Deep Learning for Voltammetric Sensing in a Living Animal Brain

Controllable radical polymerization of TEMPO redox for stable and sensitive enzyme electrode interface

Assembling functional molecules on the surface of an enzyme electrode is the most basic technique for constructing a biosensor. However, precise control of electron transfer interface or electron mediator on the electrode surface remains a challenge, which is a key step that affects the stability and sensitivity of enzyme-based biosensors. In this study, we propose the use of controllable free radical polymerization to grow stable 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) polymer as electron mediator on enzyme surface for the first time. Through scanning electron microscopy (SEM), Raman spectroscopy, electrode surface coverage measurement, static contact angle (SCA), and a series of electrochemical methods, it has been demonstrated that the TEMPO-based enzyme electrode exhibits a uniform hydrophilic morphology and stable electrochemical performance. Furthermore, the results show that the sensor demonstrates high sensitivity for detecting glucose biomolecules in artificial sweat and serum. Attributing to the quantitative and controllable radical polymerization of TEMPO redox assembled enzyme electrode surface, the as-proposed biosensor providing a use, storage, and inter-batch sensing stability, providing a vital platform for wearable/implantable biochemical sensors.

Whole blood multiplex measurements using electrochemical aptamer-based biosensors

Simultaneous measurements of various molecules ("multiplex") using electrochemical biosensors typically require multiple electrode implementations, which for neonates, hemophiliacs, etc. is problematic. Here, we introduce the oxazine ATTO 700 into electrochemical aptamer-based biosensors to achieve "true" multiplex, continuous and real-time measurements of two different molecules in undiluted whole blood using a single electrode.

A PEDOT: PSS/GO fiber microelectrode fabricated by microfluidic spinning for dopamine detection in human serum and PC12 cells

Rapid and accurate in situ determination of dopamine is of great significance in the study of neurological diseases. In this work, poly (3,4-ethylenedioxythiophene): poly (styrenesulfonic acid) (PEDOT: PSS)/graphene oxide (GO) fibers were fabricated by an effective method based on microfluidic wet spinning technology. The composite microfibers with stratified and dense arrangement were continuously prepared by injecting PEDOT: PSS and GO dispersion solutions into a microfluidic chip. PEDOT: PSS/GO fiber microelectrodes with high electrochemical activity and enhanced electrochemical oxidation activity of dopamine were constructed by controlling the structure composition of the microfibers with varying flow rate. The fabricated fiber microelectrode had a low detection limit (4.56 nM) and wide detection range (0.01-8.0 µM) for dopamine detection with excellent stability, repeatability, and reproducibility. In addition, the PEDOT: PSS/GO fiber microelectrode prepared was successfully used for the detection of dopamine in human serum and PC12 cells. The strategy for the fabrication of multi-component fiber microelectrodes is a new and effective approach for monitoring the intercellular neurotransmitter dopamine and has high potential as an implantable neural microelectrode.

Self-assembled ordered AuNRs-modified electrodes for simultaneous determination of dopamine and topotecan with improved data reproducibility

Gold nanomaterials have been widely explored in electrochemical sensors due to their high catalytic property and good stability in multi-medium. In this paper, the reproducibility of the signal among batches of gold nanorods (AuNRs)-modified electrodes was investigated to improve the data stabilization and repeatability. Ordered and random self-assembled AuNRs-modified electrodes were used as electrochemical sensors for the simultaneous determination of dopamine (DA) and topotecan (TPC), with the aim of obtaining an improved signal stability in batches of electrodes and realizing the simultaneous determination of both substances. The morphology and structure of the assemblies were analyzed and characterized by UV-Vis spectra, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray powder diffraction (XRD). Electrochemical studies showed that the ordered AuNRs/ITO electrodes have excellent signal reproducibility among several individuals due to the homogeneous mass transfer in the ordered arrangement of the AuNRs. Under the optimized conditions, the simultaneous detection results of DA and TPC showed good linearity in the ranges 1.75-45 μM and 1.5-40 μM, and the detection limits of DA and TPC were 0.06 μM and 0.17 μM, respectively. The results showed that the prepared ordered AuNR/ITO electrode had high sensitivity, long-term stability, and reproducibility for the simultaneous determination of DA and TPC, and it was expected to be applicable for real sample testing.

Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis

Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with "hotspots" generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.

Facile Synthesis of Fe-Doped, Algae Residue-Derived Carbon Aerogels for Electrochemical Dopamine Biosensors

An abnormal level of dopamine (DA), a kind of neurotransmitter, correlates with a series of diseases, including Parkinson's disease, Willis-Ekbom disease, attention deficit hyperactivity disorder, and schizophrenia. Hence, it is imperative to achieve a precise, rapid detection method in clinical medicine. In this study, we synthesized nanocomposite carbon aerogels (CAs) doped with iron and iron carbide, based on algae residue-derived biomass materials, using Fe(NO3)3 as the iron source. The modified glassy carbon electrode (GCE) for DA detection, denoted as CAs-Fe/GCE, was prepared through surface modification with this composite material. X-ray photoelectron spectroscopy and X-ray diffraction characterization confirmed the successful doping of iron into the as-prepared CAs. Additionally, the electrochemical behavior of DA on the modified electrode surface was investigated and the results demonstrate that the addition of the CAs-Fe promoted the electron transfer rate, thereby enhancing their sensing performance. The fabricated electrochemical DA biosensor exhibits an accurate detection of DA in the concentration within the range of 0.01~200 µM, with a detection limit of 0.0033 µM. Furthermore, the proposed biosensor is validated in real samples, showing its high applicability for the detection of DA in beverages.

Maximizing Electrochemical Information: A Perspective on Background-Inclusive Fast Voltammetry

This perspective encompasses a focused review of the literature leading to a tipping point in electroanalytical chemistry. We tie together the threads of a "revolution" quietly in the making for years through the work of many authors. Long-held misconceptions about the use of background subtraction in fast voltammetry are addressed. We lay out future advantages that accompany background-inclusive voltammetry, particularly when paired with modern machine-learning algorithms for data analysis.

The Role of Catecholamines in the Pathogenesis of Diseases and the Modified Electrodes for Electrochemical Detection of Catecholamines: A Review

Catecholamines (CAs), which include adrenaline, noradrenaline, and dopamine, are neurotransmitters and hormones that critically regulate the cardiovascular system, metabolism, and stress response in the human body. The abnormal levels of these molecules can lead to the development of various diseases, including pheochromocytoma and paragangliomas, Alzheimer's disease, and Takotsubo cardiomyopathy. Due to their low cost, high sensitivity, flexible detection strategies, ease of integration, and miniaturization, electrochemical techniques have been extensively employed in the detection of CAs, surpassing traditional analytical methods. Electrochemical detection of CAs in real samples is challenging due to the tendency of poisoning electrode. Chemically modified electrodes have been widely used to solve the problems of poor sensitivity and selectivity faced by bare electrodes. There are a few articles that provide an overview of electrochemical detection and efficient enrichment of CAs, but there is a dearth of updates on the role of CAs in the pathogenesis of diseases. Additionally, there is still a lack of systematic synthesis with a focus on modified electrodes for electrochemical detection. Thus, this review provides a summary of the recent clinical pathogenesis of CAs and the modified electrodes for electrochemical detection of CAs published between 2017 and 2022. Moreover, challenges and future perspectives are also highlighted. This work is expected to provide useful guidance to researchers entering this interdisciplinary field, promoting further development of CAs pathogenesis, and developing more novel chemically modified electrodes for the detection of CAs.

Ion-Selective Micropipette Sensor for In Vivo Monitoring of Sodium Ion with Crown Ether-Encapsulated Metal-Organic Framework Subnanopores

In vivo sensing of the dynamics of ions with high selectivity is essential for gaining molecular insights into numerous physiological and pathological processes. In this work, we report an ion-selective micropipette sensor (ISMS) through the integration of functional crown ether-encapsulated metal-organic frameworks (MOFs) synthesized in situ within the micropipette tip. The ISMS features distinctive sodium ion (Na+) conduction and high selectivity toward Na+ sensing. The selectivity is attributed to the synergistic effects of subnanoconfined space and the specific coordination of 18-crown-6 toward potassium ions (K+), which largely increase the steric hindrance and transport resistance for K+ to pass through the ISMS. Furthermore, the ISMS exhibits high stability and sensitivity, facilitating real-time monitoring of Na+ dynamics in the living rat brain during spreading of the depression events process. In light of the diversity of crown ethers and MOFs, we believe this study paves the way for a nanofluidic platform for in vivo sensing and neuromorphic electrochemical sensing.

