroring the trend observed in the RO fouling experiments (Fig. 1 ). The athletes’ protein powder demonstrated a relatively high adsorbed mass of 46.07 ± 11.06 ng/cm 2 , irrespective of the aqueous chemical conditions, similar to the RO fouling experiments. Overall, the adsorbed dry masses of the model foulants were consistent with their impact on permeate flux, following similar trends in the presence and absence of calcium cations and varying pHs. The effects of organic fouling by secondary effluent also showed similarly consistent results with the adsorbed dry mass on the LSPR sensor of 75.33 ± 3.08 ng/cm 2 . QCM-D prediction of RO membrane fouling Additional insights into the interactions of the foulants and effluent organic matter with the membrane surface were obtained by studying their adsorption on a polyamide-coated QCM-D sensor (Fig. 4 ). All the foulants and effluent organic matter showed decreases in resonance frequencies for all overtones following their injection (Figs. 4 and S5). Figure 4 shows the effect of the aqueous conditions on the decrease of the frequency shift of the 7th overtone for each of the foulants (indicated by the dashed lines in the figure, denoting changes in the injected solution). Figure S5 shows the frequency and dissipation shifts at different overtones (5th, 7th, 9th, and 11th ) associated with the injection of the model foulants under various aqueous conditions as well as the secondary effluent. Minor reversibility of adsorption was observed in all cases when some foulant was desorbed from the sensor, slightly increasing the frequency shift during injection of the background solution after the adsorption stage (Figure S5). The adsorption rates (Fig. 4 a–d) and final frequency shifts of the adsorption stage (Fig. 4 e) demonstrate no evident effect of calcium cations on the foulants’ associated frequency shifts. The presence of calcium was evident for humic acid and alginate in the RO fouling experiments (Fig. 1 ), and in contrast to the RO fouling results, Fig. 4 a and b indicate that for humic acid and alginate, the presence of calcium cations resulted in the smallest frequency shifts. Corroborating with the RO fouling results, only for humic acid, the decrease in frequency at pH 5 was significantly greater than that at pH 7. The athletes’ protein powder showed a different pattern: Tests at pH 5 and in the presence of calcium cations showed relatively similar frequency decreases, being greater than those for all the other foulants and conditions, whereas the results for pH 7 showed a notably lower frequency decrease. The secondary effluent exhibited similar trends to the least adsorbed foulants in this QCM-D experiment. When comparing the QCM-D results with RO membrane fouling, frequency shift is not an indicator of the adsorbed dry material on the sensor, beyond the effect of foulant transport to the membrane by permeate flux; thus, this accounts for the deviation of frequency shifts from the effects of the foulants on permeate flux. The shifts in frequency and dissipation at different overtones are dependent on the viscous, inertial, and elastic loadings induced by the adhered layer, which, for example, could be affected by the adsorbed layer’s hydration and other intermolecular interactions. 82 – 84 Previous studies have used QCM-D systems to indicate or predict fouling. 34 , 85 , 86 In the present study, as well as in our previous work, 39 the results from QCM-D and RO crossflow filtration experiments did not agree. As already mentioned, QCM-D measurement considers the effect of hydration on the layers’ inertial and viscoelastic loading on the sensor. 87 , 88 Hence, the actual dry adsorbed mass of foulant under different aqueous conditions might be markedly lower than measured total mass and might fluctuate by foulant, despite these foulants possibly having similar effects on the QCM-D resonance frequency. The adsorbed humic acid layer provided lower dissipation versus frequency shifts when measured in the presence of calcium (Fig. 5 a). This result suggests that the presence of calcium cations caused the humic acid adsorbed layer to become compact, displacing water molecules that initially surrounded the foulant molecules, and resulting in a more rigid layer. The adsorbed layers mechanical properties are illustrated by the Kelvin–Voigt -based model, 56 , 89 which was applied to estimate the viscoelastic properties as well as the hydrated mass of the adsorbed fouling layers (Fig. 5 b and c). The shear viscosity, elastic modulus, and hydrated mass of these layers, derived from QCM-D dissipation and frequency-shift measurements, were modeled using a Kelvin–Voigt element comprising a dashpot and spring that respectively represent the viscous and elastic effects of the layer adsorbed on the sensor. The impact of calcium on the viscoelastic properties of the adsorbed layer (Fig. 5 b and c) plausibly accounts for QCM-D not reliably predicting fouling solely by considering the frequency shift, when compared with results for the effect of the foulant on permeate flux. The influence of viscoelastic loading on the frequency shift varies for the different overtones. Strong elastic contact between the attached mass and the sensor surface can even give a positive frequency shift at high overtones. QCM-D can reliably measure the mass of adsorbed material only for rigid contact between the attached mass and the sensor surface that provides mainly inertial loading; in such cases, the amount of adsorbed mass varies linearly with the frequency shift. 37 , 90 – 92 Previous studies have shown that under control conditions, the viscoelastic properties of the fouling layer influence the efficacy of cleaning an RO membrane, while having a limited effect on the fouling process. 93 – 95 The effects of calcium cations on the hydrated mass of the different fouling layers (Fig. 5 b) may explain the frequency shifts acquired under the different conditions. As an example, the elevated frequency shift in the presence of calcium cations for the athletes’ protein powder (Fig. 4 c) agrees with the elevated hydrated mass (Fig. 5 b). However, also for the protein powder’s, QCM-D results were inconsistent with the flux decline, and calcium cations had no effect on permeate flux in the associated RO fouling experiments (Fig. 1 c). Using SDI to predict RO membrane fouling As SDI is commonly used by most desalination plants to predict fouling, 23 , 24 , 96 SDI measurements were conducted here for the model foulants at pH 7, with and without calcium cations, and for the secondary effluent (Fig. 6 ). As expected, the presence of calcium cations increased the SDI for all the model foulants. Notably, the secondary effluent showed the lowest SDI. The effect of calcium cations on the SDI of both the humic acid and alginate solutions and the low SDI value of the secondary effluent somewhat correlates with the RO fouling experiments (Figs. 1 and 6 ). Measuring SDI and turbidity, and using a side stream with a membrane module that desalinates similar feed water to the RO stage are all “golden standard” methods for predicting RO fouling. 24 , 31 , 97 While SDI measurement provides rapid but inaccurate information (organic species and scalants are usually not detected), accurate information about the fouling propensity of the feed water can be provided by a side-stream RO membrane module, although the warnings from this module are commonly too late. Correlating fouling predictions and membrane performance The ability to sense the fouling propensity of the feed water before any reduction of RO membrane performance is critical. Table 1 lists the correlations that could aid prediction of RO membrane fouling (permeate flux decline, fouling layer’s hydraulic resistance) using SDI, QCM-D, and LSPR methodologies (data in Table S2). It highlights the potential inconsistencies between QCM-D and RO membrane fouling experiments when considering the relationships between permeate flux decline or the hydraulic resistance of the fouling layer and either the decrease in the resonance frequency (at the 5th and 7th overtones) or the calculated hydrated mass on the QCM-D sensor. The table also highlights the potential inconsistencies between SDI and RO membrane fouling experiments, considering the relationships between permeate flux decline or fouling layer hydraulic resistance and the SDI results. In contrast to the inconsistent correlations shown by QCM-D and SDI analyses, Pearson t-testing indicated significant and strong correlations between RO fouling indicators (permeate flux and fouling layer hydraulic resistance) and the adsorbed mass assessed by LSPR measurement (Tables 1 and S2). These results confirm that LSPR analysis of the interactions of foulants with a surface-mimicking membrane material (Fig. 2 ) has strong potential as a novel tool for predicting RO membrane fouling. Table 1 Pearson t-test correlations for fouling prediction measures LSPR - Mass surface concentration QCM-D ΔF decrease [7th overtone] QCM-D ΔF decrease [5th overtone] Hydrated mass - QCM-D model SDI RO permeate flux decline 0.947 0.448 0.435 -0.257 0.429 Fouling layer’s hydraulic resistance 0.9 0.517 0.504 -0.331 0.69 Bolded text indicates significant correlation (P < 0.01) Implications This study is the first to investigate the application of nano-plasmonic sensing to membranes in order to use LSPR active surfaces to better predict membrane fouling than current methodology. 