Search Results (578)

In this study, Zn-doped Co₃O₄ nanorods and nanosheets with controlled Zn/Co molar ratios were synthesized via a hydrothermal strategy to clarify the respective roles of morphology and elemental doping in ethylbenzene sensing. The gas-sensing performance is strongly influenced by morphology, and the radially oriented nanorod structure significantly enhances sensing response compared with nanosheet structures. Zn doping effectively enhances the gas-sensing performance of Co₃O₄. As a result, the optimized Zn-doped nanorod sensor exhibits high sensitivity to ethylbenzene, a low detection limit, rapid response and recovery, and excellent operational stability. Density functional theory calculations reveal that the predominantly exposed facets of the nanorod structure possess stronger adsorption affinity and pronounced charge transfer toward ethylbenzene, providing theoretical support for the morphology-dominated sensing behavior. At the same time, Zn incorporation further adjusts the band structure and surface reactivity. Overall, this work elucidates a morphology-dominated and doping-assisted enhancement mechanism, offering clear design principles for high-performance Co₃O₄-based ethylbenzene sensors. Full article

Pesticide residues in food and agricultural products continue to constitute a significant concern for food safety, particularly when rapid decision-making is required across production and supply chains. Although chromatographic methods such as GC-MS and LC-MS/MS remain essential for confirmatory analysis, their dependence on central laboratories limits their applicability for field screening. Consequently, portable biosensor-based detection platforms have attracted increasing attention as rapid screening tools. This review synthesizes 26 peer-reviewed studies published between 2010 and 2025 on portable biosensor-based screening tools for pesticide detection in food and agricultural matrices, including electrochemical sensors, immunoassays, aptamer-based systems, paper-based lateral flow devices, and smartphone-assisted platforms. Given the heterogeneity of analytes, sensing mechanisms, and study designs, a narrative synthesis approach was applied. Overall, the evidence suggests a shift from laboratory-centered detection toward field-deployable technologies that may support preliminary screening within food safety monitoring frameworks. Paper-based lateral flow assays are widely reported as deployable formats, while electrochemical and affinity-based platforms are often positioned as intermediate solutions for mobile or semi-controlled testing environments. However, most platforms remain at the proof-of-concept or early validation stage, and challenges related to matrix interference, long-term stability, reproducibility, standardization, and large-scale implementation persist. This review highlights the potential role of portable biosensor technologies as complementary tools within tiered food safety monitoring systems and outlines key priorities for further development before wider regulatory integration can be considered. Full article

Two-dimensional Ti₃C₂O₂ MXene has emerged as a promising electrode material for non-enzymatic glucose sensing due to its metallic conductivity and biocompatibility. However, the atomic-scale sensing mechanism remains unclear. This DFT study uses the PBE functional with the D3(BJ) dispersion correction to elucidate glucose–MXene interactions under idealized vacuum conditions. Pristine Ti₃C₂O₂ shows metallic behavior with a density of states of about 8.2 states per electron volt at the Fermi level, dominated by Ti 3d states. β-d-glucose adsorbs onto the surface through hydrogen bonding, with an adsorption energy of −0.82 eV at a separation distance of 2.8 angstroms. Bader analysis indicates a transfer of about 0.15 electrons from MXene to glucose, resulting in a Fermi level shift of about −0.15 eV and an 18% reduction in the density of states at the Fermi level. These changes correspond to an estimated sensitivity of approximately 0.6 μA mM⁻¹ cm⁻² and a detection limit of about 17 µM, consistent with reported experimental performance of MXene-based sensors. Comparative adsorption calculations for common sweat interferents yield −0.45 eV for lactate and −0.25 eV for urea, indicating weaker interfacial affinity than glucose; these values reflect thermodynamic binding strength and possible surface occupation rather than definitive electrochemical selectivity, which additionally depends on redox potential, electron-transfer kinetics, and operating bias. We acknowledge three main limitations: first, the model considers only pure oxygen termination rather than mixed oxygen, hydroxyl, and fluorine terminations; second, the calculations are performed under vacuum rather than in aqueous conditions; third, the study is based on static zero kelvin structures rather than finite temperature dynamics. Despite these idealizations, the results provide baseline mechanistic insights to support rational design of MXene-based glucose sensors. Full article