Explainable Deep Learning-Assisted Self-Calibrating Colorimetric Patches for In Situ Sweat Analysis

Sweat has emerged as a compelling analyte for noninvasive biosensing technology because it contains a wealth of important biomarkers in hormones, organic biomacromolecules, and various ionic mixtures. These components offer valuable insights and can reflect an individual's physiological conditions. Here, we introduced an explainable deep learning (DL)-assisted wearable self-calibrating colorimetric biosensing analysis platform to efficiently and precisely detect the biomarker's concentration in sweat. Specifically, we have integrated the advantages of the colorimetric sensing method, adsorbing-swelling hydrogel, and explainable DL algorithms to develop an enzyme/indicator-immobilized colorimetric patch, which has reliable colorimetric sensing ability and excellent adsorbing-swelling function. A total of 5625 colorimetric images were collected as the analysis data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Zn2+, glucose, and Ca2+ in human sweats could be facilely classified and quantified with 100% accuracy via the convolutional neural network (CNN) model, and the testing results of actual sweats via the DL-assisted colorimetric approach are 91.7-97.2% matching with the classical UV-vis spectrum. Class activation mapping (CAM) was utilized to visualize the inner working mechanism of CNN operation, which contributes to verify and explicate the design rationality of the noninvasive biosensing technology. An "end-to-end" model was established to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for health monitoring, disease prevention, and clinical diagnosis.

Challenges and strategies faced in the electrochemical biosensing analysis of neurochemicals in vivo: A review

Our brain is an intricate neuromodulatory network, and various neurochemicals, including neurotransmitters, neuromodulators, gases, ions, and energy metabolites, play important roles in regulating normal brain function. Abnormal release or imbalance of these substances will lead to various diseases such as Parkinson's and Alzheimer's diseases, therefore, in situ and real-time analysis of neurochemical interactions in pathophysiological conditions is beneficial to facilitate our understanding of brain function. Implantable electrochemical biosensors are capable of monitoring neurochemical signals in real time in extracellular fluid of specific brain regions because they can provide excellent temporal and spatial resolution. However, in vivo electrochemical biosensing analysis mainly faces the following challenges: First, foreign body reactions induced by microelectrode implantation, non-specific adsorption of proteins and redox products, and aggregation of glial cells, which will cause irreversible degradation of performance such as stability and sensitivity of the microsensor and eventually lead to signal loss; Second, various neurochemicals coexist in the complex brain environment, and electroactive substances with similar formal potentials interfere with each other. Therefore, it is a great challenge to design recognition molecules and tailor functional surfaces to develop in vivo electrochemical biosensors with high selectivity. Here, we take the above challenges as a starting point and detail the basic design principles for improving in vivo stability, selectivity and sensitivity of microsensors through some specific functionalized surface strategies as case studies. At the same time, we summarize surface modification strategies for in vivo electrochemical biosensing analysis of some important neurochemicals for researchers' reference. In addition, we also focus on the electrochemical detection of low basal concentrations of neurochemicals in vivo via amperometric waveform techniques, as well as the stability and biocompatibility of reference electrodes during long-term sensing, and provide an outlook on the future direction of in vivo electrochemical neurosensing.

