Sensitive and continuous in situ electrochemical monitoring of multiple neurochemicals using an ultra-soft carbon nanotube fiber sensor

Sensitive and continuous in situ electrochemical monitoring of multiple neurochemicals using an ultra-soft carbon nanotube fiber sensor | 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 Sensitive and continuous in situ electrochemical monitoring of multiple neurochemicals using an ultra-soft carbon nanotube fiber sensor Xi Xie, Xiaotong Li, Mengyi He, Xinshuo Huang, Longwen Yan, Jinkun Chen, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7541492/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Neurochemicals, such as dopamine (DA), ascorbic acid (AA), and uric acid (UA) serve as critical modulators of neural circuit dynamics in both the central and peripheral nervous systems, and their real-time, in situ monitoring is essential for elucidating neural function and facilitating early diagnosis of neurological disorders. However, conventional in vivo mon itoring remains limited by the mechanical mismatch between rigid electrodes and soft neural tissue, as well as the electrochemical detection sensitivity of the electrodes. In this study, we develop a sensitive and multifunctional electrochemical sensing platform based on ultra-soft carbon nanotube fiber electrodes (CNTFEs), which integrate microelectrode with programmable pulsed voltammetric detection for the multiplexed sensing of DA, UA, and AA. Systematic electrochemical evaluations demonstrated that CNTFEs exhibit significantly enhanced sensitivity, compared with conventional gold fiber microelectrodes, with sensitivity improvements of 141-fold for DA detection, 146-fold for UA detection, and 44-fold for AA detection under DPV. In vivo validation via implantation of CNTFEs in the rat striatum demonstrated the sensor's capability for real-time, in-situ detection of externally supplemented DA and AA in brain model. These findings highlight the potential of ultra-soft CNTFE-based sensors for minimally invasive neurochemical monitoring, providing potential opportunity for development of implantable biosensing technologies toward personalized diagnostics in neurological disorders. Physical sciences/Nanoscience and technology/Nanobiotechnology/Biosensors Physical sciences/Nanoscience and technology/Nanoscale devices Carbon nanotube fiber electrochemical biosensing multifunctional sensing system in situ neurochemical monitoring in vivo sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Neurochemicals play a central role in the precise control of various biological processes (e.g., exercise, metabolism, immunity) as well as in the maintenance of psychological and behavioral homeostasis 1 . Monitoring neurochemical dynamics is essential for understanding the communication between neurons and their targets, as well as for the early diagnosis and treatment of neurological disorders 2 , 3 . Neurochemicals encompass neurotransmitters, hormones, and neuromodulators, which collectively contribute to the dynamic interplay between the brain and the body by mediating signal transmission, modulating physiological functions, and preserving neuroplasticity 4 . In the central nervous system (CNS), dopamine (DA), ascorbic acid (AA) and uric acid (UA) are involved in the regulation of neural activities related to motor control, reward mechanisms and oxidative stress homeostasis 5 , 6 . DA, as a key neurotransmitter, is widely implicated in multiple CNS functions and its abnormal concentrations are strongly associated with neurological disorders such as Parkinson's, Alzheimer's, and schizophrenia 7 – 9 . AA, the most abundant water-soluble antioxidant in the brain, plays a crucial role in protecting neurons from lipid peroxidation damage. As a cofactor for tyrosine hydroxylase, AA promotes DA synthesis, and imbalances in its concentration are closely linked to neurodegenerative disease progression 10 . UA is the final metabolite of purine metabolism and is commonly used as a metabolite indicator to reflect gout and renal disease, but it is also an important endogenous antioxidant in the nervous system, scavenging reactive oxygen species (e.g., peroxynitrite) and protecting dopaminergic neurons from oxidative stress-induced damage 11 . Therefore, simultaneous in situ and real-time monitoring of neurochemicals enables comprehensive analysis of neurotransmitter metabolic kinetics, oxidative stress balance, and the synergistic mechanisms of neurodegenerative diseases, thereby providing important information for investigating the pathological mechanisms of neurodegenerative diseases and developing neuromodulatory therapeutic strategies 12 – 14 . Nevertheless, the techniques for detecting multiple neurochemicals within the brain remain limited. For the detection of intracerebral neurochemical species, commonly used methods include liquid chromatography, mass spectrometry, microdialysis, molecular fluorescent probes, and electrochemical detection technologies 15 , 16 . Conventional detection methods such as liquid chromatography and mass spectrometry involve complex cerebrospinal fluid extraction steps that limit their application for in situ continuous monitoring 16 . Although microdialysis allows continuous in vivo sampling by implanting semipermeable membrane probes, its low perfusion flow rate results in poor temporal resolution, limiting the detection of rapid neurochemical fluctuations 15 . Moreover, the reliance on bulky external perfusion apparatus and post-collection analytical instruments, such as liquid chromatography or mass spectrometry, further restricts its use in real-time monitoring and freely behaving animal models 16 . The recent development of genetically encoded fluorescence sensors provides significant advantages in terms of sensitivity, selectivity and temporal resolution 17 . However, their application is constrained by limited tissue penetration depth, reliance on optical access (e.g., cranial windows), photobleaching, and susceptibility to signal drift over long-term imaging sessions. In comparison, electrochemical detection methods have emerged as an important tool for in situ monitoring in brain science due to their high sensitivity, rapid response time, and ease of miniaturized integration 18 – 21 . However, in practical applications, the concentrations of DA, AA and UA in vivo are relatively low, and their electrochemical signals overlap pose a challenge for simultaneous detection over a wide dynamic range 22 – 24 . Therefore, it is still challenging to achieve continuous and selective detection of DA, AA, and UA in the complex brain environment. Precious metals are widely utilized as electrode materials for in vivo electrochemical sensing due to their favorable electrochemical activity and low biotoxicity 22 , 25 . For instance, Au electrodes have been extensively employed for the long-term monitoring of neurochemicals such as DA and 5-HT, owing to their excellent electrochemical stability, high conductivity, and biocompatibility 26 . Similarly, Pt electrodes, owing to their intrinsic electrocatalytic activity, have demonstrated good performance in detecting oxidizable biomolecules such as glutamate, lactate, and AA, especially when surface kinetics are optimized to enhance reaction efficiency 26 , 27 . However, the rigid mechanical nature of metal electrodes limits their long-term reliability in dynamic brain environments. The mechanical mismatch between stiff electrodes and soft neural tissue may induce micromotion-related damage, immune responses, and signal instability. Moreover, the conductive nature of metals often leads to magnetic susceptibility artifacts in magnetic resonance imaging (MRI), thereby restricting their use in multimodal neuroimaging applications. To address these limitations, various conductive polymer electrodes have been explored for in vivo sensing 28 . Conductive polymers such as poly(3,4-ethylenedioxythiophene) (PEDOT) are commonly used to modify electrode surfaces, offering enhanced charge injection capacity, reduced impedance, and improved tissue-electrode interface compliance 29 . Despite these advantages, such materials often suffer from limited long-term electrochemical stability, potential degradation, and complex fabrication processes, posing challenges for chronic implantation 30 . Carbon-based materials, on the other hand, have emerged as compelling alternatives due to their intrinsic electrical conductivity, chemical stability, and exceptional mechanical flexibility 31 – 34 . Their non-ferromagnetic nature minimizes susceptibility artifacts and signal distortion under strong magnetic fields, ensuring compatibility with MRI and enabling seamless integration with multimodal diagnostic systems 35 , 36 . These properties make carbon-based electrodes particularly well-suited for implantable neurochemical sensors that demand both biocompatibility and functional stability in real-time, in vivo environments 37 – 40 . Here, we developed an ultra-soft carbon nanotube fiber electrode (CNTFE) platform for real-time, continuous electrochemical monitoring of DA, AA, and UA (Fig. 1 a-c). The CNTFEs were fabricated via aerosol-assisted floating catalyst chemical vapor deposition (FCCVD), forming continuous fibers composed of entangled carbon nanotube bundles, whose structure was featured with high mechanical flexibility and resilience under various modes of deformation (bending, stretching, and torsion), allowing electrodes to maintain stable electrical and structural integrity under dynamic physiological conditions. We first evaluated the mechanical performance of the CNTFEs, which exhibited excellent mechanical compliance, supporting their long-term operational stability and biocompatibility for chronic implantation. Subsequently, three voltammetric techniques, linear sweep voltammetry (LSV), square wave voltammetry (SWV), and differential pulse voltammetry (DPV), were employed to assess the electrochemical performance of the CNTFEs (Fig. 1 d). In vitro voltammetric measurements revealed high selectivity and sensitivity of CNTFEs toward detection of DA, AA, and UA, outperforming conventional gold fiber microelectrodes (AuFEs). Notably, under DPV conditions, the CNTFEs demonstrated sensitivity enhancements of 140.9-fold for DA detection, 146.2-fold for UA detection, and 43.8-fold for AA detection compared to AuFEs. In vivo experiments involving microelectrode implantation in rats further confirmed the high tissue conformability and mechanical stability of CNTFEs, enabling multiplexed and real-time monitoring of externally supplemented DA and AA in brain model. The CNTFEs-based electrochemical system developed here provided a robust and adaptable platform for multi-analyte detection in complex biofluids. Through combining in vitro and in vivo validation, this work established experimental basis for high-performance neurochemical sensing that might contribute to the development of miniaturized biosensing platforms for neurological applications. 2. Result and discussion Ultrasoft and highly conductive carbon nanotube fiber electrodes (CNTFEs) featuring a nano-helical bundle architecture were developed for implantable multiple neurochemicals monitoring in brain tissue ( Fig. 1e1 ). These CNTFEs were fabricated via aerosol-assisted floating catalyst chemical vapor deposition (FCCVD). Scanning electron microscopy (SEM) images ( Fig. 1e2 ) revealed that each CNTFE comprises numerous individual CNTs helically twisted and stacked into a well-defined bundle structure, yielding a typical fiber diameter of 50–100 µm. High-magnification images of the fiber surface showed tightly aligned CNTs forming highly oriented nanofiber bundles. These bundles were densely interwoven, resulting in a corrugated fiber morphology due to entanglement, which significantly increased the mechanical durability and robustness of the microelectrodes. As shown in Fig. 1f1 , the CNTFEs exhibited exceptional softness with ultra-small diameter, minimizing mechanical mismatch and tissue damage upon brain implantation. Bending tests confirmed their outstanding mechanical resilience, as the electrodes maintained structural integrity without notable cracking or deformation even after 500 cycles of 50% bend-relaxation ( Fig. 1f2-4 ). These results indicated the high mechanical compliance and stability of CNTFEs in dynamic testing environments. To electrically isolate the active regions and mitigate environmental interference, the CNTFEs were coated with a thin (~ 3 µm thick), uniform polyimide (PI) insulating layer, exposing only the tip with 1 mm area for sensing. SEM imaging confirmed the homogeneity, conformality and thin feature of the insulation coating (Fig. 1 g and h ) which preserved the intrinsic flexibility of the underlying CNT structures. For implantation, a transient hardening strategy was employed by coating the CNTFEs with a water-soluble, biocompatible polyvinylpyrrolidone (PVP) layer. This temporary coating facilitated smooth insertion into brain tissue and rapidly dissolved upon exposure to aqueous environments, thereby restoring the electrodes’ flexibility. This design ensures both surgical precision and long-term functional stability of the implanted CNTFEs in vivo. We next evaluated the electrochemical sensing performance of CNTFEs for the detection of DA, AA, and UA using three typical voltammetric techniques. Voltammetric analysis is widely employed for neurochemical monitoring due to its high sensitivity, temporal resolution, and compatibility with in vivo applications. Linear sweep voltammetry (LSV) enables the characterization of redox behavior by applying a continuously varying potential to the working electrode while measuring the resulting current response. Square wave voltammetry (SWV) improves detection sensitivity and resolution by superimposing a symmetric square waveform on a staircase potential, thereby facilitating rapid and precise quantification. Differential pulse voltammetry (DPV) further enhances analytical performance by suppressing background capacitive currents through the application