Illicit drug detection is entering a transformative era, driven by the convergence of electrochemical sensing, nanomaterials engineering, and artificial intelligence. Traditional analytical approaches, despite their precision, are increasingly misaligned with the demands of real-time, on-site, and personalized monitoring. In recent years, electrochemical biosensors have emerged as a disruptive class of technologies capable of bridging this gap, offering miniaturized platforms that combine molecular specificity, rapid response, and adaptability to diverse biological matrices. This review captures the current momentum in the development of advanced electrochemical systems tailored for the detection of psychoactive substances, with a particular focus on opioids, stimulants, cannabinoids, and date rape drugs. We highlight how the integration of high-surface-area nanomaterials (e.g., MXenes, carbon nanostructures, metal organic frameworks) and programmable biorecognition interfaces (e.g., aptamers, synthetic polymers) has redefined the sensitivity, selectivity, and stability of drug sensors. Beyond material innovation, we explore how modern transduction strategies are being repurposed into flexible, wearable formats and seamlessly coupled with AI-driven data analytics to enable intelligent, autonomous sensing. Key technical challenges, including signal interference, fouling, multianalyte discrimination, and regulatory translation, are critically assessed alongside emerging solutions such as antifouling coatings, multiplexed recognition chemistries, and artificial intelligence (AI)-assisted calibration. Looking ahead, we outline a paradigm shift toward decentralized, user-adaptable drug sensing platforms that could radically improve forensic readiness, clinical toxicology, and public health surveillance. The path forward lies in translating these innovations into robust, field-deployable devices capable of meeting the complex demands of modern drug monitoring ecosystems.

Pre-eclampsia (PE) is a serious multiorgan complication that can seriously threaten the life and health of pregnant women and their fetuses. Current clinical diagnosis relies heavily on nonspecific symptoms, while conventional biomarker assays lack the sensitivity to detect low concentrations of placental growth factor (PlGF), a key indicator whose levels drop significantly as PE progresses. This paper proposed a cascade microfiber (CMF) biosensor that utilizes the vernier effect for the quantitative detection of PlGF in clinal serum samples of PE patients. The experimental results show that the proposed CMF biosensor has a limit of detection as low as 0.49 pg/mL and a detection time that is less than 20 min. Clinical validation using serum samples from 35 pregnant women demonstrated that the CMF biosensor achieved 78.6% sensitivity, 85.7% specificity, and 82.9% diagnostic accuracy. Importantly, we have established a strong correlation between PlGF levels and clinical severity, confirming the biomarker's auxiliary diagnosis value and reinforcing the sensor's clinical relevance. The proposed method could form the basis of a next-generation diagnostic system for PE that combines high sensitivity, speed, and simplicity and has the potential to transform current screening protocols by enabling early intervention and improving maternal-fetal outcomes.

Surface-enhanced Raman spectroscopy (SERS) can capture single-molecule-level component information from complex biological samples by providing their fingerprint profiles. However, increasing complexity and subtle variations in biological media can diminish the discrimination accuracy of traditional SERS excited by a single laser wavelength. Here, we demonstrate a multiwavelength SERS strategy for urine detection, aiming to achieve accurate diagnosis of ischemic stroke (IS). This strategy can acquire more comprehensive and unique chemical information on complex samples by capturing SERS fingerprints under multiple excitation wavelengths and vertically stacking them. Then, a convolutional neural network (CNN) classifier specifically designed for spectral data achieved an accuracy rate of 85.0% and an area under the curve (AUC) of 92.8% in recognizing IS. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to the diagnosis of IS. By correlating the important feature ranges selected by machine learning (ML) with the feature ranges of known biomolecules (such as lysine, arginine, glutamic acid, and hypoxanthine), we verified that the ML model effectively identified the Raman features of IS-related molecules and used a weighted combination of these features for the diagnosis of IS. Meanwhile, based on the multiwavelength SERS spectra stacking strategy, more effective information was extracted for the diagnosis of IS.