Machine Learning-Assisted Automatically Electrochemical Addressable Cytosensing Arrays for Anticancer Drug Screening

The high-throughput and accurate screening of anticancer drugs is crucial to the preclinical assessment of candidate drugs and remains challenging. Herein, an automatically electrochemical addressable cytosensor (AEAC) for the efficient screening of anticancer drugs is reported. This sensor consists of sectionalized laser-induced graphene arrays decorated by the rhombohedral TiO2 and spherical Pt nanoparticles (LIG-TiO2-Pt) with high electrocatalytic activity for H2O2 and a homemade Ag/Pt electrode couple fixed onto the robot arm. The immobilization of laminin on the surface of LIG-TiO2-Pt can promote its biocompatibility for the growth and proliferation of various tumor cells, which empowers the in situ monitoring of H2O2 directly released from these live cells for drug screening. A machine learning (ML) algorithm is employed to eliminate the possible random or systematic errors of AEAC, realizing rapid, high-throughput, and accurate prediction of different types of anticancer drugs. This ML-assisted AEAC provides a powerful approach to accelerate the evolution of sensing-served tumor therapy.

New Opportunities of Electrochemistry for Monitoring, Modulating, and Mimicking the Brain Signals

In vivo electrochemistry is a powerful key for unlocking the chemical consequences in neural networks of the brain. The past half-century has witnessed the technology revolutionization in this field along with innovations in electrochemical concepts, principles, methods, and devices. Present applications of electrochemical approaches have extended from measuring neurochemical concentrations to modulating and mimicking brain signals. In this Perspective, newly reported strategies for tackling long-standing challenges of in vivo electrochemical brain monitoring (i.e., basal level measurement, electroactivity dependence, in vivo stability, neuron compatibility, multiplexity, and implantable device fabrication) are highlighted. Moreover, recent progress on neuromodulation tools and neuromorphic devices in electrochemical frameworks is introduced. A glimpse of future opportunities for electrochemistry in brain research is offered at last.

Electrochemical Biosensors for Whole Blood Analysis: Recent Progress, Challenges, and Future Perspectives

Whole blood, as one of the most significant biological fluids, provides critical information for health management and disease monitoring. Over the past 10 years, advances in nanotechnology, microfluidics, and biomarker research have spurred the development of powerful miniaturized diagnostic systems for whole blood testing toward the goal of disease monitoring and treatment. Among the techniques employed for whole-blood diagnostics, electrochemical biosensors, as known to be rapid, sensitive, capable of miniaturization, reagentless and washing free, become a class of emerging technology to achieve the target detection specifically and directly in complex media, e.g., whole blood or even in the living body. Here we are aiming to provide a comprehensive review to summarize advances over the past decade in the development of electrochemical sensors for whole blood analysis. Further, we address the remaining challenges and opportunities to integrate electrochemical sensing platforms.

A Novel Poly(3-hexylthiophene) Engineered Interface for Electrochemical Monitoring of Ascorbic Acid During the Occurrence of Glutamate-Induced Brain Cytotoxic Edemas

Although neuroelectrochemical sensing technology offers unique benefits for neuroscience research, its application is limited by substantial interference in complex brain environments while ensuring biosafety requirements. In this study, we introduced poly(3-hexylthiophene) (P3HT) and nitrogen-doped multiwalled carbon nanotubes (N-MWCNTs) to construct a composite membrane-modified carbon fiber microelectrode (CFME/P3HT-N-MWCNTs) for ascorbic acid (AA) detection. The microelectrode presented good linearity, selectivity, stability, antifouling, and biocompatibility and exhibited great performance for application in neuroelectrochemical sensing. Subsequently, we applied CFME/P3HT-N-MWCNTs to monitor AA release from in vitro nerve cells, ex vivo brain slices, and in vivo living rat brains and determined that glutamate can induce cell edema and AA release. We also found that glutamate activated the N-methyl-d-aspartic acid receptor, which enhanced Na⁺ and Cl^- inflow to induce osmotic stress, resulting in cytotoxic edema and ultimately AA release. This study is the first to observe the process of glutamate-induced brain cytotoxic edema with AA release and to reveal the mechanism. Our work can benefit the application of P3HT in in vivo implant microelectrode construction to monitor neurochemicals, understand the molecular basis of nervous system diseases, and discover certain biomarkers of brain diseases.