of modulated pulse waveforms, making it particularly effective for detecting low-abundance analytes in complex biological matrices. Each neurochemical species displays a distinct oxidation potential, allowing selective excitation of redox reactions and enables the differentiation of DA, AA, and UA through their characteristic current peaks. To systematically assess the electrochemical performance of CNTFEs, we performed LSV, SWV, and DPV measurements for each type of analyte under identical testing conditions. AuFEs were used in parallel as control electrodes to evaluate the relative performance of CNTFEs. All electrochemical experiments were conducted using a standardized three-electrode configuration, consisting of a CNTFE as the working electrode, a CNTFE as the counter electrode, and a CNTFE-based Ag/AgCl reference electrode (Fig. 2 a). The use of CNTF components as the base of the three-electrode system ensured material consistency across electrodes and minimized artifacts introduced by heterogeneous electrode compositions. The detection of DA is primarily based on the oxidative character of its catechol moiety, which contains a phenolic hydroxyl group. Upon reaching the oxidation potential at the electrode surface, DA undergoes a two-electron, one-proton oxidation reaction, forming o-dopaminoquinone (DAQ) and generates a measurable anodic current. Although the DA/DAQ redox process is theoretically reversible, the reduction of DAQ is thermodynamically unfavorable under physiological pH conditions 5 . Therefore, in most cases, only the oxidative signal is considered in the analysis. Based on this redox mechanism, CNTFEs can effectively capture the transferred charge and convert it into a pronounced increase in oxidation current, thereby enabling sensitive DA detection. For DA detection tests, LSV, SWV, and DPV measurements were conducted using CNTFEs, respectively, as shown in Fig. 2 b-d. DA solutions with concentrations ranging from 0 to 300 µM were incrementally added into phosphate-buffered saline (PBS), and the corresponding electrochemical responses were recorded using a standard three-electrode configuration. As shown in the LSV profiles ( Fig. 2b1 ), a distinct oxidation peak appeared near 0.1 V, indicating the occurrence of the oxidative reaction of DA at the CNTFE surface. The peak current increased proportionally with DA concentration, exhibiting a linear relationship with a sensitivity of 0.0374 µA/µM and a correlation coefficient (R²) of 0.9866 ( Fig. 2b2 ). In the case of SWV, well-defined oxidation peaks were observed across a DA concentration range of 1-300 µM, yielding a linear calibration curve with a sensitivity of 0.128 µA/µM and R² = 0.9814 (Fig. 2 c). Compared with LSV and SWV, DPV enabled detection of DA at lower concentrations with markedly improved sensitivity. As shown in Fig. 2d1 and d2 , DPV measurements demonstrated excellent linearity in the low concentration range (0.2-5 µM), with a sensitivity of 0.1127 µA/µM and R² = 0.9921. At higher DA concentrations (10–300 µM), the DPV response exhibited two distinct linear regions, with sensitivities of 0.199 µA/µM (R² = 0.9946) and 0.0678 µA/µM (R² = 0.9602), respectively ( Fig. 2d3 and d4 ). These findings collectively indicated that while LSV and SWV offer reasonable performance at higher concentration ranges, DPV provided better sensitivity and a broader dynamic range, especially in the physiologically relevant low-concentration regime. Given that extracellular DA concentrations in the brain typically fall within the 0.011 µM range, CNTFEs combined with DPV detection might be suitable for real-time monitoring of DA under physiological conditions. To evaluate the sensitivity and detection limits of CNTFEs, we conducted parallel electrochemical measurements using conventional AuFEs as control group. In LSV measurements, although the current increased with rising DA concentrations, no distinct oxidation peak was observed ( Fig. 2e1 ), and the resulting sensitivity was only 0.0026 µA/µM (R² = 0.9967), more than an order of magnitude lower than that observed with CNTFEs ( Fig. 2e2 ). In SWV, AuFEs exhibited a weak oxidation peak near 0.3 V, but the calculated sensitivity was merely 0.0008 µA/µM (R² = 0.9963), representing a reduction of over two orders of magnitude compared to CNTFEs (Fig. 2 f). Similarly, in DPV measurements, AuFEs yielded a sensitivity of only 0.0009 µA/µM (R² = 0.9930, Fig. 2 g), with a detectable concentration threshold of approximately 10 µM-significantly higher than the ~ 0.2 µM detection limit achieved with CNTFEs. Collectively, these results demonstrated that CNTFEs exhibit markedly better sensitivity and lower detection limits for DA detection across multiple voltammetric techniques. These advantages are especially useful at physiologically relevant concentrations, where CNTFEs outperform AuFEs by two to three orders of magnitude, underscoring their potential for highly sensitive neurochemical monitoring in vivo. AA, a common and highly electroactive molecule, functions as a potent reducing agent and is readily oxidized under electrochemical conditions. During a positive-going potential sweep, AA undergoes a two-electron, two-proton oxidation to form dehydroascorbic acid (DHAA). At physiological pH, DHAA is unstable and prone to irreversible hydrolysis, rendering the AA/DHAA redox process effectively irreversible 5 . Based on this electrochemical mechanism, we performed voltammetric detection of AA using CNTFEs. AA was incrementally added to PBS solution in 200 µM steps up to a final concentration of 2 mM, and the corresponding LSV, SWV, and DPV responses were recorded using a standard three-electrode setup. Across the concentration range of 0–2 mM, all three voltammetric methods showed well-defined oxidation peaks centered around − 0.1 V, with current intensity increasing proportionally with AA concentration (Fig. 3 a-c). Among them, DPV exhibited narrower peak widths and smaller potential shifts, indicating improved resolution over LSV and SWV. Linear calibration plots yielded sensitivities of 0.0244 µM/µA (R² = 0.9948, Fig. 3a2 ) for LSV, 0.0433 µM/µA (R² = 0.9983, Fig. 3b2 ) for SWV, and 0.0129 µM/µA (R² = 0.9990, Fig. 3c2 ), confirming that CNTFEs enable reliable and sensitive AA detection within the 0–2 mM range using all three techniques. To further assess the performance of CNTFEs in AA detection, we conducted comparative measurements using AuFEs under identical experimental conditions (Fig. 3 d-f). In LSV measurements, although the current increased with increasing AA concentrations, no distinct oxidation peak was observed, and the sensitivity was limited to 0.0024 µA/µM (R² = 0.9824, Fig. 3d2 ). In both SWV and DPV measurements, oxidation peaks were observed near 0.6 V, confirming the occurrence of AA oxidation on the AuFE surface. However, the corresponding sensitivities remained low, measured at 0.0011 µA/µM (R² = 0.9924, Fig. 3e2 ) for SWV and 0.0005 µA/µM (R² = 0.9928, Fig. 3f2 ) for DPV. Notably, the oxidation peak potentials observed for AuFEs were consistently above 0.6 V, in contrast to the significantly lower range of -0.2 to 0 V recorded for CNTFEs. This shift in potential not only reduces the selectivity of AuFEs but also increased the likelihood of interference from other electroactive species during in vivo detection. Furthermore, the oxidation peaks recorded with AuFEs were broader in shape, which compromises resolution and hampers accurate discrimination among coexisting analytes. Taken together, these findings underscored the better electrochemical characteristics of CNTFEs for AA detection, including lower oxidation potentials, sharper peak resolution, and substantially improved sensitivity under physiologically relevant conditions. The electrochemical detection of UA is based on its irreversible oxidation at the electrode surface. When a positive potential is applied, UA undergoes a two-electron, two-proton oxidation reaction and is ultimately converted into allantoic acid. This redox process allows the electrode to capture the resulting charge transfer, manifesting as a measurable electrochemical response 5 . To evaluate the UA sensing performance under different electrochemical conditions, we employed LSV, SWV, and DPV techniques using both CNTFEs and AuFEs. For CNTFEs, UA solutions were incrementally added to PBS buffer in 100 µM steps up to a final concentration of 600 µM, and corresponding voltammetric responses were recorded (Fig. 4 a-c). In the LSV profiles, a clear oxidation peak was observed, confirming the occurrence of UA oxidation at the CNTFE surface. Although the current increased proportionally with UA concentration, the peak potential shifted markedly toward more positive values, suggesting less stability in the oxidation dynamics. Linear fitting of current versus concentration yielded a sensitivity of 0.0552 µM/µA with a correlation coefficient of R² = 0.9577 ( Fig. 4a2 ). Both SWV and DPV responses also exhibited distinct oxidation peaks across the 0–600 µM range, with current intensities increasing in response to UA concentration (Fig. 4 b and c ). Compared with LSV, these methods showed smaller shifts in peak potential and improved signal stability. The calculated sensitivities were 0.0662 µM/µA (R² = 0.9397, Fig. 4 b 2 ) for SWV and 0.0731 µM/µA (R² = 0.8930, Fig. 4c2 ), indicating superior performance of SWV and DPV for UA detection using CNTFEs. These results suggested that, among the tested techniques, SWV and DPV offered greater potential for reliable and stable UA sensing, particularly in dynamic biological environments. For comparison, electrochemical detection of UA was also carried out using AuFEs under identical conditions (Fig. 4 d-f). In all three methods, oxidation peaks were observed as the UA concentration increased, indicating successful oxidation at the AuFE surface. However, the sensitivities were substantially lower, with values of 0.0021 µM/µA (R² = 0.9758, Fig. 4d2 ) for LSV, 0.0013 µM/µA (R² = 0.9955, Fig. 4e2 ) for SWV, and 0.0005 µM/µA (R² = 0.9928, Fig. 4f2 ) for DPV. Additionally, the oxidation peaks recorded with AuFEs were significantly broader and occurred at potentials above 0.6 V, closely overlapping with those of AA. This lack of peak separation compromised selectivity and made simultaneous detection in complex biological matrices challenging. In contrast, CNTFEs exhibited oxidation peaks for UA consistently around 0.25 V, providing better discrimination from other analytes such as AA. Furthermore, the lower oxidation potential observed with CNTFEs reduces the likelihood of interference from non-target electroactive species. Collectively, these findings highlight the advantages of CNTFEs for UA detection, offering higher sensitivity, improved selectivity, and better compatibility with physiological conditions compared to conventional AuFEs. In electrochemical sensing, key performance indicators such as sensitivity, oxidation potential, detection limit, and selectivity critically determine a sensor's applicability in complex biological environments. To evaluate these parameters systematically, we compared the performance of CNTFEs and AuFEs for the detection of DA, AA, and UA with multiple voltammetric techniques (Fig. 5 a-c). As shown in Fig. 5 a, CNTFEs exhibited significantly higher detection sensitivity than AuFEs across all analytes, with the most pronounced enhancement observed for DA-approximately three orders of magnitude greater than that achieved with AuFEs. Notably, DPV on CNTFEs was most suitable for detecting both DA and UA, while SWV offered higher sensitivity in UA detection. Figure 5 b compared the oxidation peak potentials for each analyte across different electrode types and voltammetric modalities. The peak potentials recorded using AuFEs were consistently elevated, ranging from 0.3 V to 0.8 V, which may compromise selectivity in complex biological matrices due to increased susceptibility to interference from non-target oxidizable species. In contrast, CNTFEs demonstrated significantly lower oxidation potentials, offering improved analyte specificity. As for the detection threshold (Fig. 5 c), CNTFEs showed a markedly lower minimum detectable concentration for DA (~ 0.2 µM), compared to ~ 10 µM for AuFEs. For AA and UA, the detection thresholds were comparable between CNTFEs and AuFEs. These findings collectively highlight the superior sensing performance of CNTFEs in terms of sensitivity, electrochemical resolution, and physiological relevance, underscoring their potential for real-time neurochemical monitoring in vivo. Selectivity reflects the ability to accurately identify the target in the presence of coexisting interferences. High selectivity ensures that the sensor can specifically respond to the target in complex samples, thereby minimizing interference from other substances and avoiding false positive results. The oxidation potentials of DA, UA, and AA are usually similar and may even overlap, which poses considerable difficulty to differentiate these substances for concentration quantification. In this study, CNTFEs were used to simultaneously multiplex detection of DA, UA, and AA. This method distinguished the concentration of each analyte by the peak potential size and the corresponding current value. Here, we verified the selectivity of CNTFE for DA, AA, and UA based on the DPV method, as shown in Fig. 5 d-g. Figure 5 d showed the DPV curve of DA in the concentration range of 0-100 µM in the presence of 300 µM AA and 40 µM UA. It can be found that the response current showed a clear oxidation peak near the potential of 0.1 V and increased with the increase of DA concentration, while the peak potentials of AA and UA remained unchanged near − 0.1 V and 0.25 V. Similarly, Fig. 5 e showed that when the concentrations of DA and AA were constant, the response current increased only around the peak potential of 0.25 V when the concentration of UA increased gradually. Also, when the concentrations of DA and UA were constant, the response current increased gradually around − 0.1 V when the concentration of