Jie Wu

Acrylamide (AA) is a common contaminant in foods processed at high temperatures and has attracted significant attention due to its potential neurotoxicity and carcinogenicity. Therefore, the development of a highly sensitive, highly selective sensing technology suitable for on-site detection is of great importance for ensuring food safety. In this study, an aptamer (Apt)-based capacitive AA sensor was developed based on the alternating current electrokinetics (ACEK) effect. The sensor utilizes an aptamer as the biomimetic recognition element, which can specifically recognize AA, thereby enabling quantitative detection. Additionally, a detachable detection fixture and data acquisition system were designed to enhance the detection stability and convenience of sensor. Within the linear range of 1 nmol/L to 10 µmol/L, the sensor response (dC/dt) exhibited a good linear relationship with AA concentration, with a detection limit as low as 0.4235 nmol/L. The sensor exhibits good selectivity toward structural analogs of AA, with a recovery relative standard deviations (RSDs) of less than 5.42% in spiked coffee samples. This portable detection system provides a sensitive and user-friendly tool for the analysis of acrylamide in food and holds great potential for application in food safety.

Cortisol, as a crucial biomarker reflecting psychological stress and physiological status, requires rapid and sensitive detection for health assessment and disease diagnosis. Conventional methods are time-consuming, operationally complex, and costly, limiting their use for point-of-care testing. This study reports a flexible, aptamer-based capacitive biosensor that exploits alternating current electrokinetics for ultrafast detection of cortisol in small-volume samples. Aptamers are immobilized via Au-S self-assembly on gold interdigitated electrodes on a PET substrate, and ACEK-induced fluid motion and dielectrophoresis rapidly enrich cortisol at the electrode interface, producing measurable interfacial capacitance changes ΔC/C₀. The experimental results demonstrate that the sensor achieves a detection limit of 0.337 ng/mL in artificial sweat, with a response time within 1 min and a good linear response across the concentration range of 1 to 1000 ng/mL. Requiring only 10 μL of sample, the sensor exhibits good repeatability, specificity, and interference resistance, making it suitable for rapid cortisol level detection. To enhance detection stability, this study designed and integrated a microfluidic chip, enabling efficient sample delivery and stable detection. The system demonstrates strong interference resistance, revealing potential applications in health management and disease monitoring.

Cortisol, as a crucial biomarker reflecting psychological stress and physiological status, requires rapid and sensitive detection for health assessment and disease diagnosis. Conventional methods are time-consuming, operationally complex, and costly, limiting their use for point-of-care testing. This study reports a flexible, aptamer-based capacitive biosensor that exploits alternating current electrokinetics for ultrafast detection of cortisol in small-volume samples. Aptamers are immobilized via Au-S self-assembly on gold interdigitated electrodes on a PET substrate, and ACEK-induced fluid motion and dielectrophoresis rapidly enrich cortisol at the electrode interface, producing measurable interfacial capacitance changes ΔC/C₀. Experimental results demonstrate detection limits of 0.412 ng/mL in PBS and 0.337 ng/mL in artificial sweat, with response times within 1 minute and excellent linear response across 1-1000 ng/mL concentrations. Requiring only 10 μL of sample, the sensor exhibits good repeatability, specificity, and interference resistance, making it suitable for rapid cortisol level detection. To enhance detection stability, this study designed and integrated a microfluidic chip, enabling efficient sample delivery and stable detection. The system demonstrates strong interference resistance, revealing potential applications in health management and disease monitoring.

Surface-enhanced Raman spectroscopy (SERS) has evolved from a fundamental optical phenomenon to a powerful, molecule-specific analytical technique capable of detecting ultra-trace-level species across biomedicine, catalysis, environmental monitoring, and national security applications. In this review, we summarize recent advances in SERS probe design and fabrication along three major directions: (i) engineering plasmonic hotspots with enhanced field confinement to achieve stronger and more uniform signals; (ii) analyte-directed strategies that precisely position and retain target molecules via tailored surface chemistries, nanoscale confinement, and on-surface reactions for single hotspot SERS; and (iii) hybrid architectures integrating plasmonic metals with functional materials, including high entropy materials, semiconductors, and graphene and other 2D materials, to synergistically couple electromagnetic and chemical enhancement mechanisms. Despite significant progress, key challenges remain for practical applications outside laboratories, including substrate reproducibility and stability, diverse analyte compatibility, unknown molecule identification and standardized quantitative performance in complex environments. We highlight emerging solutions, such as large-area nanomanufacturing for controlled nanoscale gaps, high-resolution Raman mapping for spatial–temporal characterization, density-functional-theory-guided molecular interpretation, and machine-learning-enabled spectral analysis. Advances in foundational AI models and data-driven discovery are positioning SERS to become an increasingly versatile platform, from decoding unknown molecular structures to analyzing complicated multi-component systems for environmental, biomedical, and national security applications with high sensitivity and selectivity.