39 , 62 , 98 The SDI mainly provides information about the propensity of a porous membrane to clog by particles and colloids, and the results were generally inconsistent with the observed declines in RO permeate flux. Although QCM-D highlights the importance of understanding the hydration, viscoelastic properties, and rigidity of fouling layers under different aqueous conditions, the technique could not predict RO membrane fouling. LSPR techniques are highly sensitive to any contamination adsorbed to a surface, showing exponentially increasing sensitivity as molecular adsorbents come into close proximity with the plasmonic gold nanoparticles. This provides LSPR with a strong ability to predict accurately the propensity of feed water to foul a membrane surface. This information can be used to prevent membrane units from facing large amounts of feed that could reduce their functionality. Hence, such a sensing device is highly desirable for use in RO pretreatment or assessing the future need for membrane cleaning. Future studies should further develop real-time LSPR measurement techniques. LSPR-based sensors can potentially (i) provide immediate information on the pretreatment process and the effects of any sudden change in the feed water (e.g., due to a possible inflow of municipal sewage to the seawater feed or exposure to a high density of jellyfish); (ii) provide a trigger for membrane cleaning before the entire desalination unit is affected; and (iii) provide a predictive analysis to control RO membrane cleaning protocols. The novel approach of applying NPS to membrane technologies has strong potential to aid the development of industrial-scale RO desalination of seawater. Declarations Author Contribution N.S. performed the LSPR and QCM-D adsorption experiment, prepared all the figures and wrote the initial draft of the manuscriptMa.Ha. performed the SDI analysis and provided help in writing the manuscriptR.B. raised funding, provided mentoring, and reviewed the main manuscriptM.H. raised funding, performed analysis, provided mentoring, and reviewed the main manuscriptAll authors reviewed the manuscript References Fito J, Van Hulle SW. Wastewater reclamation and reuse potentials in agriculture: Towards environmental sustainability. Environ Dev Sustainability . 2021;23(3):2949–2972. Schacht K, Chen Y, Tarchitzky J, Lichner L, Marschner B. Impact of treated wastewater irrigation on water repellency of mediterranean soils. Irrig Sci . 2014;32(5):369–378. Katerji N, Van Hoorn JW, Hamdy A, Mastrorilli M. Salinity effect on crop development and yield, analysis of salt tolerance according to several classification methods. 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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-6848061","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471592211,"identity":"e04d1d87-6e8d-4ffc-b49d-ab7559a31567","order_by":0,"name":"Noa Stein","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Noa","middleName":"","lastName":"Stein","suffix":""},{"id":471592213,"identity":"da24807b-ec9c-40c6-8309-8df0dd193071","order_by":1,"name":"Mahaveer Halakarni","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Mahaveer","middleName":"","lastName":"Halakarni","suffix":""},{"id":471592214,"identity":"5fea0b4d-c40d-460a-812e-837ea5c7e496","order_by":2,"name":"Roy Bernstein","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Roy","middleName":"","lastName":"Bernstein","suffix":""},{"id":471592216,"identity":"a68fb793-def0-4356-be58-477be11ad2c5","order_by":3,"name":"Moshe Herzberg","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACAyBmBjH4JaAibOzEapGcAdPCTKwWgxswIUJazNl7H38uqLhnt/l2+zUJhho7Bj5CWix7jhsYzzhTnLztzpkyCYZjyUQ47EYaQzJvW0Ky2Y2cNAkGtgNEaLn/jOEw77+EZOMZIC3/iNFyg42xmbchwc5AIv2YBGMbMVrOpDEzzziWkCBx5wyzRWJfMg9hLcePMX8uqEmw55/d/vDGh292cvLtDQT0QEFiAwOPAUMCAwMPceqBwJ6Bgf0B0apHwSgYBaNgZAEAX1w6/rcqtc8AAAAASUVORK5CYII=","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":true,"prefix":"","firstName":"Moshe","middleName":"","lastName":"Herzberg","suffix":""}],"badges":[],"createdAt":"2025-06-08 14:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6848061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6848061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84783690,"identity":"20345548-c7a8-40e3-ba59-fc38185645c0","added_by":"auto","created_at":"2025-06-17 10:01:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":568757,"visible":true,"origin":"","legend":"\u003cp\u003ePermeate flux decline during RO fouling experiments: \u003cstrong\u003ea)\u003c/strong\u003e humic acid; \u003cstrong\u003eb)\u003c/strong\u003e alginate; \u003cstrong\u003ec)\u003c/strong\u003e athletes’ protein powder, and \u003cstrong\u003ed)\u003c/strong\u003e secondary effluent. The model foulants (at 10\u0026nbsp;mM ionic strength) were examined under three aquatic conditions: 10\u0026nbsp;mM NaCl at pH 5, 10\u0026nbsp;mM NaCl at pH 7, and 8.5\u0026nbsp;mM NaCl + 0.5\u0026nbsp;mM CaCl\u003csub\u003e2\u003c/sub\u003e. The dashed lines indicate changes in solution: first, from background solution (labeled BS) to solutions of the fouling agents, and then back to the background solution. \u003cstrong\u003ee)\u003c/strong\u003e Total RO flux decline for the three model foulants and secondary effluent. Each model foulant’s concentration was 100\u0026nbsp;mg/L (w/v), and the secondary effluent had a DOC concentration of 10.73\u0026nbsp;±\u0026nbsp;0.203\u0026nbsp;mg/L (n\u0026nbsp;=\u0026nbsp;3).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/fa0b383d7fb732e8e43b2d72.png"},{"id":84783691,"identity":"f0874176-7f5a-4b15-8f8d-96979e953c08","added_by":"auto","created_at":"2025-06-17 10:01:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":645687,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment of LSPR membrane-mimetic sensors using uncoated LSPR XNano sensors (Insplorion AB, Gotenberg, Sweden). \u003cstrong\u003ea)\u003c/strong\u003e Scheme for APTES coating (1) and PA coating (2); \u003cstrong\u003eb)\u003c/strong\u003e water drop contact angles after the different coating stages; \u003cstrong\u003ec)\u003c/strong\u003e extinction peak position shift [nm] during the coating stages; and \u003cstrong\u003ed)\u003c/strong\u003e sensitivity (\u003cem\u003eS\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e) analysis using shifts in the maximum wavelength peak position, \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, during exposure to ethylene glycol at four concentrations (5%, 10%, 15% and 20%) versus the corresponding refractive index (inset).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/007fe6135c67c5d32f20b485.png"},{"id":84783693,"identity":"e2991193-46be-4682-b685-9d159c98bf6b","added_by":"auto","created_at":"2025-06-17 10:01:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":521248,"visible":true,"origin":"","legend":"\u003cp\u003eMass surface concentration, Γ\u003csub\u003eS\u003c/sub\u003e [ng/cm\u003csup\u003e2\u003c/sup\u003e], during adsorption to an LSPR membrane-mimetic sensor for over 2\u0026nbsp;h of three model foulants under different conditions with 10\u0026nbsp;mM total ionic strength (10\u0026nbsp;mM NaCl at pH 5, 10\u0026nbsp;mM NaCl at pH 7, and 8.5\u0026nbsp;mM NaCl + 0.5\u0026nbsp;mM CaCl\u003csub\u003e2\u003c/sub\u003e): \u003cstrong\u003ea)\u003c/strong\u003e humic acid, \u003cstrong\u003eb)\u003c/strong\u003e alginate, and \u003cstrong\u003ec)\u003c/strong\u003e athletes’ protein powder. \u003cstrong\u003ed)\u003c/strong\u003e Comparable results for dissolved organic matter from secondary effluent. \u003cstrong\u003ee)\u003c/strong\u003e Maximum mass surface concentration results after adsorption for 2\u0026nbsp;h. The model foulants had a concentration of 100\u0026nbsp;mg/L (w/v), and the secondary effluent had a TOC concentration of 10.73\u0026nbsp;±\u0026nbsp;0.203\u0026nbsp;mg/L.