Sugarcane smut disease poses a severe threat to global sugar production and sustainable agriculture, underscoring the critical need for early and accurate diagnostic tools. This study introduces a field-deployable selfpowered biosensor featuring dual-signal output for on-site pathogen detection, capitalizing on an innovative hybrid nanozyme-natural enzyme catalytic system. This system synergistically combines the peroxidase-like activity of Ni-doped zeolitic imidazolate framework (Ni-ZIF-67) with the high catalytic specificity of glucose oxidase (GOD), significantly enhancing electron transfer, enzyme stability, and cascade reaction efficiency. The biosensor operates through a sophisticated nucleic acid amplification strategy, target recognition triggers catalytic hairpin assembly to form a four-legged DNAzyme, which releases an initiator strand to drive a hyperbranched hybridization chain reaction, resulting in the self-assembly of a hexagonal DNA nano-mesh for efficient signal accumulation. Methylene blue adsorbed on the nano-mesh enables dual-mode electrochemical and colorimetric detection. The Au/Ni-ZIF-67@GOD-based bioanode not only facilitates glucose oxidation but also decomposes H2O2 byproducts in situ, preventing electrode fouling and ensuring sustained self-powered operation, particularly critical in complex plant-derived matrices. The biosensor demonstrates exceptional performance with detection limits of 29.5 aM (electrochemical) and 49.5 aM (colorimetric), a broad linear range from 0.1 fM to 10 nM, and 97.8 % accuracy in field-infected sugarcane samples, surpassing conventional qPCR methods. Its high selectivity, stability, and reproducibility, complemented by visual readout capability, establish a reliable and practical platform for early plant disease management. This work not only provides a transformative tool for agricultural diagnostics but also pioneers the integration of hybrid enzyme-nanozyme catalysis with DNA nanotechnology for next-generation biosensing applications.

The concurrent detection of multiple thalassemia mutations remains challenging due to the limitations of conventional single-readout biosensors in terms of reliability and sensitivity. Herein, we report an intelligent photoelectrochemical-colorimetric (PEC-CL) dual-modal biosensor based on a Z-scheme Bi2S3/BiOI heterojunction, integrating an OR logic gate with a DNAzyme Walker cascade amplification strategy. The OR logic gate enables parallel recognition of CD142 and CD41-42 mutations, outputting a unified trigger to initiate a nicking enzyme-assisted DNAzyme Walker. This autonomous machinery releases abundant G-quadruplex/hemin (G4-hemin) complexes, serving as a bifunctional signal transducer. Notably, the specific binding of G4-hemin to the electrode interface induces a distinct anodic-to-cathodic photocurrent polarity switching, which, coupled with its intrinsic peroxidase-mimicking activity, allows for self-validated dual-signal output. The platform achieves ultralow limits of detection down to 28.7 aM (PEC) and 2.6 fM (colorimetric) across a wide linear range (0.1 fM-0.1 μM). Furthermore, the dual-modal design effectively eliminates false positives through signal cross-validation. Practical application in human serum yielded satisfactory recoveries (94.1-107.8%), highlighting the platform’s potential for high-precision genetic screening in resource-limited settings.

To address the limitations of conventional methods in plant pathogen detection, we develop an intelligent detection platform that ingeniously integrates entropy-driven DNA amplification, nanozyme catalysis, and machine learning. This system employs functionalized Au@Cu2O nanozymes as versatile signal transducers, which simultaneously generate electrochemical, colorimetric, and photothermal readouts in response to the target pathogen, establishing an intrinsic triple-modal detection mechanism. The innovation of this work lies in the application of machine learning (Ridge Regression) to fuse these triple-modal signals. This integration creates a self-validating feedback loop that cross-checks the outputs from different modalities, significantly enhancing reliability by minimizing false positives/negatives. Furthermore, the machine learning (ML) model enables predictive analysis, allowing for accurate quantification beyond the conventional calibration curve. The platform achieved a remarkable detection limit of 0.22 fM within a broad linear range (1 fM - 100 nM), along with excellent reproducibility and stability. When applied to the detection of Fusarium sacchariin real sugarcane samples, the results showed high consistency with qPCR (recovery rates: 91.6-107.5 %). This work not only provides a robust tool for early plant disease diagnosis but also establishes a novel paradigm of intelligent, selfvalidating biosensing for agricultural applications.