Avian influenza (AI), particularly highly pathogenic avian influenza (HPAI), represents a serious and growing threat to global poultry production, international trade, and human health security. Control of AI is complicated by the high evolutionary rate of influenza A viruses, which drives antigenic diversity and ongoing emergence of novel strains. Effective surveillance and disease management therefore depend on timely and accurate diagnostics. While conventional methods—including virus isolation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assays (ELISAs)—remain effective and widely used, they are limited by long turnaround times, the need for specialized equipment, and reliance on highly trained personnel. In addition, strict state and federal regulatory requirements restrict testing to a limited number of authorized laboratories. Although these regulations are essential for maintaining diagnostic accuracy and quality assurance, they place substantial strain on laboratory capacity during outbreaks and delay actionable results. The need for rapid, on-site decision making has driven interest in alternative diagnostic approaches, including biosensor technologies. A major limitation of current diagnostic strategies is the lack of robust DIVA (Differentiating Infected from Vaccinated Animals) capability. In countries such as the United States, where poultry vaccination against AI is not routinely practiced, the absence of DIVA-compatible diagnostics has hindered adoption of vaccination as a disease management tool, as seropositive birds and products face significant trade restrictions. Biosensor platforms capable of enabling DIVA strategies offer a potential pathway to support vaccination while preserving surveillance integrity. This review examines the current landscape of AI and HPAI diagnostics, emphasizing the limitations of traditional approaches and the opportunities presented by biosensor platforms. We evaluate electrochemical, optical, piezoelectric, and nucleic-acid-based biosensors, with particular attention to biorecognition strategies, performance metrics, field deployability, and applications supporting subtype discrimination, DIVA implementation, and One Health surveillance.

MicroRNAs (miRNAs) are promising biomarkers for early cancer detection, but their low abundance and matrix interference continue to limit rapid, point-of-care (POC) assays. Here, we report a rapid, sensitive, and label-free biosensing platform for the detection of miRNA-133a-3p, a tumor-suppressive biomarker implicated in various cancers. The sensor operates on a non-faradaic capacitive principle, utilizing gold-plated printed-circuit-board interdigitated electrodes functionalized with thiolated single-stranded DNA capture probes. Alternating current electrokinetic (ACEK) effects are integrated with capacitive readout to achieve a single-step operation that enhances target transport to the interface, accelerates hybridization, and amplifies the capacitance transient. Assay conditions were optimized to achieve a linear dynamic range from 1 fM to 1 pM with a high sensitivity of 2.44%/min per decade and a limit of detection (LOD) as low as 0.56 fM in a standard buffer. High specificity was confirmed through selectivity tests against non-complementary miRNAs. For serum analysis, only a simple 1:100 dilution is required prior to measurement, yielding a LOD of 0.74 fM in diluted serum (equivalent to 74 fM in neat serum). This portable and cost-effective ACEK-capacitive platform enables fast and selective miRNA detection in complex matrices with minimal sample preparation. Graphical Abstract

The biological accumulation of microcontaminants and associated antibiotic resistance in food poses significant threats to both human and environmental health. Therefore, it is particularly crucial to design and develop methods of efficient identification and detection. Recently, molecularly imprinted polymers (MIPs) and aptamers (Apts), as novel hybrid recognition elements, have received widespread attention from researchers. Because the dual recognition-based sensors have demonstrated enhanced performance and desirable characteristics, including high sensitivity, strong binding affinity, a low detection limit, and excellent stability under harsh environmental conditions, which are expected to be applied in food safety fields. This paper compares the characteristics of MIP and Apt, highlighting the significant advantages of molecularly imprinted polymer–aptamer (MIP-Apt) dual recognition in selectivity, sensitivity, and stability, which stems from their symmetric integration, akin to an extension of the ‘lock-and-key’ model. It then systematically discusses three synthetic strategies for MIP-Apt hybrid recognition systems and their applications for food safety detection, focusing on analyzing their detection strategies, sensing mechanisms, construction methodologies, performance evaluations, and potential application value. It also offers substantive perspectives on both the prevailing limitations and promising developmental pathways for MIP-Apt hybrid recognition-based sensing platforms.