AA increased gradually (Fig. 5 f). The results show that CNTFE can distinguish the substances and concentrations of DA, UA and AA in a solution where they coexist, and has good detection selectivity. As shown in Fig. 5 g and h , an electrochemical voltammetric detection circuit was developed using a data acquisition card and LabView platform, enabling programmable waveform generation and real-time data acquisition. A custom three-electrode circuit module conditioned the analog signals, which were amplified and digitized via high-precision digital-to-analog converter and analog-to-digital converter modules to ensure accurate electrochemical measurements. This circuit supported LSV, SWV, and DPV techniques, with parallel programming architecture allowing simultaneous multi-mode testing and enhanced efficiency. Subsequently, DA was tested in vitro voltammetry based on the constructed multifunctional electrochemical sensing system. The LSV, SWV and DPV response current signals of CNTFEs are shown in Fig. 5 i-k. The experimental results show that CNTFEs show regular electrochemical responses under all three detection methods. Among them, the LSV and SWV peak response potentials are close to 0.2 V, and the response current signals are positively correlated with the concentration in the range of 0-200 µM. The peak response potential of DPV is around 0.1 V. In the DA solution with a concentration of 0-150 µM, the current response shows a positive correlation trend with a clear peak. This result shows that it is possible to use homemade circuits to achieve continuous detection of neurochemical substances such as DA. To evaluate the continuous in vivo sensing performance of CNTFEs, we implanted and secured an integrated CNTFE bundle into the striatal region of live rats (Fig. 6 a). The electrode system consisted of two CNTFEs functioning as the working and counter electrodes, respectively, and a third CNTFE modified with Ag/AgCl serving as the reference electrode. Due to the intrinsic softness of CNTFEs, polyvinylpyrrolidone (PVP) was applied as a temporary stiffening agent to facilitate implantation, thereby avoiding tissue damage typically associated with microneedle-assisted insertion techniques (Fig. 6 b). The PVP coating dissolved within one-minute post-implantation, restoring the original flexibility of the electrodes and ensuring mechanical compatibility with brain tissue. DPV was employed for neurochemical detection in a series of parallel experiments conducted on healthy Sprague-Dawley rats. Implantation was performed under stereotaxic guidance, and the electrodes were fixed using dental cement, leaving a cranial window approximately 1 mm in diameter for subsequent substance delivery. DA and AA, both with well-established functions in the central nervous system, were selected as representative electroactive targets for evaluating the in vivo sensing performance of CNTFEs, whereas UA was excluded due to its lower brain concentration and limited characterization. Under normal physiological conditions, the basal concentration of DA in brain tissue remains low, with transient spikes typically induced by specific stimuli (e.g., reward anticipation) or occurring under pathological conditions (e.g., Parkinson’ s disease). In anesthetized animals, the suppression of neural activity significantly limits endogenous DA release, making it difficult to elicit electrochemical signals that surpass the detection threshold. To address this limitation and to evaluate the real-time, in situ sensing performance of the CNTFE system, exogenous DA was locally administered to simulate transient neurotransmitter fluctuations. To minimize mechanical trauma to deep brain structures, the injection needle was positioned just above the striatal region, allowing DA to gradually diffuse into the striatum rather than being directly injected into it. This strategy avoids secondary injury caused by direct penetration into the target nucleus while still enabling localized delivery of the analyte. Given the high diffusivity of DA within the brain interstitial environment, its concentration at the site of action rapidly declines post-injection. To compensate for this effect and ensure measurable electrochemical responses, DA solutions at relatively high concentrations were used, acknowledging that the actual concentration reaching the striatal tissue is considerably lower due to diffusion. A comparable protocol was adopted for AA, which is typically present at low levels in healthy brain tissue and are only elevated in pathological conditions such as oxidative stress or metabolic dysregulation. Since the primary aim of these in vivo experiments was to validate the detection capability of CNTFEs under biologically relevant conditions, rather than to replicate physiological concentrations, AA was likewise administered via controlled local injections. It is important to note that the injected concentrations exceed typical endogenous levels and were deliberately chosen to generate robust, quantifiable signals for sensor performance evaluation. Additionally, due to the lack of established in vivo reference methods for determining precise concentrations of DA and AA in brain tissue, the electrochemical outputs were presented as peak current responses. These measurements reflect relative temporal changes in the local concentration of analytes following injection. As illustrated in Fig. 6 c, DA was delivered in 20 µL aliquots at three concentrations (C1: 1 mM, C2: 10 mM, C3: 50 mM) with 30-minute intervals between injections to ensure adequate diffusion and partial metabolic clearance. Six electrochemical measurements were taken at each concentration level. The same injection strategy was used for AA. Dose-dependent electrochemical responses to DA (C1-C3) were recorded in three SD rats, with injection time points marked by arrows. Across all animals, the mean detected DA current response increased with injection dose, reaching 3.16 ± 2.36 µA for C1, 4.66 ± 2.73 µA for C2, and 9.49 ± 4.18 µA for C3 (Fig. 6 d). While intra-individual fluctuations were minimal, noticeable inter-individual variability was observed. For instance, Rat #3 exhibited significantly higher DA levels, likely reflecting differences in DA diffusion dynamics, metabolic turnover, or electrode–tissue interactions. Although the in vivo detection experiments are performed by externally supplementing DA to mimic the release of DA in vivo, these findings demonstrated that the capability of CNTF electrode bundle enables reliable, responsive detection of DA in situ. Similarly, in vivo AA sensing results are presented in Fig. 6 d. Following intracranial administration of AA at C1 (1 mM), C2 (10 mM), and C3 (100 mM), all three animals exhibited concentration-dependent increases in the detected AA levels. The averaged concentrations across all rats were 13.50 ± 12.51 µA (C1), 13.49 ± 12.47 µA (C2), and 17.84 ± 13.36 µA (C3). Notably, AA responses plateaued between C1 and C2 but surged at C3, suggesting a nonlinear signal amplification effect, potentially due to local molecular accumulation or saturation of oxidation kinetics. Individual response profiles further supported this interpretation: Rat #1 showed a gradual rise, Rat #2 exhibited elevated and highly sensitive responses with larger fluctuations, Rat #3 maintained moderate increases across doses. Despite these differences, the CNTFEs consistently generated reproducible, concentration-responsive signals, underscoring their suitability for continuous AA monitoring in vivo. Collectively, these in vivo results suggested the CNTFE as a reliable platform for real-time and in situ monitoring of DA and AA within the striatum. Due to the anesthetized state of rats, we were unable to measure sufficient concentrations of DA and AA in the normal physiological state of the rat brain. Although we used external injection for DA and AA sensing based CNTFE in a brain model, the system demonstrated robust electrochemical performance across analytes measurement in the brain enviroment. Conclusion and discussion In this study, we developed an ultra-soft CNTFE platform capable of achieving sensitive, real-time, and continuous monitoring of DA, AA, and UA. The CNTFEs exhibited reliable electrochemical characteristics, including sharp and well-resolved oxidation peaks, low oxidation potentials, and strong selectivity across various voltammetric techniques such as LSV, SWV, and DPV. Compared with conventional AuFEs, the CNTFEs demonstrated significantly enhanced sensitivity, particularly for DA, where the improvement reached nearly three orders of magnitude. In addition, they retained mechanical flexibility and biocompatibility, making them particularly suitable for stable and long-term operation within delicate neural tissues. Under DPV conditions, the CNTFEs achieved detection sensitivities of 0.1127 µM/µA for DA detection, 0.0219 µM/µA for AA detection, and 0.0731 µM/µA for UA detection. To support the sensing functionality of the electrodes, we constructed a multifunctional electrochemical system that integrates waveform generation, signal acquisition, and real-time data visualization. The modular design of this system could support the concurrent operation of multiple voltammetric modes, thereby providing a flexible and efficient platform for electrochemical testing. In vivo experiments further validated the practical utility of the CNTFE for direct intracerebral chemical sensing. By temporarily stiffening the electrodes with a water-soluble polymer (PVP), we achieved smooth implantation into the striatum without relying on rigid carriers or microneedle-based techniques. After implanted, the electrodes rapidly regained their softness, enabling intimate and stable contact with the surrounding brain tissue. To overcome the difficulty of detecting low concentrations of DA and AA in the brain of anesthetized rats in natural physiological state, real-time measurements of externally supplemented DA and AA solutions were performed in the brain of anesthetized rats, and repeated trials showed a consistent signal pattern. This work presents a soft and biocompatible electrochemical sensing strategy that enables implantation and longitudinal monitoring of multiple neurochemical species in the brain, which is promising as tool to access the biochemical concentrations for in vivo applications. Materials and methods Preparation of CNTFEs . The CNTFEs were ordered from Nanjing Ji Cang Nano Technology Co., Ltd., and the preparation procedure is based on aerosol-assisted floating catalyst chemical vapor deposition (FCCVD). N , N -Dimethylformamide (DMF) and Polyimide (PI) were purchased from Shanghai Macklin Biochemical Technology Co., Ltd..1 mL of DMF was mixed with 0.6 g of PI powder and stirred thoroughly at room temperature to obtain a dark red viscous solution. For the insulating process to the electrodes, the two ends of microfilament electrodes were fixed on a hollow shelf using clamps to make the electrodes remain in an upright position. And with the help of the dust-free cotton swab, the mixture was slowly applied to the surface of the electrodes until a dense, uniform insulating coating was formed, and an area of approximately 1 mm was exposed at the top of the electrodes for sensing. The above microfilament electrodes were hung and placed in an oven at 40 ℃ for 6 hours to fully cure the insulating layer to obtain insulated electrodes. Mechanical Bending and Durability Testing. The CNTFE was subjected to cyclic bending using a reciprocating tensile testing platform. Both ends of the CNTFE were fixed to the platform fixture, leaving a 2 cm segment as the effective bending section. The platform operated at a constant speed of 2 mm/s with a displacement amplitude of 1 cm, maintaining a maximum bending strain of 50% over 500 cycles. After testing, the electrode surface morphology was examined using a scanning electron microscope (SEM) to evaluate potential cracking or deformation. Temporary Stiffening of Electrodes. Polyvinyl pyrrolidone (PVP) was purchased from Shanghai Macklin Biochemical Technology Co., Ltd., and a 10% W/V PVP solution was prepared using ethanol as solvent. The PVP solution was uniformly sprayed on the surface of AuFEs and CNTFEs using a spray bottle, and the process was repeated 5 times. The electrodes were dried at room temperature for 2 hours to completely eliminate the solvent, resulting in hardened sensing electrodes that could penetrate brain tissue. Surface Morphology Characterization. Optical microscopy (MF52-LED, Mshot) was used to characterize the electrode profile. Scanning electron microscope (Phenom Pro Desktop SEM, Thermal Fisher Scientific) was used to characterize the micro-morphology of the surface materials of the sensing electrodes at different stages. Electrochemical Characterization. Electrochemical measurements of CNTFEs and AuFEs (Nanjing Ji Cang Nano Technology Co., Ltd.) were performed using a CHI660e electrochemical workstation in a standard three-electrode configuration, with a commercial Ag/AgCl electrode as the reference electrode, a platinum wire as the counter electrode, and CNTFEs or AuFEs as the working electrodes. Three voltammetric techniques were employed: LSV, SWV, and DPV. LSV was conducted over a potential range of -0.3 V to 0.6 V with a scan rate of 0.1 V/s and a sampling interval of 1 mV. SWV was performed within the same potential window, using a voltage increment of 4 mV, an amplitude of 25 mV, and a frequency of 4 Hz. For DPV, the potential range was set to -0.3 V to 0.6 V, with a voltage increment of 4 mV, pulse width of 0.05 s, amplitude of 50 mV, sampling interval of 0.02 s, and pulse period of 0.5 s. For analyte detection, DA, AA, and UA were introduced into PBS in a stepwise concentration gradient. Before each measurement, the solution was stirred for 30 s and then allowed to stand for 30 s to ensure homogeneity and stability. Selectivity tests were performed using DPV under the same conditions as above. For DA selectivity, the concentrations of AA and UA were fixed at 300 µM and 40 µM, respectively, while