Stability Enhancement of Galvanic Redox Potentiometry by Optimizing the Redox Couple in Counterpart Poles

Potentiometry based on the galvanic cell mechanism, i.e., galvanic redox potentiometry (GRP), has recently emerged as a new tool for in vivo neurochemical sensing with high neuronal compatibility and good sensing property. However, the stability of open circuit voltage (E_OC) outputting remains to be further improved for in vivo sensing application. In this study, we find that the E_OC stability could be enhanced by adjusting the sort and the concentration ratio of the redox couple in the counterpart pole (i.e., indicating electrode) of GRP. With dopamine (DA) as the sensing target, we construct a spontaneously powered single-electrode-based GRP sensor (GRP_2.0) and investigate the correlation between the stability and the redox couple used in the counterpart pole. Theoretical consideration suggests that the E_OC drift is minimum when the concentration ratio of the oxidized form (O₁) to the reduced form (R₁) of the redox species in the backfilled solution is 1:1. The experimental results demonstrate that, compared with other redox species (i.e., dissolved O₂ at 3 M KCl, potassium ferricyanide (K₃Fe(CN)₆), and hexaammineruthenium(III) chloride (Ru(NH₃)₆Cl₃)) used as the counterpart pole, potassium hexachloroiridate(IV) (K₂IrCl₆) exhibits better chemical stability and outputs more stable E_OC. As a result, when IrCl₆^2-/3- with the concentration ratio of 1:1 is used as the counterpart, GRP_2.0 displays not only an excellent E_OC stability (i.e., 3.8 mV drifting during 2200 s for in vivo recording) but also small electrode-to-electrode variation (i.e., the maximum E_OC variation between four electrodes is 2.7 mV). Upon integration with the electrophysiology, GRP_2.0 records a robust DA release, accompanied by a burst of neural firing, during the optical stimulation. This study paves a new avenue to stable neurochemical sensing in vivo.

Sensitive Electrochemical Sensor for Glycoprotein Detection Using a Self-Serviced-Track 3D DNA Walker and Catalytic Hairpin Assembly Enzyme-Free Signal Amplification

Approaches for the detection of targets in the cellular microenvironment have been extensively developed. However, developing a method with sensitive and accurate analysis for noninvasive cancer diagnosis has remained challenging until now. Here, we reported a sensitive and universal electrochemical platform that integrates a self-serviced-track 3D DNA walker and catalytic hairpin assembly (CHA) triggering G-Quadruplex/Hemin DNAzyme assembly signal amplification. In the presence of a target, the aptamer recognition initiated the 3D DNA walker on the cell surface autonomous running and releasing DNA (C) from the triple helix. The released DNA C as the target-triggered CHA moiety, and then G-quadruplex/hemin, was formed on the surface of electrode. Eventually, a large amount of G-quadruplex/hemin was formed on the sensor surface to generate an amplified electrochemical signal. Using N-acetylgalactosamine as a model, benefiting from the high selectivity and sensitivity of the self-serviced-track 3D DNA walker and the CHA, this designed method showed a detection limit of 39 cell/mL and 2.16 nM N-acetylgalactosamine. Furthermore, this detection strategy was enzyme free and exhibited highly sensitive, accurate, and universal detection of a variety of targets by using the corresponding DNA aptamer in clinical sample analysis, showing potential for early and prognostic diagnostic application.

Deep Learning Enhanced Electrochemiluminescence Microscopy

Limited by the efficiency of electrochemiluminescence, tens of seconds of exposure time are typically required to get a high-quality image. Image enhancement of short exposure time images to obtain a well-defined electrochemiluminescence image can meet the needs of high-throughput or dynamic imaging. Here, we propose deep enhanced ECL microscopy (DEECL), a general strategy that utilizes artificial neural networks to reconstruct electrochemiluminescence images with millisecond exposure times to have similar quality as high-quality electrochemiluminescence images with second-long exposure time. Electrochemiluminescence imaging of fixed cells demonstrates that DEECL allows improvement of the imaging efficiency by 1 to 2 orders than usual. This approach is further used for a data-intensive analysis application, cell classification, achieving an accuracy of 85% with ECL data at an exposure time of 50 ms. We anticipate that the computationally enhanced electrochemiluminescence microscopy will enable fast and information-rich imaging and prove useful for understanding dynamic chemical and biological processes.