Accurate alignment of real-world object poses with their virtual counterparts using sensors, e.g. cameras, is essential for consistent interaction in mixed-reality systems. However, objects can undergo abrupt, untracked movements during periods when a tracking system is inactive, e.g., overnight, causing stored pose records to become inconsistent with the real scene and breaking user interaction in the virtual environment. Off-the-shelf 3D reconstruction networks such as MASt3R (Matching and Stereo 3D Reconstruction) method provide metrically scaled 3D point maps and pixel correspondences, but they are trained on static scenes and therefore fail to produce reliable object correspondences when the object has moved. We propose a robust pipeline that combines MASt3R’s metrically scaled 3D outputs with a background-based alignment strategy to recover and apply the true pose change of moved objects. Our method first segments foreground and background and extracts 3D background point sets for a reference day and a current day. An affine transformation between these background point sets is estimated via a standard registration technique and used to express the current-day object 3D coordinates in the reference coordinate frame. Within that unified frame we compute the object pose change and apply the resulting transform to the virtual object, restoring real–virtual consistency. Experiments on real scenes demonstrate that the proposed approach reliably corrects pose misalignments introduced during inactive periods and substantially improves over applying MASt3R alone, thereby enabling restored and consistent user interaction in the virtual environment. Full article

The distinctive bitter profile of Castanopsis fissa honey (LSZH) has not yet been clearly characterized at the chemical and molecular levels. Based on the LSZH samples (n = 6), this study investigated bitterness-associated compounds and their potential receptor interactions by integrating sensory evaluation, machine learning, untargeted metabolomics, electronic tongue analysis, targeted UPLC-QQQ-MS/MS quantification, and molecular docking. A Random Forest model combined with untargeted metabolomics screened 71 candidate bitter compounds, among which alkaloid-related metabolites were prominently represented. Electronic tongue analysis showed that several compounds exhibited higher bitterness-related sensor responses than quinine under the tested conditions. Targeted UPLC-QQQ-MS/MS analysis identified and quantified five key compounds, among which kynurenic acid was the most abundant, reaching approximately 4500 ppm (mg/kg). Molecular docking suggested that these compounds could favorably interact with the human bitter taste receptor TAS2R46, with binding affinities ranging from −5.4 to −6.5 kcal/mol, mainly through hydrogen bonding, hydrophobic interactions, and π-related interactions. Overall, this study provides chemical evidence and mechanistic clues for understanding the bitterness of LSZH and offers an integrated analytical framework for screening bitterness-associated compounds in complex food systems. Full article

Laccase plays an important role in the detection and degradation of phenolic compounds, but it is limited by its cost and stability. In this study, the laccase-like property of copper peptide (GHK-Cu) has been revealed. In terms of enzymatic reaction kinetics, GHK-Cu has a V_max of 1.735 × 10⁻⁴ mM·s⁻¹ and a K_m of 0.061 mM, demonstrating good substrate affinity and excellent catalytic efficiency. Then, a colorimetry was developed for rapid detection of epinephrine (EP) and 2-aminophenol (2-AP). The linear response range of EP is 20–240 μM, with a limit of detection (LOD) of 9.5 μM. The linear response ranges of 2-AP are 14–100 μM (in ultrapure water) and 2–120 μM (in seawater), with LODs of 2.56 μM and 1.65 μM. In addition, combined with a smartphone platform, a cotton-based sensor has been developed for the detection of 2-AP in seawater. The linear response ranges are 0–0.2 mM and 0.2–1 mM, with LOD of 0.033 mM. The structure of GHK-Cu provides a reference for the development of novel laccase mimetic enzymes. The constructed colorimetry offers an option for the rapid detection of phenolic compounds, and the developed cotton-based sensor enabled rapid and portable detection of 2-AP. Full article

Mercury ions (Hg²⁺) are highly toxic and pose severe risks to human health and ecosystems, necessitating sensitive detection methods for environmental monitoring. Here, we report a paper-based graphene sensor functionalized with single-stranded DNA (ssDNA) probes for Hg²⁺ detection based on T-Hg²⁺-T coordination chemistry. To elucidate the effect of probe structure on sensing performance, we designed DNA constructs with varying numbers of guanine (G) bases (3–6, designated DNA2–DNA5) in the bridging fragment and systematically evaluated their influence on hairpin stability, Hg²⁺ binding affinity, and sensor response. The DNA3-based sensor (four G bases) exhibited optimal electronic stability and sensitivity, achieving a detection limit of 0.673 pM with effective real-time monitoring capability in aqueous media. These findings highlight the critical role of DNA sequence design in T-Hg²⁺-T-based biosensors and provide a promising strategy for sensitive and selective Hg²⁺ detection in environmental samples. Full article

