Intelligent gas sensors (IGSs) have garnered significant attention owing to their highly sensitive, selective, and reliable gas detection in industrial safety, environmental monitoring, food quality assessment, healthcare diagnostics, and smart Internet of Things (IoT) frameworks. While traditional metal oxide semiconductor (MOS) gas sensors demonstrate exceptional sensitivity and cost-effective fabrication, their practical implementation is typically hindered by slow recovery, poor selectivity, long-term instability, and susceptibility to environmental interference. In recent times, single-atom engineered materials, specifically single-atom catalysts (SACs), have attracted considerable attention because isolated metal atoms maximize atomic utilization efficiency while providing highly uniform and well-defined catalytic active sites. These exceptional atomic-scale properties substantially enhance charge transfer, gas adsorption, surface reaction kinetics, and catalytic activity, resulting in marked improvements in sensing performance. At the same time, the rapid advancement of artificial intelligence (AI) and machine learning (ML) has transformed traditional gas sensors into smart sensing technologies with optimized pattern recognition, feature extraction, gas classification, concentration prediction, drift compensation, and instantaneous decision-making. In this review, we have comprehensively summarized the recent advancements in single-atom engineered materials for smart gas sensing. The fundamental sensing mechanisms of single-atom materials, such as electronic structure engineering, adsorption modulation, and catalytic activation, are first discussed. Afterward, recent progress in SA-supported MOs gas sensors (SAC/MO-GSs) is thoroughly addressed, with special focus on SA classification, anchoring and stabilization strategies, metal–support interactions, and their impact on catalytic reactions, gas adsorption, charge transfer, and sensing performance. The incorporation of ML algorithms-such as random forests (RF), support vector machines (SVMs), k-nearest neighbors (k-NNs), principal component analysis (PCA), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and deep learning architectures-for smart gas sensing and sensor-array design is critically examined. Additionally, current challenges relating to atomic stability, scalable synthesis, data availability, computational cost, model generalization, practical device integration, and long-term durability are reviewed alongside emerging prospects in explainable AI, edge computing, digital twins, and self-powered wearable sensing devices. In conclusion, we have proposed future research directions toward the design of next-generation smart gas sensors that integrate atomic-scale sensing material engineering along with cutting-edge artificial intelligence, offering a route toward low-power, accurate, and autonomous gas-sensing applications.
Paper-based colorimetric glucose sensing has evolved from simple visual assays toward digitally quantified systems increasingly supported by artificial intelligence (AI). However, advanced computational analysis cannot fully compensate for limitations in sensing hardware or for insufficient and poorly standardized analytical data. In this Perspective, we argue that truly AI-ready paper-based glucose sensing requires coordinated development of three capabilities: selective molecular input, sensitive and reproducible signal generation, and standardized high-throughput data generation. Selective input can be engineered through filtering and permselective interfaces; signal generation can be strengthened through catalytic amplification, enzyme stabilization, reagent organization, and reaction-microenvironment engineering; and standardized paper platforms can provide the large, comparable datasets required for robust machine-learning models. We discuss these developments in the context of paper-based glucose assays and propose an integrated framework in which sensing chemistry, device architecture, dataset design, and computational analysis are co-designed. Such integration shifts AI from compensating for limitations of individual assays toward extracting robust quantitative information from intrinsically selective, informative, and reproducible analytical data.
Accurate monitoring of antidepressant drugs is essential for ensuring therapeutic efficacy and minimizing adverse effects, creating a demand for sensitive and reliable analytical methodologies. In the present study, a novel electrochemical sensor was developed for the determination of dothiepin hydrochloride (DOTH) using an iron-based metal–organic framework supported on nitrogen-doped graphite (Fe-MOF@N-dG). The composite material was synthesized through a rapid sonication-assisted approach at ambient temperature, avoiding hydrothermal/solvothermal heating and reducing the consumption of hazardous organic solvents. The physicochemical and electrochemical characteristics of the prepared Fe-MOF@N-dG were investigated using scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, and electrochemical impedance spectroscopy. Characterization results confirmed the successful formation of the composite and demonstrated a synergistic interaction between the Fe-MOF and nitrogen-doped graphite. The conductive N-dG network facilitated electron transfer, while the Fe-MOF provided abundant electrocatalytic active sites, which enhanced sensing performance. Electrochemical measurements were performed in Britton–Robinson buffer solution at pH 3.0 under optimized conditions. The fabricated sensor exhibited a linear response over the concentration range of 0.6–100 μM, with a detection limit of 0.073 μM, indicating good sensitivity toward DOTH determination. The proposed method was validated with respect to accuracy and precision and successfully applied to the analysis of spiked human plasma samples. The environmental sustainability of the developed methodology was also assessed using the recently introduced Environmental, Performance, and Practicality Index (EPPI), together with other established green analytical evaluation tools. The findings demonstrate that the Fe-MOF@N-dG platform offers a potentially sensitive, reliable, and environmentally sustainable strategy for electrochemical determination of DOTH in human plasma.
Carbon dots (CDs) have emerged as widely used functional nanomaterials in biomedical sensing due to their tunable fluorescence emission wavelength, excellent biocompatibility, and ease of functional modification. Fluorescent CDs operating in the near-infrared region II (NIR-II, 1000–1700 nm) offer advantages such as deep tissue penetration, low tissue autofluorescence background, and a high imaging signal-to-noise ratio, making them widely used in the detection of disease biomarkers. This article systematically reviews the latest research progress of NIR-II CDs in the detection of disease-related biomarkers. Firstly, we summarize the classification of fluorescent CDs and two types of chemical synthesis strategies. Subsequently, the research progress of NIR-II CDs in the detection of disease-related physiological markers is reviewed, and the fluorescence biosensing mechanism of NIR-II CDs is analyzed in depth. Finally, we explore the advantages of NIR-II fluorescent CDs in disease diagnosis and molecular imaging, analyze their current key challenges, and provide insights into their future development directions. This review aims to garner increased attention for the development of a new generation of high-performance NIR-II fluorescent CD biosensors.
The tumor microenvironment (TME) is spatially and temporally heterogeneous, limiting the specificity of single-biomarker strategies for resolving complex tumor states. The sequence programmability and predictable base pairing of DNA allow multiple biological inputs to be recognized, logically processed, and translated into defined analytical outputs. Here, we review recent advances in DNA logic-gated biosensors and associated nanodevices for multicomponent TME profiling, spanning nucleic acids, proteins, extracellular vesicles, and physicochemical cues. Particular attention is directed to how molecular recognition, Boolean computation, signal amplification, and transduction collectively govern analytical specificity, signal-to-noise ratio, reaction kinetics, and operational reliability in complex biological matrices. We further compare chemical stabilization, smart delivery, and spatial confinement strategies, together with applications in tumor phenotyping, immune-response monitoring, molecular imaging, and conditional therapeutic activation. Emerging directions include continuous and wearable monitoring, spatially organized molecular circuits, and advanced DNA molecular computing. Translation, however, will require improved nuclease resistance, delivery efficiency, threshold robustness, fabrication reproducibility, clinical-matrix validation, and system-level integration. By converting multicomponent TME signals into programmable molecular decisions, DNA logic-gated biosensing may support more reliable tumor characterization, precision diagnosis, and context-dependent therapy.