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/c4ee27505f8272dfc0e51b9c.png"},{"id":84783694,"identity":"515afb33-b950-4280-8816-28bc3a1b9a98","added_by":"auto","created_at":"2025-06-17 10:01:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":522811,"visible":true,"origin":"","legend":"\u003cp\u003eAdsorption of model foulants and effluent organic matter on RO membrane-mimetic QCM-D sensors: \u003cstrong\u003ea)\u003c/strong\u003e humic acid, \u003cstrong\u003eb)\u003c/strong\u003e alginate, \u003cstrong\u003ec)\u003c/strong\u003e athletes’ protein powder, and \u003cstrong\u003ed)\u003c/strong\u003e dissolved organic matter from secondary effluent. Adsorption of the three model foulants was examined under different conditions with 10\u0026nbsp;mM total ionic strength (10\u0026nbsp;mM NaCl at pH 5, 10\u0026nbsp;mM NaCl at pH 7, and 8.5\u0026nbsp;mM NaCl + 0.5\u0026nbsp;mM CaCl\u003csub\u003e2\u003c/sub\u003e). The dashed lines indicate changes in solution, from the background solution (BS) to the different foulants and then back to the background solution. \u003cstrong\u003ee)\u003c/strong\u003e Maximum frequency shift for the three model foulants and dissolved organic matter from secondary effluent. The model foulants had a concentration of 100\u0026nbsp;mg/L (w/v), and the secondary effluent had a DOC concentration of 10.73\u0026nbsp;±\u0026nbsp;0.203\u0026nbsp;mg/L.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/7a09839b9c65c4239c77a642.png"},{"id":84783696,"identity":"24b16407-e3db-4fc9-8e35-1a360cdd8bef","added_by":"auto","created_at":"2025-06-17 10:01:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":453062,"visible":true,"origin":"","legend":"\u003cp\u003eViscoelastic properties of layers formed by the model foulants and secondary effluent organic matter on polyamide-coated QCM-D sensors:\u003cstrong\u003e a) \u003c/strong\u003eΔD/ΔF ratios [10\u003csup\u003e−6\u003c/sup\u003e·Hz\u003csup\u003e−1\u003c/sup\u003e], \u003cstrong\u003eb)\u003c/strong\u003e hydrated mass [ng/cm\u003csup\u003e2\u003c/sup\u003e], \u003cstrong\u003ec)\u003c/strong\u003e shear viscosity [µPa·s]. A shear modulus of 100\u0026nbsp;kPa was acquired for all fouling layers.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/07f65d9fbe1aa8732ca88f8b.png"},{"id":84784850,"identity":"a2e4323c-9df4-4298-a73c-cc444773c70b","added_by":"auto","created_at":"2025-06-17 10:17:56","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":89242,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of SDI results for model foulants under different aqueous conditions, each with a total ionic strength of 10\u0026nbsp;mM (10\u0026nbsp;mM NaCl at pH 5, 10\u0026nbsp;mM NaCl at pH 7, and 8.5\u0026nbsp;mM NaCl + 0.5\u0026nbsp;mM CaCl\u003csub\u003e2\u003c/sub\u003e), and secondary effluent.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/9569c1bf628ff998b62ae43e.jpeg"},{"id":86193467,"identity":"ba3467e5-6dfc-4ff8-ac78-d34ede826b55","added_by":"auto","created_at":"2025-07-07 20:17:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3717898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/d7c4b5f6-e5f5-491c-b3bd-44e52df6c985.pdf"},{"id":84784516,"identity":"14244fc4-724a-45b7-bd01-4be5d515644a","added_by":"auto","created_at":"2025-06-17 10:09:56","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":216125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/0fb762a12f9f54e2350a7f29.jpeg"},{"id":84783698,"identity":"7420ee6b-3844-4ab3-8c22-104e2780094f","added_by":"auto","created_at":"2025-06-17 10:01:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1838212,"visible":true,"origin":"","legend":"","description":"","filename":"Steinetal.ROfoulingpredictionwithLSPRSIJune82025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6848061/v1/6a44abe555591b91fa27bcd0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nano-plasmonic sensing for predicting fouling on a reverse osmosis membrane","fulltext":[{"header":"Synopsis","content":"\u003cp\u003eContinuous wastewater reverse osmosis desalination ensures sustainable water resources, with fouling prediction via LSPR sensing vital for minimizing downtime and optimizing system efficiency\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eMunicipal wastewater represents a globally significant water resource, particularly for agricultural applications, and its use can help preserve natural water reservoirs.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e However, using treated wastewater introduces soluble salts and organic compounds from the effluent into the environment,\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e limiting its long-term sustainability.\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The desalination of tertiary effluent using reverse osmosis (RO) membranes can remove ions, micro-pollutants, and residual microbial contaminants.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e However, membrane fouling remains a significant challenge, particularly for membrane-based technologies applied to effluent desalination, leading to plant downtime, increased energy consumption, frequent cleaning and replacement of the RO membranes, and additional operational costs.\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Organic fouling remains the main challenge,\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e followed by fouling from colloids, scaling (mainly by calcium phosphate), and biofouling.\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOrganic fouling during the desalination of tertiary effluent results primarily from the adsorption onto the membrane surface of effluent organic matter. This includes natural organic matter, synthetic organic compounds from domestic sources, soluble microbial products produced by microorganisms during biological treatment, and extracellular polymeric substances from microbial biofilms.\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Besides causing fouling, the organic matter acts as a \u0026ldquo;conditioning film\u0026rdquo; that promotes microbial adhesion and biofilm development on membrane surfaces.\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Due to the complexity and high variability of tertiary effluent feed water, RO desalination requires a robust pretreatment system to limit fouling and biofouling, and accordingly, rapid real-time monitoring of the quality of the RO feed water is required.\u003c/p\u003e \u003cp\u003eCurrently, the most common method for predicting the potential of an effluent to cause fouling during RO relies on the silt density index (SDI).\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The SDI assesses membrane clogging based on the potential of a 0.45 \u0026micro;m microfiltration membrane to be fouled by colloids and suspended particles\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and therefore, does not accurately predict the fouling of RO membranes.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Other properties of the foulants and associated hydrodynamics strongly affect the RO membrane fouling process, but are not considered by SDI analysis; these factors include the hydraulic resistance of the fouling layer, the affinity of the foulant to the RO active layer, and transport of the foulant to the membrane due to permeate flux. Other prediction methods include using a side stream with small membrane units or different types of membrane fouling simulators\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and rapid sensing using printed electrodes for electrochemical impedance spectroscopy integrated into the membrane module.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Membrane fouling simulators can provide accurate information by mimicking both the hydraulic and membrane properties, but can be slow to yield information.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Electrochemical impedance spectroscopy, on the other hand, is a powerful tool with a proven record in RO membrane fouling detection and can be practically applied using a printed miniaturized electrode. The design and material of the electrodes and optimization of their printing process are critical to the prompt and accurate prediction of RO fouling process.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn comparison with these existing techniques, methods based on a quartz crystal microbalance with dissipation (QCM-D) have recently been suggested as more advanced ways to analyze feed water.