This study presents a pilot methodological investigation of the thermal performance of a Najdi mudbrick dwelling in Ushaiger, Saudi Arabia, using short-term field monitoring and a preliminary digital-twin inspired workflow. Two field campaigns in August and September 2025 measured indoor and outdoor conditions with a portable weather station under severe site constraints, including lack of electrical infrastructure, restricted access, and the use of consumer-grade sensors. The monitored results indicate that the massive earthen walls attenuated part of the outdoor daily temperature swing, but indoor conditions remained very hot: in August, indoor temperatures averaged 38.1 °C, compared with 40.2 °C outdoors, and in September, indoor temperatures averaged 36.3 °C, compared with 36.1 °C outdoors. A simplified IDA ICE model was compared with the monitored indoor temperature over the available windows, and a post-processing affine bias adjustment was tested only as a diagnostic short-window correction rather than as a transferable calibration. Monte Carlo sensitivity analysis was used in an exploratory way. It examined how passive envelope and boundary-related parameters influenced simulated indoor relative humidity, with infiltration emerging as the dominant factor affecting relative humidity dynamics; peak indoor relative humidity increased from about 67% at 0.15 air changes per hour (ACH) to more than 74% at 0.60 ACH, whereas wall thickness had a modest buffering effect. Given the short monitoring duration and field limitations, the study is not presented as a fully validated digital twin but as a feasibility-oriented workflow that combines constrained in situ monitoring with exploratory simulation to support future, longer-term conservation and adaptive reuse research on earthen heritage in hot–arid climates. Full article

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies. Full article

Crop fungal and viral diseases cause annual economic losses exceeding USD 150 billion globally, demanding rapid, sensitive, and field-deployable diagnostic technologies. This review critically evaluates recent advances in electrochemical biosensing platforms for early crop pathogen detection, focusing on immunosensors, genosensors, aptasensors, and VOC-based systems. Reported analytical performances demonstrate ultralow detection capabilities, including 0.3 fg mL⁻¹ for viral coat proteins, 15 DNA copies for bacterial pathogens, 0.5 fg µL⁻¹ RNA detection for viroids, and nanomolar-level VOC sensing (35–62 nM), with response times ranging from 2 to 60 min. Comparative analysis reveals that genosensors and aptasensors generally achieve the lowest LODs due to nucleic acid amplification or high-affinity recognition, while immunosensors provide robust protein-level specificity validated against ELISA. Volatile organic compound (VOC) sensors enable non-invasive, pre-symptomatic monitoring but face specificity challenges. Despite strong laboratory performance, practical adoption is limited by matrix-derived electrochemical interference, environmental instability of biorecognition elements, workflow complexity, and insufficient standardization across studies. Emerging innovations, including magnetic bead enrichment, nanoporous and graphene-based electrodes, microfluidic integration, AI-assisted impedance interpretation, and biodegradable substrates, are progressively addressing these bottlenecks. This review emphasizes that successful field translation requires holistic workflow engineering, matrix-matched validation, and harmonized performance metrics rather than incremental sensitivity improvements alone. By integrating analytical chemistry, nanomaterials engineering, and agricultural decision-support frameworks, electrochemical biosensing platforms hold significant potential to enable decentralized, rapid, and sustainable crop disease management. Full article

This study presents the metrological validation of encapsulated DS18B20 digital temperature sensors. Eight units were tested, and seven were analysed (sensor 8 was excluded owing to a systematic failure). The evaluation was performed using a standard comparison calibration, where T_ref was defined as the mean of two calibrated Pt-100 probes in a Julabo DYNEO DD 601F thermostatic bath, following the TH-001 procedure of the Spanish Centre of Metrology (CEM). Four validation tests were performed: Test 1 (E1, 20 to 75 °C), Test 2 (E2, 20 to 72 °C), and with an extended range, Test 3 (E3, −12 to 86 °C) and Test 4 (E4, −12 to 86 °C; repetition to assess reproducibility relative to E3), with 10 steady-state readings per setpoint. Erroneous readings were defined and removed (probe 3, Test 4), and set points without valid readings from probe 4 above 68 °C were excluded. Without data processing, the errors were consistent with the manufacturer’s stated ±0.5 °C, despite an inter-probe bias. Several correction models were evaluated (offset, affine linear, polynomial, and segmented); the probe-specific affine linear model provided the best overall compromise, reducing MAE (Mean Absolute Error) to 0.046 to 0.130 °C and RMSE (Root Mean Square Error) to 0.057 to 0.169 °C. The process uncertainty is dominated by the traceability of the Pt-100 probes and the effective nonuniformity of the isothermal volume, which limits the achievable accuracy. The results support the use of individually calibrated DS18B20 sensors for continuous monitoring, provided that the effective operating range is maintained. Full article