Galvanic Redox Potentiometry for Fouling-Free and Stable Serotonin Sensing in a Living Animal Brain

Serotonin (5-HT) is a major neurotransmitter broadly involved in many aspects of feeling and behavior. Although its electro-activity makes it a promising candidate for electrochemical sensing, the persistent generation of fouling layers on the electrode by its oxidation products presents a hurdle for reliable sensing. Here, we present a fouling-free 5-HT sensor based on galvanic redox potentiometry. The sensor efficiently minimizes electrode fouling as revealed by in situ Raman spectroscopy, ensuring a less than 3 % signal change in a 2 hour continuous experiment, whereas amperometric sensors losing 90 % within 30 min. Most importantly, the sensor is highly amenable for in vivo studies, permitting real-time 5-HT monitoring, and supporting the mechanism associated with serotonin release in brain. Our system offers an effective way for sensing different neurochemicals having significant fouling issues, thus facilitating the molecular-level understanding of brain function.

Deep Learning-Enhanced Potentiometric Aptasensing with Magneto-Controlled Sensors

Bioelectronic sensors that report charge changes of a biomolecule upon target binding enable direct and sensitive analyte detection but remain a major challenge for potentiometric measurement, mainly due to Debye Length limitations and the need for molecular-level platforms. Here, we report on a magneto-controlled potentiometric method to directly and sensitively measure the target-binding induced charge change of DNA aptamers assembled on magnetic beads using a polymeric membrane potentiometric ion sensor. The potentiometric responses of the negatively charged aptamer, serving as a receptor and reporter, were dynamically controlled and modulated by applying a magnetic field. Based on a potentiometric array, this non-equilibrium measurement technique combined with deep learning algorithms allows for rapidly and reliably classifying and quantifying diverse small molecules using antibiotics as models. This potentiometric strategy opens new modalities for sensing applications.

Multi-Spatiotemporal Probing of Neurochemical Events by Advanced Electrochemical Sensing Methods

Neurochemical events involving biosignals of different time and space dimensionalities constitute the complex basis of neurological functions and diseases. In view of this fact, electrochemical measurements enabling real-time quantification of neurochemicals at multiple levels of spatiotemporal resolution can provide informative clues to decode the molecular networks bridging vesicles and brains. This Minireview focuses on how scientific questions regarding the properties of single vesicles, neurotransmitter release kinetics, interstitial neurochemical dynamics, and multisignal interconnections in vivo have driven the design of electrochemical nano/microsensors, sensing interface engineering, and signal/data processing. An outlook for the future frontline in this realm will also be provided.

Acupuncture Needle-Based Transistor Neuroprobe for In Vivo Monitoring of Neurotransmitter

Chemical communication via neurotransmitters is central to brain functions. Nevertheless, in vivo real-time monitoring of neurotransmitters released in the brain, especially the electrochemically inactive molecules, remains a great challenge. In this work, a novel needle field-effect transistor (FET) microsensor based on an acupuncture needle is proposed, which is demonstrated to be capable of real-time monitoring dopamine molecules as well as neuropeptide Y in vivo. The FET microstructure is fabricated by successively wrapping an insulating layer and a gold layer on the top of the needle, where the needle and the Au served as the source and drain, respectively. After assembling reduced graphene oxide (RGO) between the source and drain electrodes, the specific aptamer is immobilized on the RGO, making this needle-FET biosensor highly selective and sensitive to real-time monitor neurotransmitters released from rat brain, even in a Parkinson's diseases model. Furthermore, the needle-FET biosensor is applied to detect a variety of targets including hormones, proteins, and nucleic acid. By constructing a FET sensing interface on an acupuncture needle and implanting the sensor in a rat's brain for in vivo detection, this work provides a new sight in the FET domain and further expands the species of real-time in vivo detection.