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e However, QCM-D analysis does not always correlate a membrane\u0026rsquo;s actual fouling and reduced performance with the observations for a similar layer adsorbed on the QCM-D sensor. This is because the measured mechanical load on QCM-D sensor that is affected by the amount, shape, conformation, and viscoelastic properties of the foulants adsorbed to the sensor, does not necessarily link to the effects of the accumulated foulant on membrane filtration performance.\u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36 CR37 CR38\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study proposes utilizing localized surface plasmon resonance (LSPR) sensing to predict potential RO membrane fouling and considers various model foulants that are commonly found in the feed water for effluent desalination by RO. LSPR signals are generated by the interactions of incident light with discrete metallic nanostructures, which cause electrons in the metallic conduction band to oscillate, amplifying the electromagnetic field near the nanostructure\u0026rsquo;s surface.\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Near a locally enhanced field, small changes in the local dielectric environment, which can be caused by the adsorption of molecules, are manifested as changes in the nanoparticles\u0026rsquo; optical extinction. The wavelength of the extinction peak, at the plasmon resonant frequency, depends on the refractive index of the surrounding medium and is the basis for LSPR sensing. Accordingly, the adsorption interactions of a variety of organic macromolecules,\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e inorganic precipitation,\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e conformational changes of macromolecules,\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and structural transformations of adsorbates can be detected close to the sensor\u0026rsquo;s surface.\u003c/p\u003e \u003cp\u003eWhile LSPR has been used mainly in medical research to assess the binding affinity of biomolecules,\u003csup\u003e\u003cspan additionalcitationids=\"CR47 CR48 CR49 CR50 CR51\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e its potential use in water technology in general, and membrane-based processes in particular, remains largely untapped. It represents a robust technique for monitoring alterations in the local environment of adsorbed molecules and nanoparticles, making it particularly relevant to monitoring membrane fouling. Using LSPR to explore fouling potential offers benefits in that the adsorbed foulant concentration can be sensed accurately up to tens of nanometers from the surface, providing immediate, real-time, information about the intimate interactions of adsorbed foulants from the feed water with an LSPR sensor that mimics the RO membrane surface. In addition, LSPR monitors only the \u0026ldquo;dry\u0026rdquo; material adsorbed to the surface, and it neglects hydration and viscoelastic effects on the detected mass as analyzed by QCM-D. Our previous study explored the relationship between RO fouling by hydrophobic fractions of organic matter in a tertiary effluent of municipal wastewater and LSPR sensing of the dissolved fractions. The insight identified a specific proxy for characterizing foulant accumulation on a membrane and the associated effect of each fraction on the membrane\u0026rsquo;s performance.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e This study introduces a novel application: Developing LSPR for RO membrane fouling prediction. We also compare the results with those obtained by QCM-D and SDI measurements.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eModel foulants and secondary wastewater effluent\u003c/h2\u003e \u003cp\u003eThree model RO-membrane foulants were tested for their RO membrane fouling potential and for assessing LSPR as tool for predicting fouling: (i) very-low-viscosity sodium alginate (Sigma-Aldrich, Rehovot, Israel), (ii) humic acid (Sigma-Aldrich, Rehovot, Israel), and (iii) athletes\u0026rsquo; protein powder (87% pea protein, maltodextrin, flavorings, silicone dioxide, sucralose; \u0026ldquo;Vegan One\u0026rdquo;, Sommer Laboratories Ltd, Rosh Ha\u0026rsquo;ayin, Israel). Each fouling experiment used 100 mg/L model foulant dissolved in one of three background solutions: 10 mM NaCl at pH 5, 10 mM NaCl at pH 7, and 8.5 mM NaCl\u0026thinsp;+\u0026thinsp;0.5 mM CaCl\u003csub\u003e2\u003c/sub\u003e (providing a total ionic strength of 10 mM). Solutions were filtered in a 0.22-\u0026micro;m syringe filter (Millipore, PVDF) before each experiment. Fouling by secondary wastewater effluent with a dissolved organic carbon (DOC) concentration of 10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 mg/L was also tested. The effluents were collected from the Yeruham wastewater treatment plant, Israel, which employs conventional activated sludge treatment followed by direct coagulation, sand filtration, and hypochlorite disinfection. The effluents were kept in the dark at 4\u0026deg;C until use. The stages before (baseline) and after fouling measurements, with secondary effluents, in both the QCM-D and LSPR experiments used a synthetic background solution with a mineral composition similar to the effluent; this solution was prepared according to the ion composition measured by inductively coupled plasma emission spectroscopy (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQCM-D sensor preparation and fouling experiments\u003c/h3\u003e\n\u003cp\u003eThe degree of adsorption of the model foulants and organic matter dissolved in secondary wastewater effluent to a polyamide layer was determined by coating gold-titanium-covered piezoelectric sensors (AW sensors, Valencia, Spain) using nylon 6\u0026ndash;6. Nylon 6\u0026ndash;6 as measured, has a mid-hydrophobic surface and safely mimics a close zeta-potential of -20 \u0026ndash; -40 mV as the active layer RO membrane, at pH 7 and ionic strength of ~\u0026thinsp;10 mM.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e For QCM-D sensor preparation, a titanium-gold-covered piezoelectric sensor (AW sensors, Valencia, Spain) was washed according to the following protocol: (i) immersion in a 2% SDS solution for 30 min followed by (ii) rinsing with double distilled water (DDW), (iii) drying in a jet stream of nitrogen gas (medical grade), and (iv) exposure to UV radiation for 10 min in a UV/ozone cleaner (Pro cleaner plus, Bioforce Nanoscience, USA). The QCM-D sensor was coated with nylon 6\u0026ndash;6 as follows: 80 \u0026micro;L 0.5% nylon 6\u0026ndash;6 solution in 99% formic acid filtered through a 0.22-\u0026micro;m syringe filter (Millipore, PVDF) was spin-coated on the sensor surface at 2400 rev/s for 60 s with a 40 s acceleration time (WS400-6NPP, Laurell Technologies Corporation, North Wales, PA, USA).\u003c/p\u003e \u003cp\u003eFouling experiments were conducted using a four-channel QCM-D device (Q-Sense Analyzer, Biolin Scientific, Sweden) like our previous work\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, and the 3rd, 5th, 7th, 9th, 11th, and 13th overtones were recorded. These adsorption experiments involved five steps: (i) DDW injection at a flow rate of 100 \u0026micro;L min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for over 12 h to establish a baseline with a frequency fluctuation, Δ\u003cem\u003ef\u003c/em\u003e, below 0.5 Hz\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; (ii) background solution injection for 30 min to establish a baseline; (iii) foulant injection at a rate of 100 \u0026micro;l\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for 180 min, followed by (iv) background solution washing for 30 min and (v) DDW washing for 30 min. The shear modulus, shear viscosity, and hydrated thickness of the adsorbed layer on the QCM-D coated crystal were calculated using Dfind software (Q-Sence, v. 1.2.7.; Biolin Scientific, Sweden) based on the Voigt model.\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e Best-fit values for each parameter were calculated by modeling the frequency and dissipation shifts in each experiment for different overtones (n\u0026thinsp;=\u0026thinsp;5, 7, 9, and 11).