DA was incrementally increased from 0 to 100 µM. For UA selectivity, UA concentration was varied from 0 to 300 µM in a background containing 20 µM DA and 300 µM AA. For AA selectivity, AA was added in increments to a solution containing 20 µM DA and 40 µM UA, resulting in a concentration range of 0 to 2000 µM. After each addition, the solution was stirred for 30 s and rested for 30 s before DPV measurements. Design of Custom Sensing Circuitry. An electrochemical pulse voltammetric detection system was implemented utilizing a National Instruments (NI) data acquisition (DAQ) card. A programmable waveform sequence was generated using the LabView software development platform, which is subsequently output as a precise analog voltage waveform via the integrated digital-to-analog converter (DAC) of the NI DAQ card. Further, this analog voltage waveform was signal-conditioned by a custom-designed three-electrode circuit module to optimize electrochemical test parameters. For data acquisition, the three-electrode circuit collected electrochemical signals, which were then amplified by a transimpedance amplifier and fed into the high-precision analog-to-digital converter (ADC) module of the NI DAQ card. Real-time data processing and visualization were performed using the LabView software. This multifunctional electrochemical sensing system supports various voltammetric techniques, including LSV, SWV and DPV. Moreover, the programming adopts the parallel structure, which makes this electrochemical work sensing system can realize the different testing methods at the same time, and improves the efficiency of electrochemical testing. Waveform generation and data processing are performed using high-precision DAC and ADC modules within the NI DAQ card to ensure data stability and accuracy. Animal studies. The experimental protocols related to the in vivo rats in this work were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University and Guangzhou Boyao Biotechnology Co. The designed experiments were carried out in full accordance with the guidelines for the care and use of laboratory animals by the Institutional Animal Care and Use Committee of Sun Yat-sen University (No. SYSU-IACUC-2023-001854) and Guangzhou Boyao Biotechnology Co (No. IAEC-K-240925-01). The SD rats used in the experiments weighed 220–250 g and were about 8 weeks old, purchased from Guangzhou Boyao Biotechnology Co. and continued to be housed for one week after purchase to adaptive the surrounding environment. In Vivo Neurochemical Detection Using CNTFEs. Adult Sprague-Dawley (SD) rats were anesthetized via intramuscular injection of Zoletil (50 mg/kg) and fixed in a stereotaxic frame. After a midline scalp incision, the skull was exposed and leveled to ensure accurate targeting. A circular craniotomy (~ 5 mm diameter) was performed above the striatal region using a surgical drill. Integrated CNTFE bundles were stereotaxically implanted into the striatum at the following coordinates: anteroposterior (AP) + 0.7 mm, mediolateral (ML) + 3.0 mm, and dorsoventral (DV) -4.0 mm. The electrodes were secured with dental cement (Minnesota Mining and Manufacturing), leaving a ~ 1 mm opening to allow for solution delivery. The implanted CNTFEs were connected to an external electrochemical workstation for differential pulse voltammetry (DPV) recording. Considering that the basal concentrations of DA and AA in brain tissues are usually maintained at low levels in physiological states, the concentrations generated by endogenous release is difficult to meet the electrochemical detection threshold. Therefore, in this study, we validated the performance of CNTFE for in situ real-time monitoring by injecting external supplemental solution at a constant rate of 1 µL/min via a micro syringe pump into the striatal brain region at a total volume of 20 µL to detect neurochemicals. It should be noted that, considering the high-speed diffusion characteristics of brain interstitial fluid, the concentration of neurochemicals at the injection site can decay rapidly after drug injection. Therefore, we used a gradient concentration progression injection strategy to simulate the fluctuation of neurochemicals in a real physiological environment. Following each injection, electrochemical recordings were initiated immediately. In the DA sensing protocol, three concentrations (1 mM, 10 mM, and 50 mM) were prepared in PBS and sequentially administered from low to high, with each concentration considered as a distinct measurement cycle. For each concentration, DPV signals were collected at 5-minute intervals over a 30-minute recording window. The same procedure was applied for AA detection, using concentrations of 1 mM, 10 mM, and 100 mM. Upon completion of measurements for a given analyte, the cranial cavity was thoroughly flushed with PBS or sterile saline to remove residual compounds. The solution was then aspirated to minimize cross-contamination before proceeding to the next neurochemical trial. Declarations Competing interests The authors declare no competing interests. Author Contributions Xiaotong Li, Mengyi He and Xinshuo Huang contributed equally. Acknowledgment The authors would like to acknowledge financial support from the National Natural Science Foundation of China (Grant No. T2225010, 32171399, 32171456, 32401202), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023A1515111139, 2025A1515010608), Science and Technology Program of Guangzhou, China (Grant No. 2024B03J0121, 2024B03J1284), Shenzhen Science and Technology Program (Grant No. RCBS20231211090558093), Fundamental Research Funds for the Central Universities, Sun Yat-sen University (No. 24xkjc011), the Opening Project of The National Key Laboratory of Smart Farm Technology and Systems, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University (SKLACLS2502). References He, J. et al. Recent Advances in the Development and Characterization of Electrochemical and Electrical Biosensors for Small Molecule Neurotransmitters. ACS Sensors 8 , 1391-1403 (2023). Marinesco, S. Micro-and nano-electrodes for neurotransmitter monitoring. 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Additional Declarations There is no conflict of interest Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: revise 11 Nov, 2025 Review # 2 received at journal 10 Nov, 2025 Review # 1 received at journal 03 Nov, 2025 Review # 3 received at journal 29 Oct, 2025 Reviewer # 3 agreed at journal 28 Oct, 2025 Reviewer # 2 agreed at journal 24 Oct, 2025 Reviewer # 1 agreed at journal 23 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Submission checks completed at journal 12 Sep, 2025 Editor assigned by journal 05 Sep, 2025 First submitted to journal 05 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7541492","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":532685869,"identity":"ec01c9d4-f753-4dc9-a973-4addb89de98f","order_by":0,"name":"Xi 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09:15:52","extension":"html","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132035,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7541492/v1/9126188b751e7c4a293adc71.html"},{"id":94840298,"identity":"c947f7e0-85e2-4d7c-a01b-52f25ab9534c","added_by":"auto","created_at":"2025-10-31 09:15:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3202481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUltra-soft and multifunctional CNTFE-based platform for continuous neurochemical monitoring.\u003c/strong\u003e (a) Schematic of CNTFEs-based electrochemical sensing platform for in vivo detection of neurochemicals. (b) Conceptual diagram showing the continuous detection of DA, AA, and UA in brain tissue using implanted CNTFEs. (c) (c) Representative redox reactions of DA, AA, and UA illustrating the electrochemical detection mechanism. (d) Input waveforms of LSV, SWV, and DPV applied to the CNTFE surface, along with their respective electrochemical response profiles for neurochemical sensing. (e) Structural and morphological characterization of CNTFEs. (e1) Schematic depiction of the CNTFE featuring a nano-helical bundle architecture. (e2) SEM image of the CNTFEs at different scales. (f) Evaluation of CNTFE flexibility and structural integrity. (f1) Photographs of CNTFE in straightened and bent states. (f2) Schematic of mechanical bending test performed on CNTFE. (f3) SEM characterization of CNTFE before and after 500 bending-recovery tests. (g) Surface morphology and (h) cross-sectional SEM image of CNTFE following polyimide (PI) insulation, confirming conformal coating.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7541492/v1/8fe3e08c81cfe56f2a819ead.jpg"},{"id":94840292,"identity":"c2d35808-2551-4340-851b-84a28abe3c49","added_by":"auto","created_at":"2025-10-31 09:15:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1437547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectrochemical performance of CNTFEs for DA detection. \u003c/strong\u003e(a) Schematic illustration of the electrochemical detection of DA in solution. (b-d) Electrochemical voltammetry responses of CNTFEs for DA detection using (b) LSV, (c) SWV, and (d) DPV. (b1) LSV responses of CNTFEs to increasing DA concentrations. (b2) Calibration curve of LSV peak current as a function of DA concentration. (c1) SWV responses of CNTFEs to increasing DA concentrations. (c2) Calibration curve of SWV peak current versus DA concentration. (d1) DPV responses of CNTFEs to increasing DA concentrations. (d2) Calibration curve of DPV peak current versus DA concentration. (e-g) Electrochemical voltammetry responses of AuFEs for DA detection using (e) LSV, (f) SWV, and (g) DPV. (e1) LSV responses of AuFEs to increasing DA concentrations. (e2) Corresponding calibration curve of LSV peak current. (f1) SWV responses of AuFEs to increasing DA concentrations. (f2) Calibration curve of SWV peak current versus DA concentration. (g1) DPV responses of AuFEs to increasing DA concentrations. (g2) Calibration curve of DPV peak current versus DA concentration.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7541492/v1/90c75f2529dbf5ecfa7644f2.jpg"},{"id":94840295,"identity":"a87e813b-99f3-402c-9935-2e23183730b9","added_by":"auto","created_at":"2025-10-31 09:15:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1316407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectrochemical performance of CNTFEs for AA detection. \u003c/strong\u003e(a-c) Electrochemical voltammetry responses of CNTFEs for AA detection using (a) LSV, (b) SWV, and (c) DPV. (a1) LSV responses of CNTFEs to increasing concentrations of AA. (a2) Calibration curve of LSV peak current versus AA concentration. (b1) SWV responses of CNTFEs to increasing concentrations of AA. (b2) Calibration curve of SWV peak current versus AA concentration. (c1) DPV responses of CNTFEs to increasing concentrations of AA. (c2) Calibration curve of DPV peak current versus AA concentration. (d-f) Electrochemical voltammetry responses of AuFEs for AA detection using (d) LSV, (e) SWV, and (f) DPV. (d1) LSV responses of AuFEs to increasing concentrations of AA. (d2) Calibration curve of LSV peak current versus AA concentration. (e1) SWV responses of AuFEs to increasing concentrations of AA. (e2) Calibration curve of SWV peak current versus AA concentration. (f1) DPV responses of AuFEs to increasing concentrations of AA. (f2) Calibration curve of DPV peak current versus AA concentration.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7541492/v1/2a0b1ad8d4c6c2f9b89e9e58.jpg"},{"id":94984979,"identity":"795f59a9-2e7e-4bbb-959d-809e53344ee9","added_by":"auto","created_at":"2025-11-03 06:57:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1157077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectrochemical performance of CNTFEs for UA detection. \u003c/strong\u003e(a-c) Electrochemical voltammetry responses of CNTFEs for UA detection using (a) LSV, (b) SWV, and (c) DPV. (a1) LSV responses of CNTFEs to increasing concentrations of UA. (a2) Calibration curve of LSV peak current versus UA concentration. (b1) SWV responses of CNTFEs to increasing concentrations of UA. (b2) Calibration curve of SWV peak current versus UA concentration. (c1) DPV responses of CNTFEs to increasing concentrations of UA. (c2) Calibration curve of DPV peak current versus UA concentration. (d-f) Electrochemical voltammetry responses of AuFEs for UA detection using (d) LSV, (e) SWV, and (f) DPV. (d1) LSV responses of AuFEs to increasing concentrations of UA. (d2) Calibration curve of LSV peak current versus UA concentration. (e1) SWV responses of AuFEs to increasing concentrations of UA. (e2) Calibration curve of SWV peak current versus UA concentration. (f1) DPV responses of AuFEs to increasing concentrations of UA. (f2) Calibration curve of DPV peak current versus UA concentration.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7541492/v1/f31811bfc7ac7ad4195172a2.jpg"},{"id":94840293,"identity":"6d82cc2d-6078-4606-a9ef-a73ce044b2c7","added_by":"auto","created_at":"2025-10-31 09:15:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":736257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectrochemical sensing performance and system integration of CNTFEs. \u003c/strong\u003e(a-c) Comparison of CNTFE performance in terms of (a) sensitivity, (b) peak potential, and (c) minimum detection threshold for DA, AA, and UA using LSV, SWV, and DPV methods. (d-f) Selectivity of CNTFEs for (d) DA, (e) UA, and (f) AA under DPV measurements in mixed-analyte conditions. (g) Logic diagram of the pulse voltammetry-based electrochemical sensing circuit. (h) Software interface of the integrated multifunctional electrochemical sensing system. (i-k) Real-time response current signals for DA detection using the integrated system under (i) LSV, (j) SWV, and (k) DPV modes.