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Discriminating structurally similar neurotransmitters in complex environments remains a major challenge for electrochemical sensing due to overlapping responses, cross‐reactivity, and interferences. Here, we report nanozyme‐engineered carbon fiber microsensors that generate catalytically differentiated electrochemical signatures, enabling neurotransmitter recognition when coupled with machine learning‐assisted signal analysis and feature engineering. We show that Au@CeO₂nanozyme provides catalytic sites that modulate electron‐transfer kinetics at microelectrodes and enhance chemical selectivity toward dopamine, serotonin, L‐3,4‐dihydroxyphenylalanine, and ascorbic acid. By integrating feature extraction with multivariate learning, the platform achieves simultaneous identification and concentration prediction of multiple neurochemicals in mixtures. The Au@CeO₂microsensors exhibit enhanced sensitivity and nanomolar detection limits for dopamine and serotonin, while enabling robust discrimination in complex mixtures. This work establishes a general framework for integrating functional nanozyme interfaces with electrochemistry and data‐driven analytics to enhance selectivity of sensing systems for neurochemical monitoring, bioelectronic interfaces, diagnostic and measurement technologies. 

Lactate (LA) is a key biomarker of anaerobic metabolism, hypoxia, and cellular stress, with elevated levels indicating metabolic changes during intense exercise, sepsis, and microbial activity. Despite its broad relevance, real‐time, user‐friendly LA sensing remains challenging. Here, we report a versatile 3D‐printed hydrogel biosensor integrating lactate oxidase (LOx) and ceria nanozyme (nCe) mimetics, coupled with smartphone imaging and machine learning (ML) for robust, matrix‐specific LA detection. The LOx/nCe hydrogel enables single‐step colorimetric sensing, while ML‐based analysis compensates for environmental imaging variations and improves detection accuracy, achieving a detection limit of 46 µm LA. Synergistic enzymatic and nanozyme catalysis provides molecular recognition and signal amplification, enabling sensitive and selective measurements across diverse sample types. The biosensors exhibit excellent storage stability, maintaining performance for 45 d at room temperature, 6 months at 4°C, and 9 months at −20°C. The 3D‐printing approach enables reproducible and scalable fabrication, while smartphone‐based ML analysis provides real‐time concentration prediction with an R2 of 0.95. The platform is demonstrated in sweat monitoring and intelligent food packaging applications for milk and seafood freshness. Overall, this work establishes a low‐cost, point‐of‐use LA sensing platform for personalized health tracking, athletic performance monitoring, and intelligent food‐quality assessment. 

The accurate and timely diagnosis of neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, and major depressive disorder critically depends on real-time monitoring and precise interpretation of authentic neurotransmitter (NT) signal dynamics in complex biological fluids (CBFs), including cerebrospinal fluid. These NT dynamics are governed by both the type and concentration of NTs present in the CBFs. However, current biosensors face significant limitations in sensitivity and selectivity, thereby hindering reliable estimation (detection and quantification) of NTs. Though nanomaterials and bioenzymes have been utilized to modify sensor interfaces for enhanced performance, issues like signal convolution, electrode fouling, and inter-NT crosstalk persist. Objectives: This review aims to evaluate and synthesize current research on the use of artificial intelligence (AI), particularly machine learning (ML), pattern recognition (PR), and deep learning (DL), to improve the automated detection and quantification of neurotransmitters from complex biological fluids. Design: A systematic review of 33 peer-reviewed studies was conducted, focusing on the integration of AI methods in neurotransmitter estimation. The review includes an analysis of commonly studied NTs, the methodologies for their detection, data acquisition techniques, and the AI algorithms applied for signal processing and interpretation. Results: The studies reviewed demonstrate that AI-based approaches have shown considerable potential in overcoming traditional biosensor limitations by effectively deconvoluting complex, multiplexed NT signals. These techniques allow for more accurate NT estimation in real-time monitoring scenarios. The review categorizes AI methodologies by their application and performance in NT signal analysis. Conclusions: AI-enhanced NT monitoring represents a promising direction for advancing diagnostic and therapeutic capabilities in neurodegenerative diseases. Despite current challenges, such as sensor stability and NT interaction complexity, AI integration, particularly in applications like closed-loop deep brain stimulation (CLDBS), offers significant potential for more effective and personalized treatments. 

In this work, we simultaneously detected and predicted the concentration levels of serotonin (SE) and dopamine (DA) neurotransmitters (NTs) for in vitro mixtures, with measurements obtained using conventional glassy carbon electrodes (CGCEs) and differential pulse voltammetry (DPV). The NTs were estimated by deconvolving the multiplexed signals of both NTs using Principal Component Analysis with Gaussian Process Regression (PCA-GPR) and Partial Least Squares with Gaussian Process Regression (PLS-GPR), both with exponential–isotropic kernels. The average testing accuracies of estimation using PCA-GPR for DA alone, SE alone and their mixture (DA–SE) were 87.6%, 88.1%, and 96.7%, respectively. Using PLS-GPR, the testing accuracies of estimation for DA alone, SE alone, and their mixture (DA–SE) were 87.3%, 83.8%, and 95.1%, respectively. Furthermore, we explored methods of reducing the procedural complexity in estimating the NTs by finding reduced subsets of features for accurately detecting and predicting their concentrations. The reduced subsets of features found in the oxidation potential windows of the NTs improved the testing accuracy of the estimation of DA–SE to 97.4%. We thus believe that reducing complexity has the potential to increase the detection and prediction accuracies of NT measurements for practical clinical uses such as deep brain stimulation. 