\u003c/p\u003e\n\u003ch3\u003eFouling analysis using LSPR\u003c/h3\u003e\n\u003cp\u003eThe dry masses of the model foulants and organic matter from the secondary wastewater effluent adsorbed on a polyamide surface were determined by nano-plasmonic sensing (NPS) using an XNano LSPR device (Insplorion AB, Goteborg, Sweden). Uncoated SiO\u003csub\u003e2\u003c/sub\u003e-based sensors (Insplorion AB, Goteborg, Sweden) covered by gold nano-disc plasmons (NPS structures) were silanized with 3-aminopropyl triethoxysilane (APTES) and spin-coated with nylon 6\u0026ndash;6. Before coating, residual organic matter was removed from each sensor\u0026rsquo;s surface by 10 min of sonication with 2-propanol followed by 10 min of sonication with DDW, drying with 99.99% N\u003csub\u003e2\u003c/sub\u003e, and irradiation for 10 min in a UV/ozone chamber (BioFORCE Nanoscience, Ames, IA, USA). The contact angle of a sessile water drop was measured to determine the surface hydrophobicity throughout the procedure (OCA 15, DataPhysics Instruments, Filderstadt, Germany). The sensors were coated as follows: (i) 40 \u0026micro;L 1% APTES solution in HPLC-grade absolute ethanol was uniformly distributed on the sensor surface in a \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003efume\u003c/span\u003e hood for 4 h at room temperature; (ii) the sensor was immersed in clean HPLC-grade absolute ethanol for 12 h at room temperature, followed by (iii) contact angle measurement to ensure hydrophobicity was elevated to a corresponding water drop contact angle of ~\u0026thinsp;56\u0026deg;; (iv) after an additional wash with HPLC-grade absolute ethanol and drying with 99.99% N\u003csub\u003e2\u003c/sub\u003e, (v) 0.5% nylon 6\u0026ndash;6 solution was prepared in 99% formic acid filtered through a 0.22-\u0026micro;m syringe filter (Millipore, PVDF) and (vi) spin-coated (80 \u0026micro;L) on the sensor surface at 2400 rev/s for 60 s with 40 s acceleration time (WS400-6NPP, Laurell Technologies Corporation, North Wales, PA, USA); (vii) the sessile water drop contact angle on the surface was measured again to ensure hydrophobicity was elevated to a corresponding water drop contact angle of ~\u0026thinsp;73\u0026deg;.\u003c/p\u003e \u003cp\u003eThe XNano flow system was operated as follows: (i) DDW injection at a flow rate of 100 \u0026micro;L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for 30 min to establish a DDW baseline; (ii) background solution (10 mM NaCl at pH 7, 10 mM NaCl at pH 5, or 8.5 mM NaCl\u0026thinsp;+\u0026thinsp;0.5 mM CaCl\u003csub\u003e2\u003c/sub\u003e at pH 7) injection for 30 min to establish a background solution baseline before applying the foulants to the system; (iii) injection of model foulants (100 mg/L) and 0.22-\u0026micro;m filtered secondary wastewater effluents through the system at a flow rate of 100 \u0026micro;l\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for 120 min; and (iv) background solution injection for 30 min followed by DDW injection for 30 min.\u003c/p\u003e \u003cp\u003eThe dry thickness, \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e (nm), of the foulants was estimated from the change in maximum light extinction with respect to wavelength (i.e., the NPS response, Δ\u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003eNPS\u003c/em\u003e\u003c/sub\u003e). A refractive index (\u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) of 1.37 was assumed for all foulants, similar to that described previously for an alginate layer,\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and a value (\u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e) of 1.33 was assumed for the background solution.\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e The sensor decay length parameter, \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e (30 nm), was provided by Insplorion AB. The sensor selectivity, S\u003csub\u003e0\u003c/sub\u003e, was measured before each experiment by passing ethylene glycol in different concentrations (5%, 10%, 15%, and 20%) through the system and creating a calibration curve of the maximum wavelength, \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, of each concentration\u0026rsquo;s centroid peak, and the refractive index of each concentration tested. The layer thickness, \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e (nm), was calculated according to Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:{\\lambda\\:}_{NPS}={S}_{0}({n}_{s}-{n}_{a})\\bullet\\:\\left(1-{e}^{\\frac{2{d}_{S}}{{L}_{z}}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe dry thickness of foulant (\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e) was assumed to be on the same order of magnitude as the probe depth (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/sub\u003e = 30 nm), according to the size and shape of the Au nanostructures\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e as well as information from Insplorion AB. The relationship between a foulant\u0026rsquo;s mass surface concentration, \u003cem\u003eΓ\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e (g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and layer thickness, \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e, was calculated as follows based on the refractive index increment, \u003cem\u003edn\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003edc\u003c/em\u003e (cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), of the foulant (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003csup\u003e\u003cspan additionalcitationids=\"CR61 CR62\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e The \u003cem\u003edn\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003edc\u003c/em\u003e value was derived from refractive index values of the foulant measured at known concentrations. Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provides the \u003cem\u003edn\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003edc\u003c/em\u003e values for two foulants, as used to calculate the mass surface concentration, \u003cem\u003eΓ\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e (g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), on the LSPR sensors (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The values, 0.261 and 0.1392 for humic acid and alginate, respectively, were determined from the linear relation between refractive index and foulant concentration (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) tested in an ATR refractometer (SCHMIDT\u0026thinsp;+\u0026thinsp;HAENSCH GmbH \u0026amp; Co, Germany). However, limitations related to the sensitivity of the refractometer\u0026rsquo;s measurement of refractive index did not allow the calculation of \u003cem\u003edn\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003edc\u003c/em\u003e for athletes\u0026rsquo; protein. Also, the high concentration of salt in the secondary effluent precluded a linear correlation between refractive index and the different concentrations of dissolved organic matter in the effluent. Therefore, a value of \u003cem\u003edn\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003edc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2 was assumed for both athletes\u0026rsquo; protein powder solution and secondary effluent, as this value is commonly used for proteins and polysaccharides.\u003csup\u003e\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\varGamma\\:}_{S}={d}_{S}\\bullet\\:\\frac{\\varDelta\\:{\\lambda\\:}_{NPS}}{{S}_{0}\\bullet\\:\\left(1-{e}^{\\frac{2{d}_{S}}{{L}_{z}}}\\right)\\bullet\\:\\frac{d{n}_{S}}{dc}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e\n\u003ch3\u003eRO filtration experiments\u003c/h3\u003e\n\u003cp\u003eThe degree of membrane fouling caused by the model foulants and secondary wastewater effluent was measured in RO crossflow desalination experiments using a laboratory-scale RO flow cell (CF042, Sterlitech) under constant pressure (10 bar) and crossflow velocity (0.079 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at 25\u0026deg;C (Figure S2). The degree of fouling was estimated from the reduction in permeate flux. A low-pressure high-flux RO membrane (Hydranautics ESPA1 membrane, Nitto Group) with an effective surface area of 42.09 cm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e was used. Fouling experiments commenced after compacting each membrane with DDW for 2 h under a constant pressure of 12 bar, followed by stabilization using DDW for 1 h under a constant pressure of 10 bar, and stabilization using background solution for another 1 h. When a permeate flux baseline was established, (i) background solution containing the model foulant or (ii) secondary effluent were desalinated for 2 h. The model foulants were each used at 100 mg/L for all experiments and were filtered through a 0.22-\u0026micro;m syringe filter (Millipore, PVDF) prior to desalination. A 1:10 diluted background solution (based on a measured salt rejection of ~\u0026thinsp;90% for this system\u0026rsquo;s operating conditions) was added to the feed tank during the experiments at a rate similar to the permeate flux, to maintain similar aqueous conditions in the feed to the RO unit.