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7541492/v1/7460489f5531fd3c29779671.jpg"},{"id":94840303,"identity":"9e7fcae0-1ac7-490e-a323-2cd55b2651f5","added_by":"auto","created_at":"2025-10-31 09:15:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1323093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn vivo detection of externally supplemented DA, AA, and UA levels in the striatum of rats using CNTFE. \u003c/strong\u003e(a) Schematic illustration of the implantation and fixation of CNTFE in the rat striatal region. (b) Schematic of CNTFE hardening via PVP coating to facilitate implantation. (c) Flow chart depicting the injection and detection procedure for DA, AA, and UA at varying concentrations in the striatum, where the arrows indicated the externally supplemented DA, AA, and UA solution into the brain model. (d) Time-dependent DA concentration changes in three SD rats (#1, #2, #3) after sequential injections of DA at three concentration levels (C1, C2, C3), along with the averaged DA response at each level (n = 3, mean ± SEM). (e) Time-dependent AA concentration changes in the same rats following injections of AA at concentrations C1, C2, and C3, with averaged values shown (n = 3, mean ± SEM).\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7541492/v1/ab34b1704f024dc656be97e6.jpg"},{"id":95000512,"identity":"c4105a09-4305-469f-b38e-e6b60b23316f","added_by":"auto","created_at":"2025-11-03 08:58:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10115225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7541492/v1/414fd112-ea7a-46a9-a84e-90395a45027f.pdf"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Sensitive and continuous in situ electrochemical monitoring of multiple neurochemicals using an ultra-soft carbon nanotube fiber sensor","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNeurochemicals play a central role in the precise control of various biological processes (e.g., exercise, metabolism, immunity) as well as in the maintenance of psychological and behavioral homeostasis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Monitoring neurochemical dynamics is essential for understanding the communication between neurons and their targets, as well as for the early diagnosis and treatment of neurological disorders\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Neurochemicals encompass neurotransmitters, hormones, and neuromodulators, which collectively contribute to the dynamic interplay between the brain and the body by mediating signal transmission, modulating physiological functions, and preserving neuroplasticity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In the central nervous system (CNS), dopamine (DA), ascorbic acid (AA) and uric acid (UA) are involved in the regulation of neural activities related to motor control, reward mechanisms and oxidative stress homeostasis\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. DA, as a key neurotransmitter, is widely implicated in multiple CNS functions and its abnormal concentrations are strongly associated with neurological disorders such as Parkinson's, Alzheimer's, and schizophrenia\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. AA, the most abundant water-soluble antioxidant in the brain, plays a crucial role in protecting neurons from lipid peroxidation damage. As a cofactor for tyrosine hydroxylase, AA promotes DA synthesis, and imbalances in its concentration are closely linked to neurodegenerative disease progression\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. UA is the final metabolite of purine metabolism and is commonly used as a metabolite indicator to reflect gout and renal disease, but it is also an important endogenous antioxidant in the nervous system, scavenging reactive oxygen species (e.g., peroxynitrite) and protecting dopaminergic neurons from oxidative stress-induced damage\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Therefore, simultaneous in situ and real-time monitoring of neurochemicals enables comprehensive analysis of neurotransmitter metabolic kinetics, oxidative stress balance, and the synergistic mechanisms of neurodegenerative diseases, thereby providing important information for investigating the pathological mechanisms of neurodegenerative diseases and developing neuromodulatory therapeutic strategies\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNevertheless, the techniques for detecting multiple neurochemicals within the brain remain limited. For the detection of intracerebral neurochemical species, commonly used methods include liquid chromatography, mass spectrometry, microdialysis, molecular fluorescent probes, and electrochemical detection technologies\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Conventional detection methods such as liquid chromatography and mass spectrometry involve complex cerebrospinal fluid extraction steps that limit their application for in situ continuous monitoring\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Although microdialysis allows continuous in vivo sampling by implanting semipermeable membrane probes, its low perfusion flow rate results in poor temporal resolution, limiting the detection of rapid neurochemical fluctuations\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Moreover, the reliance on bulky external perfusion apparatus and post-collection analytical instruments, such as liquid chromatography or mass spectrometry, further restricts its use in real-time monitoring and freely behaving animal models\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The recent development of genetically encoded fluorescence sensors provides significant advantages in terms of sensitivity, selectivity and temporal resolution\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, their application is constrained by limited tissue penetration depth, reliance on optical access (e.g., cranial windows), photobleaching, and susceptibility to signal drift over long-term imaging sessions. In comparison, electrochemical detection methods have emerged as an important tool for in situ monitoring in brain science due to their high sensitivity, rapid response time, and ease of miniaturized integration\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, in practical applications, the concentrations of DA, AA and UA in vivo are relatively low, and their electrochemical signals overlap pose a challenge for simultaneous detection over a wide dynamic range\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, it is still challenging to achieve continuous and selective detection of DA, AA, and UA in the complex brain environment.\u003c/p\u003e\u003cp\u003ePrecious metals are widely utilized as electrode materials for in vivo electrochemical sensing due to their favorable electrochemical activity and low biotoxicity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For instance, Au electrodes have been extensively employed for the long-term monitoring of neurochemicals such as DA and 5-HT, owing to their excellent electrochemical stability, high conductivity, and biocompatibility\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Similarly, Pt electrodes, owing to their intrinsic electrocatalytic activity, have demonstrated good performance in detecting oxidizable biomolecules such as glutamate, lactate, and AA, especially when surface kinetics are optimized to enhance reaction efficiency\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, the rigid mechanical nature of metal electrodes limits their long-term reliability in dynamic brain environments. The mechanical mismatch between stiff electrodes and soft neural tissue may induce micromotion-related damage, immune responses, and signal instability. Moreover, the conductive nature of metals often leads to magnetic susceptibility artifacts in magnetic resonance imaging (MRI), thereby restricting their use in multimodal neuroimaging applications. To address these limitations, various conductive polymer electrodes have been explored for in vivo sensing\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Conductive polymers such as poly(3,4-ethylenedioxythiophene) (PEDOT) are commonly used to modify electrode surfaces, offering enhanced charge injection capacity, reduced impedance, and improved tissue-electrode interface compliance\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Despite these advantages, such materials often suffer from limited long-term electrochemical stability, potential degradation, and complex fabrication processes, posing challenges for chronic implantation\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Carbon-based materials, on the other hand, have emerged as compelling alternatives due to their intrinsic electrical conductivity, chemical stability, and exceptional mechanical flexibility\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Their non-ferromagnetic nature minimizes susceptibility artifacts and signal distortion under strong magnetic fields, ensuring compatibility with MRI and enabling seamless integration with multimodal diagnostic systems\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. These properties make carbon-based electrodes particularly well-suited for implantable neurochemical sensors that demand both biocompatibility and functional stability in real-time, in vivo environments\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHere, we developed an ultra-soft carbon nanotube fiber electrode (CNTFE) platform for real-time, continuous electrochemical monitoring of DA, AA, and UA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-c). The CNTFEs were fabricated via aerosol-assisted floating catalyst chemical vapor deposition (FCCVD), forming continuous fibers composed of entangled carbon nanotube bundles, whose structure was featured with high mechanical flexibility and resilience under various modes of deformation (bending, stretching, and torsion), allowing electrodes to maintain stable electrical and structural integrity under dynamic physiological conditions. We first evaluated the mechanical performance of the CNTFEs, which exhibited excellent mechanical compliance, supporting their long-term operational stability and biocompatibility for chronic implantation. Subsequently, three voltammetric techniques, linear sweep voltammetry (LSV), square wave voltammetry (SWV), and differential pulse voltammetry (DPV), were employed to assess the electrochemical performance of the CNTFEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). In vitro voltammetric measurements revealed high selectivity and sensitivity of CNTFEs toward detection of DA, AA, and UA, outperforming conventional gold fiber microelectrodes (AuFEs). Notably, under DPV conditions, the CNTFEs demonstrated sensitivity enhancements of 140.9-fold for DA detection, 146.2-fold for UA detection, and 43.8-fold for AA detection compared to AuFEs. In vivo experiments involving microelectrode implantation in rats further confirmed the high tissue conformability and mechanical stability of CNTFEs, enabling multiplexed and real-time monitoring of externally supplemented DA and AA in brain model. The CNTFEs-based electrochemical system developed here provided a robust and adaptable platform for multi-analyte detection in complex biofluids. Through combining in vitro and in vivo validation, this work established experimental basis for high-performance neurochemical sensing that might contribute to the development of miniaturized biosensing platforms for neurological applications.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Result and discussion","content":"\u003cp\u003eUltrasoft and highly conductive carbon nanotube fiber electrodes (CNTFEs) featuring a nano-helical bundle architecture were developed for implantable multiple neurochemicals monitoring in brain tissue (\u003cb\u003eFig.\u0026nbsp;1e1\u003c/b\u003e). These CNTFEs were fabricated via aerosol-assisted floating catalyst chemical vapor deposition (FCCVD). Scanning electron microscopy (SEM) images (\u003cb\u003eFig.\u0026nbsp;1e2\u003c/b\u003e) revealed that each CNTFE comprises numerous individual CNTs helically twisted and stacked into a well-defined bundle structure, yielding a typical fiber diameter of 50–100 µm. High-magnification images of the fiber surface showed tightly aligned CNTs forming highly oriented nanofiber bundles. These bundles were densely interwoven, resulting in a corrugated fiber morphology due to entanglement, which significantly increased the mechanical durability and robustness of the microelectrodes.\u003c/p\u003e\u003cp\u003eAs shown in \u003cb\u003eFig.\u0026nbsp;1f1\u003c/b\u003e, the CNTFEs exhibited exceptional softness with ultra-small diameter, minimizing mechanical mismatch and tissue damage upon brain implantation. Bending tests confirmed their outstanding mechanical resilience, as the electrodes maintained structural integrity without notable cracking or deformation even after 500 cycles of 50% bend-relaxation (\u003cb\u003eFig.\u0026nbsp;1f2-4\u003c/b\u003e). These results indicated the high mechanical compliance and stability of CNTFEs in dynamic testing environments. To electrically isolate the active regions and mitigate environmental interference, the CNTFEs were coated with a thin (~ 3 µm thick), uniform polyimide (PI) insulating layer, exposing only the tip with 1 mm area for sensing. SEM imaging confirmed the homogeneity, conformality and thin feature of the insulation coating (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg \u003cb\u003eand h\u003c/b\u003e) which preserved the intrinsic flexibility of the underlying CNT structures. For implantation, a transient hardening strategy was employed by coating the CNTFEs with a water-soluble, biocompatible polyvinylpyrrolidone (PVP) layer. This temporary coating facilitated smooth insertion into brain tissue and rapidly dissolved upon exposure to aqueous environments, thereby restoring the electrodes’ flexibility. This design ensures both surgical precision and long-term functional stability of the implanted CNTFEs in vivo.