The biological accumulation of microcontaminants and associated antibiotic resistance in food poses significant threats to both human and environmental health. Therefore, it is particularly important to design and develop methods of efficient identification and detection. Recently, molecularly imprinted polymers (MIPs) and aptamers (Apts), as an emerging hybrid recognition element, have received widespread attention from researchers. Because the dual recognition-based sensors have exhibited improved properties and desirable features, such as high sensitivity, low limit of detection, high stability under harsh environmental conditions, high binding affinity, which are expected to be applied in food safety fields. This paper compares the characteristics of MIP and Apt, highlighting the significant advantages of aptamer-integrated MIP (MIP@Apt) dual recognition. It then systematically discusses three synthetic strategies for MIP-Aptamer hybrid recognition systems and their applications for food safety detection, focusing on analyzing their detection strategies, sensing mechanisms, construction methodologies, performance evaluations, and potential application value. It also offers substantive perspectives on both the prevailing limitations and promising developmental pathways for MIP-aptamer hybrid recognition-based sensing platforms.

Escherichia coli (E. coli) is one of the most common strains that produce Shiga toxin, which can contaminate food and water, causing serious diseases and even endangering life. Therefore, the detection of E. coli is crucial for protecting public health. At present, most traditional methods have disadvantages such as long detection cycles, high cost, and complex operations. This article proposed a novel commercial Rayleigh surface acoustic wave (R-SAW) biosensor for the detection of trace amounts of E. coli, which utilized the coordination reaction between carboxyl (-COOH) groups and aluminum ions (Al³⁺) to form the bio-enhanced probes, enabling the 5-terminal -COOH-modified aptamers to be preferentially enriched and directionally immobilized on the electrode surface. The biosensor could complete the detection within 100 s, with a linear detection range of 10³–10⁸ cells/mL, a limit of detection (LOD) as low as 732 cells/mL, and a selectivity ratio of 3270:1. This article conducted spiked detection on six types of food, indicating that the biosensor had the advantages of rapid speed, high sensitive, wide detection range, low LOD, strong specificity, and low cost, providing an economical and convenient solution for detecting trace amounts of E. coli in food.

Escherichia coli (E. coli) remains a major concern in poultry production due to its ability to incite foodborne illness and public health crisis, zoonotic potential, and the increasing prevalence of antibiotic-resistant strains. The contamination of poultry products with pathogenic E. coli, including avian pathogenic E. coli (APEC) and Shiga toxin-producing E. coli (STEC), presents risks at multiple stages of the poultry production cycle. The stages affected by E. coli range from, but are not limited to, the hatcheries to grow-out operations, slaughterhouses, and retail markets. While traditional detection methods such as culture-based assays and polymerase chain reaction (PCR) are well-established for E. coli detection in the food supply chain, their time, cost, and high infrastructure demands limit their suitability for rapid and field-based surveillance—hindering the ability for effective cessation and handling of outbreaks. Biosensors have emerged as powerful diagnostic tools that offer rapid, sensitive, and cost-effective alternatives for E. coli detection across various stages of poultry development and processing where detection is needed. This review examines current biosensor technologies designed to detect bacterial biomarkers, toxins, antibiotic resistance genes, and host immune response indicators for E. coli. Emphasis is placed on field-deployable and point-of-care (POC) platforms capable of integrating into poultry production environments. In addition to enhancing early pathogen detection, biosensors support antimicrobial resistance monitoring, facilitate integration into Hazard Analysis Critical Control Points (HACCP) systems, and align with the One Health framework by improving both animal and public health outcomes. Their strategic implementation in slaughterhouse quality control and marketplace testing can significantly reduce contamination risk and strengthen traceability in the poultry value chain. As biosensor technology continues to evolve, its application in E. coli surveillance is poised to play a transformative role in sustainable poultry production and global food safety.