\u003c/p\u003e\n\u003ch3\u003eSDI measurement\u003c/h3\u003e\n\u003cp\u003eThe SDI was measured for the different foulants according to the SDI 5-min test by Lenntech.\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e Briefly, the procedure involves timing the filtration of 500 ml foulant solution through a 0.45 \u0026micro;m pore size microfiltration membrane (diameter, 47 mm) in a dead-end system under constant pressure of 2 bar, first for a clean membrane and again after 5 min of filtration. The SDI value is then calculated as follows:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:SDI=\\frac{\\left(1-\\frac{{t}_{i}}{{t}_{f}}\\right)\\bullet\\:100}{T}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the initial time (s) required to collect a 500 ml sample, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e is the time (s) required to collect a 500 ml sample after 5 min of filtration, and \u003cem\u003eT\u003c/em\u003e is the total test time (min).\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFouling of RO membranes by model foulants and secondary wastewater effluent\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;d presents the decline of RO permeate flux due to fouling in a crossflow system (Figure S2) of the three model foulants (alginate, humic acid, and athletes\u0026rsquo; protein powder) under different aqueous conditions (pH 5 and 7 and with calcium cations) and secondary effluent. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee summarizes the total flux decline for each foulant. The results highlight the distinct effect of calcium cations on fouling by alginate and humic acid, which caused flux declines of 8.8% and 5.4%, respectively, in the absence of calcium cations and 38.7% and 35.9%, respectively, in the presence of calcium cations. Changing the pH from 7 to 5 slightly increased the flux decline, although not at the same extent as the addition of calcium cations: the declines at pH 5 were 9.07% and 10.5% for alginate and humic acid, respectively. None of the tested aqueous conditions affected the decline of flux during the treatment of athletes\u0026rsquo; protein powder solution (whose main ingredients were 87% pea protein, food stabilizers, anticaking agents, flavorings, and polysaccharides). Interactions between the different components in the athletes\u0026rsquo; protein powder might have obscured the effects of calcium addition or reducing the pH, in agreement with our previous study.\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e Also in agreement with prior works is the present observation of calcium cations greatly strengthening the reduction of the flux due to alginate fouling through the formation of a crossed-linked alginate gel layer caused by intermolecular bridging among alginate molecules.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e Previous studies have also reported minor variations in the decline of permeate flux in the studied pH range \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e due to the protonation of alginate at pH\u0026thinsp;\u0026lt;\u0026thinsp;5.\u003csup\u003e68,69\u003c/sup\u003e Compared with alginate, the greater flux decline with humic acid at low pH is likely attributable to the deprotonation of various carboxylic functional groups occurring at pH\u0026thinsp;\u0026gt;\u0026thinsp;5\u003csup\u003e70\u003c/sup\u003e and consequent reduced electrostatic repulsion between humic acid molecules and the membrane surface.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan additionalcitationids=\"CR72 CR73 CR74\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e Fouling by humic acid showed an increase in the presence of calcium cations\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan additionalcitationids=\"CR72 CR73 CR74 CR75 CR76\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e due to the interactions of calcium cations with humic acid carboxyl moieties forming a compact fouling layer with elevated hydraulic resistance,\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e as explored further in this study.\u003c/p\u003e \u003cp\u003eComparison of the decline in RO permeate flux caused by secondary effluent with the declines by each of the model foulant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;d) revealed that the secondary effluent had an effect closest to that of the athletes\u0026rsquo; protein powder. The effect of secondary effluent on permeate flux was slightly higher than those of alginate or humic acid in the absence of calcium cations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe hydraulic resistance of each of the fouling layers was assessed by comparing the hydraulic resistance of the pristine membrane and the fouled membrane, tested with DDW. The hydraulic resistance of the secondary effluent deposits was slightly lower than those of the other model foulants in the absence of calcium cations (Figure S3). In the presence of calcium cations, the hydraulic resistance of alginate and humic acid increased greatly to the same high level for both. In contrast, calcium cations had little effect on the hydraulic resistance of the layer formed by the athletes\u0026rsquo; protein powder. As expected, these results correlate with the results of the RO crossflow fouling experiments, showing similar trends in the presence and absence of calcium cations. The high hydraulic resistance for humic acid and alginate layers in the presence of calcium cations can be explained by cationic bridging between the molecules leading to a compact fouling layer.\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLSPR prediction of RO membrane fouling\u003c/h3\u003e\n\u003cp\u003eWe tested LSPR and QCM-D as tools for predicting fouling and used them to explore the links between various treatments and the associated fouling mechanisms. Accordingly, we measured the adsorption of foulants onto LSPR and QCM-D membrane-mimetic sensors and compared the results with those obtained from the RO fouling experiments. LSPR sensing quantifies the adsorbed \u0026ldquo;dry\u0026rdquo; mass, whereas QCM-D measures the adsorbed hydrated molecular mass on a membrane-mimetic surface under parallel flow conditions. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the preparation of a polyamide membrane-mimetic LSPR sensor. As previously described,\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e silanization of the LSPR sensors with APTES was essential to provide a hydrophobic surface prior to polyamide spin coating (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). During sensor preparation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and c), surface hydrophobicity increased, and the maximum extinction peak shifted. Finally, the sensitivity of the sensor, \u003cem\u003eS\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, was measured before each set of experiments by exposing the system to four elevated concentrations of ethylene glycol (5%, 10%, 15%, and 20%), and the shifts in the maximum wavelength peak position, \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, were acquired (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). \u003cem\u003eS\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e was calculated by linear fitting of \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e versus concentration (inset, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) and then used to calculate the dry mass accumulated on the LSPR sensor (Eqs.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe dry-molecular-mass surface concentrations (\u003cem\u003eΓ\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e) of the model foulants and dissolved organic matter from secondary effluent adsorbed on the polyamide surface were determined by observing the shift in light-extinction maximum, Δ\u003cem\u003eλ\u003c/em\u003e, of the LSPR sensor (Figure S4). The dry mass results (calculated using Eqs.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) provided the accumulation rate and the final adsorbed mass of the foulants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Humic acid and alginate exhibited their highest adsorbed masses in the presence of calcium cations (130.