\u003c/p\u003e\u003cp\u003eWe next evaluated the electrochemical sensing performance of CNTFEs for the detection of DA, AA, and UA using three typical voltammetric techniques. Voltammetric analysis is widely employed for neurochemical monitoring due to its high sensitivity, temporal resolution, and compatibility with in vivo applications. Linear sweep voltammetry (LSV) enables the characterization of redox behavior by applying a continuously varying potential to the working electrode while measuring the resulting current response. Square wave voltammetry (SWV) improves detection sensitivity and resolution by superimposing a symmetric square waveform on a staircase potential, thereby facilitating rapid and precise quantification. Differential pulse voltammetry (DPV) further enhances analytical performance by suppressing background capacitive currents through the application of modulated pulse waveforms, making it particularly effective for detecting low-abundance analytes in complex biological matrices. Each neurochemical species displays a distinct oxidation potential, allowing selective excitation of redox reactions and enables the differentiation of DA, AA, and UA through their characteristic current peaks. To systematically assess the electrochemical performance of CNTFEs, we performed LSV, SWV, and DPV measurements for each type of analyte under identical testing conditions. AuFEs were used in parallel as control electrodes to evaluate the relative performance of CNTFEs. All electrochemical experiments were conducted using a standardized three-electrode configuration, consisting of a CNTFE as the working electrode, a CNTFE as the counter electrode, and a CNTFE-based Ag/AgCl reference electrode (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The use of CNTF components as the base of the three-electrode system ensured material consistency across electrodes and minimized artifacts introduced by heterogeneous electrode compositions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe detection of DA is primarily based on the oxidative character of its catechol moiety, which contains a phenolic hydroxyl group. Upon reaching the oxidation potential at the electrode surface, DA undergoes a two-electron, one-proton oxidation reaction, forming o-dopaminoquinone (DAQ) and generates a measurable anodic current. Although the DA/DAQ redox process is theoretically reversible, the reduction of DAQ is thermodynamically unfavorable under physiological pH conditions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, in most cases, only the oxidative signal is considered in the analysis. Based on this redox mechanism, CNTFEs can effectively capture the transferred charge and convert it into a pronounced increase in oxidation current, thereby enabling sensitive DA detection. For DA detection tests, LSV, SWV, and DPV measurements were conducted using CNTFEs, respectively, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-d. DA solutions with concentrations ranging from 0 to 300 µM were incrementally added into phosphate-buffered saline (PBS), and the corresponding electrochemical responses were recorded using a standard three-electrode configuration.\u003c/p\u003e\u003cp\u003eAs shown in the LSV profiles (\u003cb\u003eFig.\u0026nbsp;2b1\u003c/b\u003e), a distinct oxidation peak appeared near 0.1 V, indicating the occurrence of the oxidative reaction of DA at the CNTFE surface. The peak current increased proportionally with DA concentration, exhibiting a linear relationship with a sensitivity of 0.0374 µA/µM and a correlation coefficient (R²) of 0.9866 (\u003cb\u003eFig.\u0026nbsp;2b2\u003c/b\u003e). In the case of SWV, well-defined oxidation peaks were observed across a DA concentration range of 1-300 µM, yielding a linear calibration curve with a sensitivity of 0.128 µA/µM and R² = 0.9814 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Compared with LSV and SWV, DPV enabled detection of DA at lower concentrations with markedly improved sensitivity. As shown in \u003cb\u003eFig.\u0026nbsp;2d1 and d2\u003c/b\u003e, DPV measurements demonstrated excellent linearity in the low concentration range (0.2-5 µM), with a sensitivity of 0.1127 µA/µM and R² = 0.9921. At higher DA concentrations (10–300 µM), the DPV response exhibited two distinct linear regions, with sensitivities of 0.199 µA/µM (R² = 0.9946) and 0.0678 µA/µM (R² = 0.9602), respectively (\u003cb\u003eFig.\u0026nbsp;2d3 and d4\u003c/b\u003e). These findings collectively indicated that while LSV and SWV offer reasonable performance at higher concentration ranges, DPV provided better sensitivity and a broader dynamic range, especially in the physiologically relevant low-concentration regime. Given that extracellular DA concentrations in the brain typically fall within the 0.011 µM range, CNTFEs combined with DPV detection might be suitable for real-time monitoring of DA under physiological conditions.\u003c/p\u003e\u003cp\u003eTo evaluate the sensitivity and detection limits of CNTFEs, we conducted parallel electrochemical measurements using conventional AuFEs as control group. In LSV measurements, although the current increased with rising DA concentrations, no distinct oxidation peak was observed (\u003cb\u003eFig.\u0026nbsp;2e1\u003c/b\u003e), and the resulting sensitivity was only 0.0026 µA/µM (R² = 0.9967), more than an order of magnitude lower than that observed with CNTFEs (\u003cb\u003eFig.\u0026nbsp;2e2\u003c/b\u003e). In SWV, AuFEs exhibited a weak oxidation peak near 0.3 V, but the calculated sensitivity was merely 0.0008 µA/µM (R² = 0.9963), representing a reduction of over two orders of magnitude compared to CNTFEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Similarly, in DPV measurements, AuFEs yielded a sensitivity of only 0.0009 µA/µM (R² = 0.9930, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg), with a detectable concentration threshold of approximately 10 µM-significantly higher than the ~ 0.2 µM detection limit achieved with CNTFEs. Collectively, these results demonstrated that CNTFEs exhibit markedly better sensitivity and lower detection limits for DA detection across multiple voltammetric techniques. These advantages are especially useful at physiologically relevant concentrations, where CNTFEs outperform AuFEs by two to three orders of magnitude, underscoring their potential for highly sensitive neurochemical monitoring in vivo.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAA, a common and highly electroactive molecule, functions as a potent reducing agent and is readily oxidized under electrochemical conditions. During a positive-going potential sweep, AA undergoes a two-electron, two-proton oxidation to form dehydroascorbic acid (DHAA). At physiological pH, DHAA is unstable and prone to irreversible hydrolysis, rendering the AA/DHAA redox process effectively irreversible\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Based on this electrochemical mechanism, we performed voltammetric detection of AA using CNTFEs. AA was incrementally added to PBS solution in 200 µM steps up to a final concentration of 2 mM, and the corresponding LSV, SWV, and DPV responses were recorded using a standard three-electrode setup. Across the concentration range of 0–2 mM, all three voltammetric methods showed well-defined oxidation peaks centered around − 0.1 V, with current intensity increasing proportionally with AA concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c). Among them, DPV exhibited narrower peak widths and smaller potential shifts, indicating improved resolution over LSV and SWV. Linear calibration plots yielded sensitivities of 0.0244 µM/µA (R² = 0.9948, \u003cb\u003eFig.\u0026nbsp;3a2\u003c/b\u003e) for LSV, 0.0433 µM/µA (R² = 0.9983, \u003cb\u003eFig.\u0026nbsp;3b2\u003c/b\u003e) for SWV, and 0.0129 µM/µA (R² = 0.9990, \u003cb\u003eFig.\u0026nbsp;3c2\u003c/b\u003e), confirming that CNTFEs enable reliable and sensitive AA detection within the 0–2 mM range using all three techniques.\u003c/p\u003e\u003cp\u003eTo further assess the performance of CNTFEs in AA detection, we conducted comparative measurements using AuFEs under identical experimental conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-f). In LSV measurements, although the current increased with increasing AA concentrations, no distinct oxidation peak was observed, and the sensitivity was limited to 0.0024 µA/µM (R² = 0.9824, \u003cb\u003eFig.\u0026nbsp;3d2\u003c/b\u003e). In both SWV and DPV measurements, oxidation peaks were observed near 0.6 V, confirming the occurrence of AA oxidation on the AuFE surface. However, the corresponding sensitivities remained low, measured at 0.0011 µA/µM (R² = 0.9924, \u003cb\u003eFig.\u0026nbsp;3e2\u003c/b\u003e) for SWV and 0.0005 µA/µM (R² = 0.9928, \u003cb\u003eFig.\u0026nbsp;3f2\u003c/b\u003e) for DPV. Notably, the oxidation peak potentials observed for AuFEs were consistently above 0.6 V, in contrast to the significantly lower range of -0.2 to 0 V recorded for CNTFEs. This shift in potential not only reduces the selectivity of AuFEs but also increased the likelihood of interference from other electroactive species during in vivo detection. Furthermore, the oxidation peaks recorded with AuFEs were broader in shape, which compromises resolution and hampers accurate discrimination among coexisting analytes. Taken together, these findings underscored the better electrochemical characteristics of CNTFEs for AA detection, including lower oxidation potentials, sharper peak resolution, and substantially improved sensitivity under physiologically relevant conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe electrochemical detection of UA is based on its irreversible oxidation at the electrode surface. When a positive potential is applied, UA undergoes a two-electron, two-proton oxidation reaction and is ultimately converted into allantoic acid. This redox process allows the electrode to capture the resulting charge transfer, manifesting as a measurable electrochemical response\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. To evaluate the UA sensing performance under different electrochemical conditions, we employed LSV, SWV, and DPV techniques using both CNTFEs and AuFEs. For CNTFEs, UA solutions were incrementally added to PBS buffer in 100 µM steps up to a final concentration of 600 µM, and corresponding voltammetric responses were recorded (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c). In the LSV profiles, a clear oxidation peak was observed, confirming the occurrence of UA oxidation at the CNTFE surface. Although the current increased proportionally with UA concentration, the peak potential shifted markedly toward more positive values, suggesting less stability in the oxidation dynamics. Linear fitting of current versus concentration yielded a sensitivity of 0.0552 µM/µA with a correlation coefficient of R² = 0.9577 (\u003cb\u003eFig.\u0026nbsp;4a2\u003c/b\u003e). Both SWV and DPV responses also exhibited distinct oxidation peaks across the 0–600 µM range, with current intensities increasing in response to UA concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb \u003cb\u003eand c\u003c/b\u003e). Compared with LSV, these methods showed smaller shifts in peak potential and improved signal stability. The calculated sensitivities were 0.0662 µM/µA (R² = 0.9397, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cb\u003e2\u003c/b\u003e) for SWV and 0.0731 µM/µA (R² = 0.8930, \u003cb\u003eFig.\u0026nbsp;4c2\u003c/b\u003e), indicating superior performance of SWV and DPV for UA detection using CNTFEs. These results suggested that, among the tested techniques, SWV and DPV offered greater potential for reliable and stable UA sensing, particularly in dynamic biological environments.\u003c/p\u003e\u003cp\u003eFor comparison, electrochemical detection of UA was also carried out using AuFEs under identical conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-f). In all three methods, oxidation peaks were observed as the UA concentration increased, indicating successful oxidation at the AuFE surface. However, the sensitivities were substantially lower, with values of 0.0021 µM/µA (R² = 0.9758, \u003cb\u003eFig.\u0026nbsp;4d2\u003c/b\u003e) for LSV, 0.0013 µM/µA (R² = 0.9955, \u003cb\u003eFig.\u0026nbsp;4e2\u003c/b\u003e) for SWV, and 0.0005 µM/µA (R² = 0.9928, \u003cb\u003eFig.\u0026nbsp;4f2\u003c/b\u003e) for DPV. Additionally, the oxidation peaks recorded with AuFEs were significantly broader and occurred at potentials above 0.6 V, closely overlapping with those of AA. This lack of peak separation compromised selectivity and made simultaneous detection in complex biological matrices challenging. In contrast, CNTFEs exhibited oxidation peaks for UA consistently around 0.25 V, providing better discrimination from other analytes such as AA. Furthermore, the lower oxidation potential observed with CNTFEs reduces the likelihood of interference from non-target electroactive species. Collectively, these findings highlight the advantages of CNTFEs for UA detection, offering higher sensitivity, improved selectivity, and better compatibility with physiological conditions compared to conventional AuFEs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn electrochemical sensing, key performance indicators such as sensitivity, oxidation potential, detection limit, and selectivity critically determine a sensor's applicability in complex biological environments. To evaluate these parameters systematically, we compared the performance of CNTFEs and AuFEs for the detection of DA, AA, and UA with multiple voltammetric techniques (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, CNTFEs exhibited significantly higher detection sensitivity than AuFEs across all analytes, with the most pronounced enhancement observed for DA-approximately three orders of magnitude greater than that achieved with AuFEs. Notably, DPV on CNTFEs was most suitable for detecting both DA and UA, while SWV offered higher sensitivity in UA detection. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb compared the oxidation peak potentials for each analyte across different electrode types and voltammetric modalities. The peak potentials recorded using AuFEs were consistently elevated, ranging from 0.3 V to 0.8 V, which may compromise selectivity in complex biological matrices due to increased susceptibility to interference from non-target oxidizable species. In contrast, CNTFEs demonstrated significantly lower oxidation potentials, offering improved analyte specificity. As for the detection threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), CNTFEs showed a markedly lower minimum detectable concentration for DA (~ 0.2 µM), compared to ~ 10 µM for AuFEs. For AA and UA, the detection thresholds were comparable between CNTFEs and AuFEs. These findings collectively highlight the superior sensing performance of CNTFEs in terms of sensitivity, electrochemical resolution, and physiological relevance, underscoring their potential for real-time neurochemical monitoring in vivo.\u003c/p\u003e\u003cp\u003eSelectivity reflects the ability to accurately identify the target in the presence of coexisting interferences. High selectivity ensures that the sensor can specifically respond to the target in complex samples, thereby minimizing interference from other substances and avoiding false positive results. The oxidation potentials of DA, UA, and AA are usually similar and may even overlap, which poses considerable difficulty to differentiate these substances for concentration quantification. In this study, CNTFEs were used to simultaneously multiplex detection of DA, UA, and AA. This method distinguished the concentration of each analyte by the peak potential size and the corresponding current value. Here, we verified the selectivity of CNTFE for DA, AA, and UA based on the DPV method, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed-g. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed showed the DPV curve of DA in the concentration range of 0-100 µM in the presence of 300 µM AA and 40 µM UA. It can be found that the response current showed a clear oxidation peak near the potential of 0.1 V and increased with the increase of DA concentration, while the peak potentials of AA and UA remained unchanged near − 0.1 V and 0.25 V. Similarly, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee showed that when the concentrations of DA and AA were constant, the response current increased only around the peak potential of 0.25 V when the concentration of UA increased gradually. Also, when the concentrations of DA and UA were constant, the response current increased gradually around − 0.1 V when the concentration of AA increased gradually (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). The results show that CNTFE can distinguish the substances and concentrations of DA, UA and AA in a solution where they coexist, and has good detection selectivity.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg \u003cb\u003eand h\u003c/b\u003e, an electrochemical voltammetric detection circuit was developed using a data acquisition card and LabView platform, enabling programmable waveform generation and real-time data acquisition. A custom three-electrode circuit module conditioned the analog signals, which were amplified and digitized via high-precision digital-to-analog converter and analog-to-digital converter modules to ensure accurate electrochemical measurements. This circuit supported LSV, SWV, and DPV techniques, with parallel programming architecture allowing simultaneous multi-mode testing and enhanced efficiency. Subsequently, DA was tested in vitro voltammetry based on the constructed multifunctional electrochemical sensing system. The LSV, SWV and DPV response current signals of CNTFEs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei-k. The experimental results show that CNTFEs show regular electrochemical responses under all three detection methods. Among them, the LSV and SWV peak response potentials are close to 0.2 V, and the response current signals are positively correlated with the concentration in the range of 0-200 µM. The peak response potential of DPV is around 0.1 V. In the DA solution with a concentration of 0-150 µM, the current response shows a positive correlation trend with a clear peak. This result shows that it is possible to use homemade circuits to achieve continuous detection of neurochemical substances such as DA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the continuous in vivo sensing performance of CNTFEs, we implanted and secured an integrated CNTFE bundle into the striatal region of live rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The electrode system consisted of two CNTFEs functioning as the working and counter electrodes, respectively, and a third CNTFE modified with Ag/AgCl serving as the reference electrode. Due to the intrinsic softness of CNTFEs, polyvinylpyrrolidone (PVP) was applied as a temporary stiffening agent to facilitate implantation, thereby avoiding tissue damage typically associated with microneedle-assisted insertion techniques (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The PVP coating dissolved within one-minute post-implantation, restoring the original flexibility of the electrodes and ensuring mechanical compatibility with brain tissue. DPV was employed for neurochemical detection in a series of parallel experiments conducted on healthy Sprague-Dawley rats. Implantation was performed under stereotaxic guidance, and the electrodes were fixed using dental cement, leaving a cranial window approximately 1 mm in diameter for subsequent substance delivery.\u003c/p\u003e\u003cp\u003eDA and AA, both with well-established functions in the central nervous system, were selected as representative electroactive targets for evaluating the in vivo sensing performance of CNTFEs, whereas UA was excluded due to its lower brain concentration and limited characterization. Under normal physiological conditions, the basal concentration of DA in brain tissue remains low, with transient spikes typically induced by specific stimuli (e.g., reward anticipation) or occurring under pathological conditions (e.g., Parkinson’ s disease). In anesthetized animals, the suppression of neural activity significantly limits endogenous DA release, making it difficult to elicit electrochemical signals that surpass the detection threshold. To address this limitation and to evaluate the real-time, in situ sensing performance of the CNTFE system, exogenous DA was locally administered to simulate transient neurotransmitter fluctuations. To minimize mechanical trauma to deep brain structures, the injection needle was positioned just above the striatal region, allowing DA to gradually diffuse into the striatum rather than being directly injected into it. This strategy avoids secondary injury caused by direct penetration into the target nucleus while still enabling localized delivery of the analyte. Given the high diffusivity of DA within the brain interstitial environment, its concentration at the site of action rapidly declines post-injection. To compensate for this effect and ensure measurable electrochemical responses, DA solutions at relatively high concentrations were used, acknowledging that the actual concentration reaching the striatal tissue is considerably lower due to diffusion. A comparable protocol was adopted for AA, which is typically present at low levels in healthy brain tissue and are only elevated in pathological conditions such as oxidative stress or metabolic dysregulation. Since the primary aim of these in vivo experiments was to validate the detection capability of CNTFEs under biologically relevant conditions, rather than to replicate physiological concentrations, AA was likewise administered via controlled local injections. It is important to note that the injected concentrations exceed typical endogenous levels and were deliberately chosen to generate robust, quantifiable signals for sensor performance evaluation. Additionally, due to the lack of established in vivo reference methods for determining precise concentrations of DA and AA in brain tissue, the electrochemical outputs were presented as peak current responses. These measurements reflect relative temporal changes in the local concentration of analytes following injection. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, DA was delivered in 20 µL aliquots at three concentrations (C1: 1 mM, C2: 10 mM, C3: 50 mM) with 30-minute intervals between injections to ensure adequate diffusion and partial metabolic clearance. Six electrochemical measurements were taken at each concentration level. The same injection strategy was used for AA. Dose-dependent electrochemical responses to DA (C1-C3) were recorded in three SD rats, with injection time points marked by arrows. Across all animals, the mean detected DA current response increased with injection dose, reaching 3.16 ± 2.36 µA for C1, 4.66 ± 2.73 µA for C2, and 9.49 ± 4.18 µA for C3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). While intra-individual fluctuations were minimal, noticeable inter-individual variability was observed. For instance, Rat #3 exhibited significantly higher DA levels, likely reflecting differences in DA diffusion dynamics, metabolic turnover, or electrode–tissue interactions. Although the in vivo detection experiments are performed by externally supplementing DA to mimic the release of DA in vivo, these findings demonstrated that the capability of CNTF electrode bundle enables reliable, responsive detection of DA in situ.\u003c/p\u003e\u003cp\u003eSimilarly, in vivo AA sensing results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed. Following intracranial administration of AA at C1 (1 mM), C2 (10 mM), and C3 (100 mM), all three animals exhibited concentration-dependent increases in the detected AA levels. The averaged concentrations across all rats were 13.50 ± 12.51 µA (C1), 13.49 ± 12.47 µA (C2), and 17.84 ± 13.36 µA (C3). Notably, AA responses plateaued between C1 and C2 but surged at C3, suggesting a nonlinear signal amplification effect, potentially due to local molecular accumulation or saturation of oxidation kinetics. Individual response profiles further supported this interpretation: Rat #1 showed a gradual rise, Rat #2 exhibited elevated and highly sensitive responses with larger fluctuations, Rat #3 maintained moderate increases across doses. Despite these differences, the CNTFEs consistently generated reproducible, concentration-responsive signals, underscoring their suitability for continuous AA monitoring in vivo.\u003c/p\u003e\u003cp\u003eCollectively, these in vivo results suggested the CNTFE as a reliable platform for real-time and in situ monitoring of DA and AA within the striatum. Due to the anesthetized state of rats, we were unable to measure sufficient concentrations of DA and AA in the normal physiological state of the rat brain. Although we used external injection for DA and AA sensing based CNTFE in a brain model, the system demonstrated robust electrochemical performance across analytes measurement in the brain enviroment.\u003c/p\u003e"},{"header":"Conclusion and discussion","content":"\u003cp\u003eIn this study, we developed an ultra-soft CNTFE platform capable of achieving sensitive, real-time, and continuous monitoring of DA, AA, and UA. The CNTFEs exhibited reliable electrochemical characteristics, including sharp and well-resolved oxidation peaks, low oxidation potentials, and strong selectivity across various voltammetric techniques such as LSV, SWV, and DPV. Compared with conventional AuFEs, the CNTFEs demonstrated significantly enhanced sensitivity, particularly for DA, where the improvement reached nearly three orders of magnitude. In addition, they retained mechanical flexibility and biocompatibility, making them particularly suitable for stable and long-term operation within delicate neural tissues. Under DPV conditions, the CNTFEs achieved detection sensitivities of 0.1127 µM/µA for DA detection, 0.0219 µM/µA for AA detection, and 0.0731 µM/µA for UA detection. To support the sensing functionality of the electrodes, we constructed a multifunctional electrochemical system that integrates waveform generation, signal acquisition, and real-time data visualization. The modular design of this system could support the concurrent operation of multiple voltammetric modes, thereby providing a flexible and efficient platform for electrochemical testing. In vivo experiments further validated the practical utility of the CNTFE for direct intracerebral chemical sensing. By temporarily stiffening the electrodes with a water-soluble polymer (PVP), we achieved smooth implantation into the striatum without relying on rigid carriers or microneedle-based techniques. After implanted, the electrodes rapidly regained their softness, enabling intimate and stable contact with the surrounding brain tissue. To overcome the difficulty of detecting low concentrations of DA and AA in the brain of anesthetized rats in natural physiological state, real-time measurements of externally supplemented DA and AA solutions were performed in the brain of anesthetized rats, and repeated trials showed a consistent signal pattern. This work presents a soft and biocompatible electrochemical sensing strategy that enables implantation and longitudinal monitoring of multiple neurochemical species in the brain, which is promising as tool to access the biochemical concentrations for in vivo applications.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003ePreparation of CNTFEs\u003c/b\u003e. The CNTFEs were ordered from Nanjing Ji Cang Nano Technology Co., Ltd., and the preparation procedure is based on aerosol-assisted floating catalyst chemical vapor deposition (FCCVD). \u003cem\u003eN\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e-Dimethylformamide (DMF) and Polyimide (PI) were purchased from Shanghai Macklin Biochemical Technology Co., Ltd..1 mL of DMF was mixed with 0.6 g of PI powder and stirred thoroughly at room temperature to obtain a dark red viscous solution. For the insulating process to the electrodes, the two ends of microfilament electrodes were fixed on a hollow shelf using clamps to make the electrodes remain in an upright position. And with the help of the dust-free cotton swab, the mixture was slowly applied to the surface of the electrodes until a dense, uniform insulating coating was formed, and an area of approximately 1 mm was exposed at the top of the electrodes for sensing. The above microfilament electrodes were hung and placed in an oven at 40 ℃ for 6 hours to fully cure the insulating layer to obtain insulated electrodes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMechanical Bending and Durability Testing.