The hypothalamic–pituitary–adrenal (HPA) axis coordinates metabolic, immune, and behavioral responses to a changing environment. Its molecular effectors are the nuclear receptors for glucocorticoids and mineralocorticoids (the GRs/MRs), encoded by nr3c1/nr3c2. The MR serves as the high-affinity sensor of basal hormone concentrations, whereas the GR amplifies the stress response and mediates negative feedback. Despite their shared domain architecture, the receptors have diverged functionally: isoform composition, post-translational modifications, and the complement of co-regulators together determine which genes are activated or repressed in a given tissue at a given time. The regulation of the HPA axis activity is a major determinant of embryonic development. Pregnancy adds a placental control layer that meters maternal signals: 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) in the syncytiotrophoblast inactivates cortisol, whereas 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) can regenerate it, and systemic buffering by transcortin (cortisol-binding globulin, CBG) limits the free hormone fraction. Under stress, inflammation, or hypoxia, this barrier weakens, exposing the fetus to stronger glucocorticoid pulses during windows of heightened vulnerability for brain and immune development. Such overexposure not only reshapes ongoing transcription but is also epigenetically inscribed: the methylation of alternative nr3c1 promoters, the remodeling of histones, and the shifts in ncRNA profiles recalibrate the axis sensitivity for the long term. At the phenotypic level, this manifests as variability in stress reactivity, cognitive and affective trajectories, and an immune and metabolic risk across later ontogeny. In this review, we integrate evidence on the structure and functions of the GR, the mechanisms of its post-translational and epigenetic regulation, and the role of the placenta, to provide a coherent framework for understanding the multifaceted consequences of prenatal stress and to identify potential targets for early prevention. Full article

In₂O₃ has high electron mobility, strong affinity for oxidizing gases, and abundant tunable surface oxygen species. These features enable efficient charge transfer during ozone adsorption, making In₂O₃ a promising ozone-sensing material. However, conventional In₂O₃-based gas sensors still suffer from insufficient sensitivity at low ozone concentrations and slow response/recovery rates, limiting their performance for high-precision gas detection. In this study, morphology-controlled In₂O₃ nanorods were synthesized via a glucose-assisted hydrothermal method, enabling coordinated regulation of the material structure and surface properties. Compared with conventional In₂O₃ nanocubes, the glucose-modulated In₂O₃ nanorods exhibited an approximately sevenfold increase in response toward 1 ppm O₃, indicating markedly improved capability for detecting low-concentration ozone. In addition, the sensor demonstrated a relatively low detection limit of about 80 ppb and fast response/recovery behavior (108 s/238 s). This strategy improves gas sensing performance through morphology optimization, increased surface active sites, and enhanced electron transport, offering a feasible materials design route for high-performance ozone gas sensors and showing potential for real-time environmental ozone monitoring and related applications. Full article

Underwater acoustic single-beacon positioning technology achieves localization by integrating vehicle motion with range measurements acquired from acoustic ranging devices, offering advantages such as system simplicity, flexible deployment, and high cost-effectiveness. However, its accuracy is limited by weak initial observability and degraded observation geometry. To address this, a sensor data correction and collaborative optimization framework is proposed. A hybrid outlier rejection strategy first suppresses acoustic multipath and sensor noise. To compensate for systematic sensor errors ignored in conventional Virtual Long Baseline methods, an affine transformation maps the true trajectory to the sensor-indicated one, reformulating error compensation as a correction to virtual beacon coordinates. To further mitigate the accuracy degradation caused by degenerated geometric configurations, this paper proposes a collaborative algorithm that integrates Chan initialization with affine transformation optimization. This approach formulates the positioning problem as an optimization task, simultaneously estimating the position information and affine transformation parameters through iterative refinement to achieve high-precision localization. The process begins with Chan’s algorithm, which provides an initial estimate from the virtual sensor array. This estimate is then refined under affine constraints to achieve high-precision localization. Experimental results show the method improves positioning accuracy by 36.30% compared to baseline algorithms, demonstrating significant performance enhancement. Full article

The ability to assess molecular binding kinetics in real time is critical for advancing our understanding of molecular interactions in biochemical and biotechnological systems. This work presents a novel optical tweezer (OT)-based method to monitor molecular affinity in real time, focusing on the high-affinity streptavidin–biotin system as a model. Transparent poly(methyl methacrylate) (PMMA) microparticles functionalized with streptavidin were trapped before, during, and after binding with biotinylated bovine serum albumin (biotin–BSA), enabling the analysis of forward-scattered signals to detect nanoscale changes in particle size. By applying the Power Spectral Density method, the friction coefficient of individual particles was calculated, allowing for real-time tracking of binding dynamics and the estimation of the association rate constant ($k_{\mathrm{on}}\approx {10}^6{\mathrm{M}}^{-1}{\mathrm{s}}^{-1}$). These results are consistent with literature values and demonstrate the potential of this OT-based approach for non-invasive, label-free detection of molecular interactions. Compared to existing techniques, such as atomic force microscopy and cantilever-based sensors, this method offers significant advantages, including real-time monitoring, adaptability to different bioaffinity systems, and compatibility with miniaturized setups. This work establishes a foundation for using OT-based tools to monitor high-affinity molecular interactions in real time. While demonstrated here using biotinylated BSA as a model ligand, future studies will explore the method’s applicability to smaller ligands and more subtle surface modifications. Full article