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75 and 129.45\u0026thinsp;\u0026plusmn;\u0026thinsp;47.7 ng/cm\u003csup\u003e2\u003c/sup\u003e, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), mirroring the trend observed in the RO fouling experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The athletes\u0026rsquo; protein powder demonstrated a relatively high adsorbed mass of 46.07\u0026thinsp;\u0026plusmn;\u0026thinsp;11.06 ng/cm\u003csup\u003e2\u003c/sup\u003e, irrespective of the aqueous chemical conditions, similar to the RO fouling experiments. Overall, the adsorbed dry masses of the model foulants were consistent with their impact on permeate flux, following similar trends in the presence and absence of calcium cations and varying pHs. The effects of organic fouling by secondary effluent also showed similarly consistent results with the adsorbed dry mass on the LSPR sensor of 75.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08 ng/cm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQCM-D prediction of RO membrane fouling\u003c/h2\u003e \u003cp\u003eAdditional insights into the interactions of the foulants and effluent organic matter with the membrane surface were obtained by studying their adsorption on a polyamide-coated QCM-D sensor (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All the foulants and effluent organic matter showed decreases in resonance frequencies for all overtones following their injection (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and S5). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the effect of the aqueous conditions on the decrease of the frequency shift of the 7th overtone for each of the foulants (indicated by the dashed lines in the figure, denoting changes in the injected solution). Figure S5 shows the frequency and dissipation shifts at different overtones (5th, 7th, 9th, and 11th ) associated with the injection of the model foulants under various aqueous conditions as well as the secondary effluent. Minor reversibility of adsorption was observed in all cases when some foulant was desorbed from the sensor, slightly increasing the frequency shift during injection of the background solution after the adsorption stage (Figure S5).\u003c/p\u003e \u003cp\u003eThe adsorption rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;d) and final frequency shifts of the adsorption stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee) demonstrate no evident effect of calcium cations on the foulants\u0026rsquo; associated frequency shifts. The presence of calcium was evident for humic acid and alginate in the RO fouling experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and in contrast to the RO fouling results, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and b indicate that for humic acid and alginate, the presence of calcium cations resulted in the smallest frequency shifts. Corroborating with the RO fouling results, only for humic acid, the decrease in frequency at pH 5 was significantly greater than that at pH 7. The athletes\u0026rsquo; protein powder showed a different pattern: Tests at pH 5 and in the presence of calcium cations showed relatively similar frequency decreases, being greater than those for all the other foulants and conditions, whereas the results for pH 7 showed a notably lower frequency decrease. The secondary effluent exhibited similar trends to the least adsorbed foulants in this QCM-D experiment. When comparing the QCM-D results with RO membrane fouling, frequency shift is not an indicator of the adsorbed dry material on the sensor, beyond the effect of foulant transport to the membrane by permeate flux; thus, this accounts for the deviation of frequency shifts from the effects of the foulants on permeate flux. The shifts in frequency and dissipation at different overtones are dependent on the viscous, inertial, and elastic loadings induced by the adhered layer, which, for example, could be affected by the adsorbed layer\u0026rsquo;s hydration and other intermolecular interactions.\u003csup\u003e\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have used QCM-D systems to indicate or predict fouling.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e In the present study, as well as in our previous work,\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e the results from QCM-D and RO crossflow filtration experiments did not agree. As already mentioned, QCM-D measurement considers the effect of hydration on the layers\u0026rsquo; inertial and viscoelastic loading on the sensor.\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e Hence, the actual dry adsorbed mass of foulant under different aqueous conditions might be markedly lower than measured total mass and might fluctuate by foulant, despite these foulants possibly having similar effects on the QCM-D resonance frequency. The adsorbed humic acid layer provided lower dissipation versus frequency shifts when measured in the presence of calcium (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This result suggests that the presence of calcium cations caused the humic acid adsorbed layer to become compact, displacing water molecules that initially surrounded the foulant molecules, and resulting in a more rigid layer. The adsorbed layers mechanical properties are illustrated by the Kelvin\u0026ndash;Voigt -based model,\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e which was applied to estimate the viscoelastic properties as well as the hydrated mass of the adsorbed fouling layers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and c). The shear viscosity, elastic modulus, and hydrated mass of these layers, derived from QCM-D dissipation and frequency-shift measurements, were modeled using a Kelvin\u0026ndash;Voigt element comprising a dashpot and spring that respectively represent the viscous and elastic effects of the layer adsorbed on the sensor. The impact of calcium on the viscoelastic properties of the adsorbed layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and c) plausibly accounts for QCM-D not reliably predicting fouling solely by considering the frequency shift, when compared with results for the effect of the foulant on permeate flux. The influence of viscoelastic loading on the frequency shift varies for the different overtones. Strong elastic contact between the attached mass and the sensor surface can even give a positive frequency shift at high overtones. QCM-D can reliably measure the mass of adsorbed material only for rigid contact between the attached mass and the sensor surface that provides mainly inertial loading; in such cases, the amount of adsorbed mass varies linearly with the frequency shift.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan additionalcitationids=\"CR91\" citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e Previous studies have shown that under control conditions, the viscoelastic properties of the fouling layer influence the efficacy of cleaning an RO membrane, while having a limited effect on the fouling process.\u003csup\u003e\u003cspan additionalcitationids=\"CR94\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e The effects of calcium cations on the hydrated mass of the different fouling layers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) may explain the frequency shifts acquired under the different conditions. As an example, the elevated frequency shift in the presence of calcium cations for the athletes\u0026rsquo; protein powder (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) agrees with the elevated hydrated mass (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). However, also for the protein powder\u0026rsquo;s, QCM-D results were inconsistent with the flux decline, and calcium cations had no effect on permeate flux in the associated RO fouling experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUsing SDI to predict RO membrane fouling\u003c/h2\u003e \u003cp\u003eAs SDI is commonly used by most desalination plants to predict fouling,\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e SDI measurements were conducted here for the model foulants at pH 7, with and without calcium cations, and for the secondary effluent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). As expected, the presence of calcium cations increased the SDI for all the model foulants. Notably, the secondary effluent showed the lowest SDI. The effect of calcium cations on the SDI of both the humic acid and alginate solutions and the low SDI value of the secondary effluent somewhat correlates with the RO fouling experiments (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Measuring SDI and turbidity, and using a side stream with a membrane module that desalinates similar feed water to the RO stage are all \u0026ldquo;golden standard\u0026rdquo; methods for predicting RO fouling.