\u003c/b\u003e The CNTFE was subjected to cyclic bending using a reciprocating tensile testing platform. Both ends of the CNTFE were fixed to the platform fixture, leaving a 2 cm segment as the effective bending section. The platform operated at a constant speed of 2 mm/s with a displacement amplitude of 1 cm, maintaining a maximum bending strain of 50% over 500 cycles. After testing, the electrode surface morphology was examined using a scanning electron microscope (SEM) to evaluate potential cracking or deformation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTemporary Stiffening of Electrodes.\u003c/b\u003e Polyvinyl pyrrolidone (PVP) was purchased from Shanghai Macklin Biochemical Technology Co., Ltd., and a 10% W/V PVP solution was prepared using ethanol as solvent. The PVP solution was uniformly sprayed on the surface of AuFEs and CNTFEs using a spray bottle, and the process was repeated 5 times. The electrodes were dried at room temperature for 2 hours to completely eliminate the solvent, resulting in hardened sensing electrodes that could penetrate brain tissue.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurface Morphology Characterization.\u003c/b\u003e Optical microscopy (MF52-LED, Mshot) was used to characterize the electrode profile. Scanning electron microscope (Phenom Pro Desktop SEM, Thermal Fisher Scientific) was used to characterize the micro-morphology of the surface materials of the sensing electrodes at different stages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eElectrochemical Characterization.\u003c/b\u003e Electrochemical measurements of CNTFEs and AuFEs (Nanjing Ji Cang Nano Technology Co., Ltd.) were performed using a CHI660e electrochemical workstation in a standard three-electrode configuration, with a commercial Ag/AgCl electrode as the reference electrode, a platinum wire as the counter electrode, and CNTFEs or AuFEs as the working electrodes. Three voltammetric techniques were employed: LSV, SWV, and DPV. LSV was conducted over a potential range of -0.3 V to 0.6 V with a scan rate of 0.1 V/s and a sampling interval of 1 mV. SWV was performed within the same potential window, using a voltage increment of 4 mV, an amplitude of 25 mV, and a frequency of 4 Hz. For DPV, the potential range was set to -0.3 V to 0.6 V, with a voltage increment of 4 mV, pulse width of 0.05 s, amplitude of 50 mV, sampling interval of 0.02 s, and pulse period of 0.5 s. For analyte detection, DA, AA, and UA were introduced into PBS in a stepwise concentration gradient. Before each measurement, the solution was stirred for 30 s and then allowed to stand for 30 s to ensure homogeneity and stability. Selectivity tests were performed using DPV under the same conditions as above. For DA selectivity, the concentrations of AA and UA were fixed at 300 \u0026micro;M and 40 \u0026micro;M, respectively, while DA was incrementally increased from 0 to 100 \u0026micro;M. For UA selectivity, UA concentration was varied from 0 to 300 \u0026micro;M in a background containing 20 \u0026micro;M DA and 300 \u0026micro;M AA. For AA selectivity, AA was added in increments to a solution containing 20 \u0026micro;M DA and 40 \u0026micro;M UA, resulting in a concentration range of 0 to 2000 \u0026micro;M. After each addition, the solution was stirred for 30 s and rested for 30 s before DPV measurements.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign of Custom Sensing Circuitry.\u003c/b\u003e An electrochemical pulse voltammetric detection system was implemented utilizing a National Instruments (NI) data acquisition (DAQ) card. A programmable waveform sequence was generated using the LabView software development platform, which is subsequently output as a precise analog voltage waveform via the integrated digital-to-analog converter (DAC) of the NI DAQ card. Further, this analog voltage waveform was signal-conditioned by a custom-designed three-electrode circuit module to optimize electrochemical test parameters. For data acquisition, the three-electrode circuit collected electrochemical signals, which were then amplified by a transimpedance amplifier and fed into the high-precision analog-to-digital converter (ADC) module of the NI DAQ card. Real-time data processing and visualization were performed using the LabView software. This multifunctional electrochemical sensing system supports various voltammetric techniques, including LSV, SWV and DPV. Moreover, the programming adopts the parallel structure, which makes this electrochemical work sensing system can realize the different testing methods at the same time, and improves the efficiency of electrochemical testing. Waveform generation and data processing are performed using high-precision DAC and ADC modules within the NI DAQ card to ensure data stability and accuracy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnimal studies.\u003c/b\u003e The experimental protocols related to the in vivo rats in this work were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University and Guangzhou Boyao Biotechnology Co. The designed experiments were carried out in full accordance with the guidelines for the care and use of laboratory animals by the Institutional Animal Care and Use Committee of Sun Yat-sen University (No. SYSU-IACUC-2023-001854) and Guangzhou Boyao Biotechnology Co (No. IAEC-K-240925-01). The SD rats used in the experiments weighed 220\u0026ndash;250 g and were about 8 weeks old, purchased from Guangzhou Boyao Biotechnology Co. and continued to be housed for one week after purchase to adaptive the surrounding environment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn Vivo Neurochemical Detection Using CNTFEs.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdult Sprague-Dawley (SD) rats were anesthetized via intramuscular injection of Zoletil (50 mg/kg) and fixed in a stereotaxic frame. After a midline scalp incision, the skull was exposed and leveled to ensure accurate targeting. A circular craniotomy (~\u0026thinsp;5 mm diameter) was performed above the striatal region using a surgical drill. Integrated CNTFE bundles were stereotaxically implanted into the striatum at the following coordinates: anteroposterior (AP)\u0026thinsp;+\u0026thinsp;0.7 mm, mediolateral (ML)\u0026thinsp;+\u0026thinsp;3.0 mm, and dorsoventral (DV) -4.0 mm. The electrodes were secured with dental cement (Minnesota Mining and Manufacturing), leaving a\u0026thinsp;~\u0026thinsp;1 mm opening to allow for solution delivery. The implanted CNTFEs were connected to an external electrochemical workstation for differential pulse voltammetry (DPV) recording.\u003c/p\u003e\u003cp\u003eConsidering that the basal concentrations of DA and AA in brain tissues are usually maintained at low levels in physiological states, the concentrations generated by endogenous release is difficult to meet the electrochemical detection threshold. Therefore, in this study, we validated the performance of CNTFE for in situ real-time monitoring by injecting external supplemental solution at a constant rate of 1 \u0026micro;L/min via a micro syringe pump into the striatal brain region at a total volume of 20 \u0026micro;L to detect neurochemicals. It should be noted that, considering the high-speed diffusion characteristics of brain interstitial fluid, the concentration of neurochemicals at the injection site can decay rapidly after drug injection. Therefore, we used a gradient concentration progression injection strategy to simulate the fluctuation of neurochemicals in a real physiological environment.\u003c/p\u003e\u003cp\u003eFollowing each injection, electrochemical recordings were initiated immediately. In the DA sensing protocol, three concentrations (1 mM, 10 mM, and 50 mM) were prepared in PBS and sequentially administered from low to high, with each concentration considered as a distinct measurement cycle. For each concentration, DPV signals were collected at 5-minute intervals over a 30-minute recording window. The same procedure was applied for AA detection, using concentrations of 1 mM, 10 mM, and 100 mM. Upon completion of measurements for a given analyte, the cranial cavity was thoroughly flushed with PBS or sterile saline to remove residual compounds. The solution was then aspirated to minimize cross-contamination before proceeding to the next neurochemical trial.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eXiaotong Li, Mengyi He and Xinshuo Huang contributed equally.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge financial support from the National Natural Science Foundation of China (Grant No. T2225010, 32171399, 32171456, 32401202), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023A1515111139, 2025A1515010608), Science and Technology Program of Guangzhou, China (Grant No. 2024B03J0121, 2024B03J1284), Shenzhen Science and Technology Program (Grant No. RCBS20231211090558093), Fundamental Research Funds for the Central Universities, Sun Yat-sen University (No. 24xkjc011), the Opening Project of The National Key Laboratory of Smart Farm Technology and Systems, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University (SKLACLS2502).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHe, J. et al. 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Tailoring Diffusional Fields in Zwitterion/Dopamine Copolymer Electropolymerized at Carbon Nanowalls for Sensitive Recognition of Neurotransmitters. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 13183-13198 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"microsystems-and-nanoengineering","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"micronano","sideBox":"Learn more about [Microsystems \u0026 Nanoengineering](http://www.nature.com/micronano/)","snPcode":"41378","submissionUrl":"https://mts-micronano.nature.com/","title":"Microsystems \u0026 Nanoengineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Carbon nanotube fiber, electrochemical biosensing, multifunctional sensing system, in situ neurochemical monitoring, in vivo sensing","lastPublishedDoi":"10.21203/rs.3.rs-7541492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7541492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeurochemicals, such as dopamine (DA), ascorbic acid (AA), and uric acid (UA) serve as critical modulators of neural circuit dynamics in both the central and peripheral nervous systems, and their real-time, in situ monitoring is essential for elucidating neural function and facilitating early diagnosis of neurological disorders. However, conventional in vivo mon\u003c/p\u003e\u003cp\u003eitoring remains limited by the mechanical mismatch between rigid electrodes and soft neural tissue, as well as the electrochemical detection sensitivity of the electrodes. In this study, we develop a sensitive and multifunctional electrochemical sensing platform based on ultra-soft carbon nanotube fiber electrodes (CNTFEs), which integrate microelectrode with programmable pulsed voltammetric detection for the multiplexed sensing of DA, UA, and AA. Systematic electrochemical evaluations demonstrated that CNTFEs exhibit significantly enhanced sensitivity, compared with conventional gold fiber microelectrodes, with sensitivity improvements of 141-fold for DA detection, 146-fold for UA detection, and 44-fold for AA detection under DPV. In vivo validation via implantation of CNTFEs in the rat striatum demonstrated the sensor's capability for real-time, in-situ detection of externally supplemented DA and AA in brain model. These findings highlight the potential of ultra-soft CNTFE-based sensors for minimally invasive neurochemical monitoring, providing potential opportunity for development of implantable biosensing technologies toward personalized diagnostics in neurological disorders.\u003c/p\u003e","manuscriptTitle":"Sensitive and continuous in situ electrochemical monitoring of multiple neurochemicals using an ultra-soft carbon nanotube fiber sensor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 09:15:46","doi":"10.21203/rs.3.rs-7541492/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-11-11T09:03:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-11T04:25:43+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-04T03:44:31+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-10-29T09:58:02+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-10-28T06:40:39+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-10-24T04:02:44+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-10-24T03:44:49+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-10-21T07:22:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T06:46:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T06:54:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microsystems \u0026 Nanoengineering","date":"2025-09-05T06:54:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microsystems-and-nanoengineering","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"micronano","sideBox":"Learn more about [Microsystems \u0026 Nanoengineering](http://www.nature.com/micronano/)","snPcode":"41378","submissionUrl":"https://mts-micronano.nature.com/","title":"Microsystems \u0026 Nanoengineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09e5982d-e323-4aeb-952b-45ef315ec149","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":56621277,"name":"Physical sciences/Nanoscience and technology/Nanobiotechnology/Biosensors"},{"id":56621278,"name":"Physical sciences/Nanoscience and technology/Nanoscale devices"}],"tags":[],"updatedAt":"2025-11-11T09:07:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 09:15:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7541492","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7541492","identity":"rs-7541492","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}