Nano- and Microsensors for In Vivo Real-Time Electrochemical Analysis: Present and Future Perspectives

Electrochemical nano- and microsensors have been a useful tool for measuring different analytes because of their small size, sensitivity, and favorable electrochemical properties. Using such sensors, it is possible to study physiological mechanisms at the cellular, tissue, and organ levels and determine the state of health and diseases. In this review, we highlight recent advances in the application of electrochemical sensors for measuring neurotransmitters, oxygen, ascorbate, drugs, pH values, and other analytes in vivo. The evolution of electrochemical sensors is discussed, with a particular focus on the development of significant fabrication schemes. Finally, we highlight the extensive applications of electrochemical sensors in medicine and biological science.

Deep Learning-Based Label-Free Surface-Enhanced Raman Scattering Screening and Recognition of Small-Molecule Binding Sites in Proteins

Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS); however, this has always been impeded by the complexity of peptide mixtures and the elaborate synthetic design for tags. Here, we demonstrate a novel technique for identifying protein binding sites using a deep learning-based label-free surface-enhanced Raman scattering (SERS) screening (DLSS) strategy. In DLSS, the deep learning model that was trained with large SERS signals could detect signal features of small molecules with high accuracy (>99%). Without any secondary tag, the small molecules are directly complexed with proteins. After proteolysis and LC, SERS signals of all LC fractions are collected and input into the model, whereby the fractions containing the small-molecule-modified peptides can be recognized by the model and sent to MS/MS to identify the binding site(s). By using an automated DLSS system, we successfully identified the modification sites of fomepizole in alcohol dehydrogenase, which is coordinated with zinc along with three peptides. We also showed that the DLSS strategy works for identification of amino-acid residues that covalently bond with ibrutinib in Bruton tyrosine kinase. These results suggest that the DLSS strategy, which provides high molecular recognition capability to LC-MS analysis, has potential in drug discovery, proteomics, and metabolomics.

Highly Stable and Selective Sensing of Hydrogen Sulfide in Living Mouse Brain with NiN4 Single-Atom Catalyst-Based Galvanic Redox Potentiometry

Hydrogen sulfide (H₂S) is recognized as a gasotransmitter and multifunctional signaling molecule in the central nervous system. Despite its essential neurofunctions, the chemical dynamics of H₂S during physiological and pathological processes remains poorly understood, emphasizing the significance of H₂S sensor development. However, the broadly utilized electrochemical H₂S sensors suffer from low stability and sensitivity loss in vivo due to sulfur poisoning-caused electrode passivation. Herein, we report a high-performance H₂S sensor that combines single-atom catalyst strategy and galvanic redox potentiometry to overcome the issue. Atomically dispersed NiN₄ active sites on the sensing interface promote electrochemical H₂S oxidation at an extremely low potential to drive spontaneous bipolarization of a single carbon fiber. Bias-free potentiometric sensing at open-circuit condition minimizes sulfur accumulation on the electrode surface, thus significantly enhancing the stability and sensitivity. The resulting sensor displays high selectivity to H₂S against physiological interferents and enables real-time accurate quantification of H₂S-releasing behavior in the living mouse brain.

Fast-Scanning Potential-Gated Organic Electrochemical Transistors for Highly Sensitive Sensing of Dopamine in Living Rat Brain

Developing techniques for the highly sensitive assay of neurotransmitters is essential for understanding physiological and pathological processes. Here, we demonstrate a fast-scanning potential (FSP)-gated organic electrochemical transistor (OECT): for the highly sensitive sensing of dopamine (DA) in a living rat brain. The configuration combines the selectivity of fast-scan cyclic voltammetry (FSCV) with the high sensitivity of an OECT. The combined use of FSP as a gating mode and transconductance (g_m ) as a sensing parameter further improve the sensing performance in terms of sensitivity, limit of detection, reproducibility, and stability. The FSP-OECT exhibits a sensitivity of 0.899 S M^-1 and a low limit of detection down to 5 nM and was validated for in vivo monitoring of the basal level and electrically stimulated release of DA.