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e While SDI measurement provides rapid but inaccurate information (organic species and scalants are usually not detected), accurate information about the fouling propensity of the feed water can be provided by a side-stream RO membrane module, although the warnings from this module are commonly too late.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelating fouling predictions and membrane performance\u003c/h2\u003e \u003cp\u003eThe ability to sense the fouling propensity of the feed water before any reduction of RO membrane performance is critical. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the correlations that could aid prediction of RO membrane fouling (permeate flux decline, fouling layer\u0026rsquo;s hydraulic resistance) using SDI, QCM-D, and LSPR methodologies (data in Table S2). It highlights the potential inconsistencies between QCM-D and RO membrane fouling experiments when considering the relationships between permeate flux decline or the hydraulic resistance of the fouling layer and either the decrease in the resonance frequency (at the 5th and 7th overtones) or the calculated hydrated mass on the QCM-D sensor. The table also highlights the potential inconsistencies between SDI and RO membrane fouling experiments, considering the relationships between permeate flux decline or fouling layer hydraulic resistance and the SDI results. In contrast to the inconsistent correlations shown by QCM-D and SDI analyses, Pearson t-testing indicated significant and strong correlations between RO fouling indicators (permeate flux and fouling layer hydraulic resistance) and the adsorbed mass assessed by LSPR measurement (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and S2). These results confirm that LSPR analysis of the interactions of foulants with a surface-mimicking membrane material (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) has strong potential as a novel tool for predicting RO membrane fouling.\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\u003ePearson t-test correlations for fouling prediction measures\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSPR - Mass surface concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQCM-D ΔF decrease [7th overtone]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQCM-D ΔF decrease [5th overtone]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrated mass - QCM-D model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO permeate flux decline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.947\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFouling layer\u0026rsquo;s hydraulic resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBolded text indicates significant correlation (P\u0026nbsp;\u0026lt;\u0026nbsp;0.01)\u003c/b\u003e\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=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eThis study is the first to investigate the application of nano-plasmonic sensing to membranes in order to use LSPR active surfaces to better predict membrane fouling than current methodology.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e The SDI mainly provides information about the propensity of a porous membrane to clog by particles and colloids, and the results were generally inconsistent with the observed declines in RO permeate flux. Although QCM-D highlights the importance of understanding the hydration, viscoelastic properties, and rigidity of fouling layers under different aqueous conditions, the technique could not predict RO membrane fouling. LSPR techniques are highly sensitive to any contamination adsorbed to a surface, showing exponentially increasing sensitivity as molecular adsorbents come into close proximity with the plasmonic gold nanoparticles. This provides LSPR with a strong ability to predict accurately the propensity of feed water to foul a membrane surface. This information can be used to prevent membrane units from facing large amounts of feed that could reduce their functionality. Hence, such a sensing device is highly desirable for use in RO pretreatment or assessing the future need for membrane cleaning. Future studies should further develop real-time LSPR measurement techniques. LSPR-based sensors can potentially (i) provide immediate information on the pretreatment process and the effects of any sudden change in the feed water (e.g., due to a possible inflow of municipal sewage to the seawater feed or exposure to a high density of jellyfish); (ii) provide a trigger for membrane cleaning before the entire desalination unit is affected; and (iii) provide a predictive analysis to control RO membrane cleaning protocols. The novel approach of applying NPS to membrane technologies has strong potential to aid the development of industrial-scale RO desalination of seawater.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.S. performed the LSPR and QCM-D adsorption experiment, prepared all the figures and wrote the initial draft of the manuscriptMa.Ha. performed the SDI analysis and provided help in writing the manuscriptR.B. raised funding, provided mentoring, and reviewed the main manuscriptM.H. raised funding, performed analysis, provided mentoring, and reviewed the main manuscriptAll authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFito J, Van Hulle SW. 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The membrane fouling simulator as a new tool for biofouling control of spiral-wound membranes. \u003cem\u003eDesalination\u003c/em\u003e. 2007;204(1\u0026ndash;3):170\u0026ndash;174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnalyzing the fouling propensity of organic foulants towards reverse osmosis membrane using nylon modified nano-plasmonic sensor.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"LSPR, QCM-D, Reverse Osmosis, Fouling, Wastewater Effluent desalination","lastPublishedDoi":"10.21203/rs.3.rs-6848061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6848061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe reuse of municipal wastewater is crucial to the development of new water resources, especially for agriculture. A challenge to the long-term sustainability of this approach is the presence of organic foulants in the feed water. While purification using a reverse osmosis (RO) membrane can effectively desalinate wastewater effluent to produce potable water, the main drawback is fouling of the membrane by the accumulation of a layer of organic matter from the effluent. Therefore, monitoring the propensity of pre-treated feed water to foul the RO membrane is essential for robust continuous RO operation. The silt density index (SDI), turbidity measurement, and side stream membrane modules have been employed to predict fouling. They generally provide either quick but inaccurate assessments or give accurate assessments at timescales too long to be useful in preventing fouling. This study investigated localized surface plasmon resonance (LSPR) sensing as a novel tool for predicting RO membrane fouling. We compared LSPR with predictions using SDI and a recently suggested quartz crystal microbalance with dissipation technique. The LSPR method showed high-sensitivity detection to model and environmental fouling agents by quantifying real-time foulant adsorption to the sensor surface. Our findings demonstrate that LSPR can surpass traditional methods in predicting fouling propensity, likely owing to its high sensitivity to adsorbed material up to tens of nanometers from the sensor surface. LSPR thus offers a precise method of predicting RO membrane fouling that can potentially enable proactive fouling management, enhancing the longevity of membranes and reducing downtime during their operation.\u003c/p\u003e","manuscriptTitle":"Nano-plasmonic sensing for predicting fouling on a reverse osmosis membrane","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 10:01:51","doi":"10.21203/rs.3.rs-6848061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07183058-5d0b-49bb-920d-369c4f663ec2","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50078008,"name":"Physical sciences/Engineering"},{"id":50078009,"name":"Physical sciences/Nanoscience and technology"}],"tags":[],"updatedAt":"2025-07-07T20:09:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 10:01:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6848061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6848061","identity":"rs-6848061","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}