Efficient NO2 gas sensors are critical for environmental monitoring and industrial safety. However, the design of reliable NO2 sensing materials with high activity and moisture resistance remains a challenge, primarily due to limited active sites and interference from high air humidity. In this paper, SnO2/ZIF-8 nanocomposites were prepared by leveraging the inherent moisture resistance and abundant active sites of ZIF-8 nanoparticles, as well as the good electronic migration capabilities of SnO2 nanospheres. The synergistic effect between metal-organic frameworks and metal oxides enables the gas sensor to show an impressive response (Rg/Ra = 85.95) to 1 ppm of NO2 even in high-humidity conditions, with good selectivity. Additionally, the SnO2/ZIF-8 composite-based chemiresistive sensors exhibit a low limitation of detection (11 ppb), fast response/recovery time (57/75 s), and excellent long-term stability (over 35 days), rendering them highly suitable for practical NO2 detection. Density functional theory (DFT) calculations have demonstrated that the loading of ZIF-8 nanoparticles can enhance the adsorption effect as well as promote charge transfer between NO2 molecules and the SnO2 nanospheres. The synthesis of this composite material provides new ideas for improving the detection ability of NO2 at high humidity.

Food, especially fish meat, is extremely vulnerable to oxidation and microbiological deterioration. Therefore, effective analytical techniques for quality control and safety monitoring are required. Electrochemical biosensors have become reliable, rapid, and affordable devices for in-field and real-time food quality assessment. However, their application is often limited in point-of-need scenarios due to the requirement for intensive sample preparation. Here, we introduce a microneedle array (MNA)-based electrochemical biosensor, designed for direct food safety and quality analysis without the need for sample preparation. A gold (Au)-coated polymeric MNA was functionalized with a chitosan-gold nanoparticles (Ch-AuNP) nanocomposite and further modified by immobilizing xanthine oxidase (XO) for selective hypoxanthine (HX) detection. The MNA-based biosensor exhibited a linear range between 5 and 50 μM, and 50 to 200 μM, with a sensitivity of 0.024 μA/μM and a limit of detection (LOD) of 2.18 ± 0.75 μM for HX, with a response time of approximately 100 s. Furthermore, the MNA-based biosensor was successfully utilized for monitoring HX levels in fish tissue samples over 48 h, showing strong agreement with results obtained from a commercial Amplex Red assay kit. The technology can be used for real-time food quality assessment and food safety monitoring due to its high sensitivity, interference tolerance, and lack of requirement for sample preparation.

Graphene-based chips face persistent sensor-to-sensor variability due to manufacturing defects and polymer contamination, limiting their analytical reliability for healthcare applications. Here, we demonstrate that the integration of a machine learning (ML) model with graphene field-effect transistors (GFETs) enables quantitative and calibration-free analytical sensing. Using Random Forest Regression and field-effect-related figures of merit, the model enabled robust, quantitative predictions across analytes of varying chemical nature─from small ions to viral antigens. pH sensing was used as a reference system to validate the augmented platform. Compared with the reference analytical model, ML enabled a marked improvement of accuracy, from 93 to 97%, and a reduction of the coefficient of variability, from 14 to 3%. Then, the ML-integrated GFETs were applied to chloride detection, the gold standard for cystic fibrosis diagnosis. Finally, using GFETs functionalized with llama nanobodies, we targeted the ORF2 antigen of the Hepatitis E virus. ML integration significantly enhanced immunoassay sensitivity-specificity from 89-69% to 100-100% and allowed the quantitative prediction of antigen concentration. Furthermore, the ML-augmented test demonstrated a strong performance for HEV antigen detection in capillary blood samples without the need for any sample pretreatment.

Real-time monitoring of milk spoilage is crucial for ensuring food safety and minimizing waste; however, it remains challenging due to the lack of sensitive and robust detection technologies operable in humid environments. While metal oxide sensors are promising for volatile sensing, their performance is often compromised by poor room-temperature activity and liquid interference. Herein, we design a superhydrophobic SnO2/montmorillonite (MMT)@wax composite that overcomes these limitations by synergistically combining enhanced gas sensing and exceptional liquid resistance. MMT shows an intrinsic affinity to ammonia through ion-mediated adsorption within its interlayer galleries. Formation of a heterojunction between MMT and SnO2 further amplifies the ammonia response. A wax-based superhydrophobic coating not only effectively shields the sensing material from liquid environments but also preserves high sensitivity, fast response/recovery, remarkable reversibility, and long-term stability. During accelerated spoilage testing of milk at 30 °C, the composite sensor reliably detects ammonia vapors from the fourth day onward, as validated by gas chromatography and correlated with synchronous pH changes indicative of quality deterioration. The capability of continuous monitoring throughout the spoilage process is demonstrated. This work offers a scalable and field-ready platform for real-time quality assessment of liquid foods, bridging material innovation with practical sensing applications.