Reactive oxygen species (ROS) including the superoxide anion (O2•−) are typically studied in cell cultures using fluorescent dyes, which provide only discrete single-point measurements. These methods lack the capabilities for assessing O2•− kinetics and release in a quantitative manner over long monitoring times. Herein, we present the fabrication and application of an electrochemical biosensor that enables real-time continuous monitoring of O2•− release in cell cultures for extended periods (> 8 h) using an O2•− specific microelectrode. To achieve the sensitivity and selectivity requirements for cellular sensing, we developed a biohybrid system consisting of superoxide dismutase (SOD) and Ti3C2Tx MXenes, deposited on a gold microwire electrode (AuME) as O2•− specific materials with catalytic amplification through the synergistic action of the enzyme and the biomimetic MXenes-based structure. The biosensor demonstrated a sensitivity of 18.35 nA/μM with a linear range from 147 to 930 nM in a cell culture medium. To demonstrate its robustness and practicality, we applied the biosensor to monitor O2•− levels in human leukemia monocytic THP-1 cells upon stimulation with lipopolysaccharide (LPS). Using this strategy, we successfully monitored LPS-induced O2•− in THP-1 cells, as well as the quenching effect induced by the ROS scavenger N-acetyl-l-cysteine (NAC). The biosensor is generally useful for exploring the role of oxidative stress and longitudinally monitoring O2•− release in cell cultures, enabling studies of biochemical processes and associated oxidative stress mechanisms in cellular and other biological environments. 

Dopamine is an essential neurotransmitter for daily cognitive functions controlling many neurophysiological processes including memory, cognition, and physical control. Development of analytical methods and sensors to detect dopamine is important for health monitoring and neurological research. This review provides an overview of recent advances in the development of electrochemical catalytic biosensors based on enzyme and enzyme-mimetic materials and discusses their potential applications for measurements of dopamine in biological fluids. The first part of the review summarizes and critically assesses the different types of enzymes and enzyme mimetic materials that can be used to catalytically convert dopamine, followed by a discussion of the biosensor’s fabrication, key design parameters, and detection mechanism on various electrode platforms ranging from single-use screen-printed electrodes to microneedles and implantable microelectrodes. The second part provides examples of measurements of dopamine in biological samples, including saliva, urine, serum, cell cultures, and brain tissue. We conclude with a summary of advantages and limitations of these devices in the clinical field, and an outlook to future research towards the implementation and broader adoption of electrochemical biosensors in neurophysiology, pharmacology, and the clinical field. 

Two-dimensional (2D) layered materials that integrate metallic conductivity, catalytic activity and the ability to stabilize biological receptors provide unique capabilities for designing electrochemical biosensors for large-scale detection and diagnostic applications. Herein, we report a multifunctional MXene-based 2D nanostructure decorated with enzyme mimetic cerium oxide nanoparticle (MXCeO2) as a novel platform and catalytic amplifier for electrochemical biosensors, specifically targeting the detection of oxidase enzyme substrates. We demonstrate enhanced catalytic efficiency of the MXCeO2 for the reduction of hydrogen peroxide (H2O2) and its ability to immobilize oxidase enzymes, such as glucose oxidase, lactate oxidase and xanthine oxidase. The designed biosensors exhibit high selectivity, stability, and sensitivity, achieving detection limits of 0.8 μM H2O2, 0.49 μM glucose, 3.6 μM lactate and 1.7 μM hypoxanthine, when the MXCeO2 and their respective enzymes were used. The MXCeO2 was successfully incorporated into a wearable fabric demonstrating high sensitivity for lactate measurements in sweat. The unique combination of MXenes with CeO2 offers excellent conductivity, catalytic efficiency and enhanced enzyme loading, demonstrating potential of the MXCeO2 as a catalytically active material to boost efficiency of oxidase enzyme reactions. This design can be used as a general platform for increasing the sensitivity of enzyme based biosensors and advance the development of electrochemical biosensors for a variety of applications. 

Cerium oxide nanoparticles (CeNPs) are versatile materials with unique and unusual properties that vary depending on their surface chemistry, size, shape, coating, oxidation states, crystallinity, dopant, structural and surface defects. This review details advances made over the past twenty years in the development of CeNPs and ceria-based nanostructures, the structural determinants affecting their activity, and translation of these distinct features into applications. The two-oxidation states of nanosized CeNPs (Ce3+/Ce4+) coexisting at the nanoscale level, facilitate formation of oxygen vacancies and defect states which confer extremely high reactivity and oxygen buffering capacity, and the ability to act as catalysts for oxidation and reduction reactions. However, the method of synthesis, surface functionalization, surface coating and defects are important factors in determining their properties. This review highlights the key properties of CeNPs, their synthesis, interactions and reaction pathways, and provides examples of emerging applications. Due to their unique properties, CeNPs have become quintessential candidates for catalysis, chemical mechanical planarization (CMP), sensing, biomedical applications and environmental remediation, with tremendous potential to create novel products and translational innovations in a wide range of industries. This review highlights the timely relevance and the transformative potential of these materials in addressing societal challenges and driving technological advancements across these fields.