Multiplexed Monitoring of Neurochemicals via Electrografting-Enabled Site-Selective Functionalization of Aptamers on Field-Effect Transistors

Neurochemical corelease has received much attention in understanding brain activity and cognition. Despite many attempts, the multiplexed monitoring of coreleased neurochemicals with spatiotemporal precision and minimal crosstalk using existing methods remains challenging. Here, we report a soft neural probe for multiplexed neurochemical monitoring via the electrografting-assisted site-selective functionalization of aptamers on graphene field-effect transistors (G-FETs). The neural probes possess excellent flexibility, ultralight mass (28 mg), and a nearly cellular-scale dimension of 50 μm × 50 μm for each G-FET. As a demonstration, we show that G-FETs with electrochemically grafted molecular linkers (-COOH or -NH₂) and specific aptamers can be used to monitor serotonin and dopamine with high sensitivity (limit of detection: 10 pM) and selectivity (dopamine sensor >22-fold over norepinephrine; serotonin sensor >17-fold over dopamine). In addition, we demonstrate the feasibility of the simultaneous monitoring of dopamine and serotonin in a single neural probe with minimal crosstalk and interferences in phosphate-buffered saline, artificial cerebrospinal fluid, and harvested mouse brain tissues. The stability studies show that multiplexed neural probes maintain the capability for simultaneously monitoring dopamine and serotonin with minimal crosstalk after incubating in rat cerebrospinal fluid for 96 h, although a reduced sensor response at high concentrations is observed. Ex vivo studies in harvested mice brains suggest potential applications in monitoring the evoked release of dopamine and serotonin. The developed multiplexed detection methodology can also be adapted for monitoring other neurochemicals, such as metabolites and neuropeptides, by simply replacing the aptamers functionalized on the G-FETs.

Novel, User-Friendly Experimental and Analysis Strategies for Fast Voltammetry: Next Generation FSCAV with Artificial Neural Networks

Fast-scan adsorption-controlled voltammetry (FSCAV) was recently derived from fast-scan cyclic voltammetry to estimate the absolute concentrations of neurotransmitters by using the innate adsorption properties of carbon fiber microelectrodes. This technique has improved our knowledge of serotonin dynamics in vivo. However, the analysis of FSCAV data is laborious and technically challenging. First, each electrode requires post-experimental in vitro calibration. Second, current analysis methods are semi-manual and time-consuming and require a steep learning curve. Finally, the calibration methods used do not adapt to nonlinear electrode responses. In this work, we provide freely accessible computational solutions to these issues. First, we design an artificial neural network (ANN) and train it with a large data set (calibrations from 140 electrodes by six different researchers) to achieve calibration-free estimations and improve predictive error. We discuss the power of the ANN to obtain a low predictive error without electrode-specific calibrations as a function of being able to predict the sensitivity of the electrode. We use the ANN to successfully predict the absolute serotonin concentrations of real in vivo data. Finally, we create a fast and user-friendly, fully automated analysis web platform to simplify and reduce the expertise required for the postanalysis of FSCAV signals.

Recent Progress on Highly Selective and Sensitive Electrochemical Aptamer-based Sensors

Highly selective, sensitive, and stable biosensors are essential for the molecular level understanding of many physiological activities and diseases. Electrochemical aptamer-based (E-AB) sensor is an appealing platform for measurement in biological system, attributing to the combined advantages of high selectivity of the aptamer and high sensitivity of electrochemical analysis. This review summarizes the latest development of E-AB sensors, focuses on the modification strategies used in the fabrication of sensors and the sensing strategies for analytes of different sizes in biological system, and then looks forward to the challenges and prospects of the future development of electrochemical aptamer-based sensors.

Experimental Voltammetry Analyzed Using Artificial Intelligence: Thermodynamics and Kinetics of the Dissociation of Acetic Acid in Aqueous Solution

Artificial intelligence (AI) is used to quantitatively analyze the voltammetry of the reduction of acetic acid in aqueous solution generating thermodynamic and kinetic data. Specifically, the variation of the steady-state current for the reduction of protons at a platinum microelectrode as a function of the bulk concentration of acetic acid is recorded and analyzed giving data in close agreement with independent measurements, provided the AI is trained with accurate and precise knowledge of diffusion coefficients of acetic acid, acetate ions, and H⁺.