Regulating "hotspots" in surface-enhanced Raman scattering (SERS) constitutes a key research focus, whereas achieving highly sensitive and full-coverage detection of multiphase analytes remains a major obstacle to the widespread application of SERS technology. To address the drawbacks that most current SERS substrates are confined to single-phase detection and that multifunctional integrated substrates are in short supply, constructed three-dimensional Ag nanoparticle (AgNPs)-decorated cassava starch aerogels (ACA) as SERS substrates through a green and facile gelatinization, retrogradation, and freeze-drying approach. The method takes advantage of the gelling characteristic of cassava starch (CS) and the in situ reduction capacity of cassava dialdehyde starch (CDS). The substrate possesses both superhydrophilic features and a collapse-induced mechanism: upon contact with aqueous solutions, its three-dimensional porous scaffold undergoes directional collapse, reducing the distance between AgNPs to generate high-density SERS "hotspots" and remarkably boosting detection sensitivity. Its three-dimensional interconnected pore structure and large specific surface area enable effective entrapment of gaseous, liquid, and solid analytes, overcoming the constraints of conventional substrates and achieving swift, sensitive, and quantitative detection of multiphase targets using a single substrate. With rhodamine 6G (R6G) and crystal violet (CV) as probe molecules, the enhancement factors (EF) were determined to be 6.8 × 108 and 5.6 × 108, respectively. In practical application, the adsorption method was adopted to detect gaseous 4-aminothiophenol (4-ATP), with a limit of detection (LOD) of 0.64 mg/L. Meanwhile, liquid and solid thiabendazole (TBZ) residues in food samples were measured via extraction and swabbing approaches, whose corresponding limits of detection were 0.063 mg/L and 0.067 mg/L separately. The findings demonstrate that the ACA SERS substrate exhibits high sensitivity and can quickly detect multiphase analytes, and the analytical platform holds great potential in the fields of food safety inspection and ecological monitoring.

This study reports a trimodal biosensing platform centered on an adaptive signal-fusion strategy for the ultrasensitive detection of pathogens, developed through the strategic integration of heterogeneous interface engineering and enzyme-powered molecular machines. The platform is built on a Ni-MOF-on-Co-MOF heterostructure, where precise interface modulation and pore channel engineering significantly enhance the specific surface area and electron-transport efficiency (1.55-fold higher enzyme-loading capacity), complemented by a novel Au@Ni/Co ZIF@PDA with a remarkable 38.1% photothermal conversion efficiency. For the detection mechanism, the system synergistically combines exonuclease III-mediated target cycling with a dual-output toehold-mediated strand displacement-DNA Walker cascade amplification strategy, which achieves exponential signal amplification by releasing double the signal probes per reaction cycle. This integrated design enables electrochemical, colorimetric, and photothermal trimodal output, with exceptional detection limits (LOD, S/N = 3) of 31.4 aM, 1.32 fM, and 1.14 fM respectively, alongside built-in self-verification and correction for enhanced reliability. Practical validation with real samples shows strong agreement with qPCR results, high spiked recovery rates (96.1-103.4%), excellent repeatability, and outstanding stability. Consequently, this trimodal system presents a novel, robust approach for pathogen detection and, via its adaptive intelligent multisignal cross-checking, offers a highly promising technological platform for complex sample analysis in early agricultural disease diagnosis, food safety monitoring, and clinical diagnostics.

Abstract Per- and polyfluoroalkyl substances require analytical methods that combine sensitivity with internal result checking. Here, we report an S-Loop CRISPR dual-mode assay (SCDA) for perfluorooctanoic acid (PFOA). PFOA-induced aptamer displacement initiates an AMPLON-derived isothermal S-Loop reaction mediated by an eight-arm poly(ethylene glycol) spider-inspired primer (SP), and the resulting amplicons activate Cas12a trans-cleavage. A FAM–quencher reporter provides the fluorescence (FL) output, whereas cleavage-induced removal of CdS from a CdS/gold nanoparticle/ZnIn2S4 photoelectrode releases an electron cage produced by type-I carrier confinement and visible-light shielding of the underlying photoactive layer, thereby restoring the photoelectrochemical (PEC) signal. At target concentrations of 10–13, 10–10, and 10–7 M, Cas12a reporter cleavage was linear with time over the first 10 min, and PEG20000-SP produced the largest amplification response and highest apparent reporter-signal rising rate. PEC and FL calibrations were linear over 10–15–10–7 and 10–14–10–7 M, respectively, with equations I = 12.3328 + 0.7207 log10C (R2 = 0.9929) and F = 13631.32 + 845.64 log10C (R2 = 0.9926). Estimated blank-based limits of detection were 1.27 × 10–16 M (PEC) and 8.34 × 10–16 M (FL); these estimates are below the lowest tested standards and do not establish quantitation below the calibration ranges. Paired results were compared within the common calibration range using log-concentration differences and relative percent differences. Consensus recoveries were 94.3–103.6% in milk and 95.7–103.0% in river water. SCDA therefore combines sensitive dual-readout with an explicit preliminary screen for discordant measurements.

The development of highly sensitive and selective platforms for trace Zn2+ detection is crucial for environmental monitoring and biomedical diagnostics. Herein, a photoelectrochemical (PEC) sensor was constructed using an L-cysteine (L-Cys)-functionalized BiOBr composite, in which surface-interface engineering was adopted to enrich available recognition sites and modulate the interfacial PEC response. Compared with pristine BiOBr, the incorporation of L-Cys introduces abundant Zn2+ binding functional groups and modifies the local surface environment of BiOBr, thereby enhancing the photocurrent response and enabling the selective recognition of Zn2+. The sensor operates through a signal-off mechanism, in which the coordination of Zn2+ with surface-bound L-Cys forms an interfacial coordination layer that partially hinders mass transport and interfacial charge transfer, resulting in a decrease in photocurrent. Under optimized conditions, the platform exhibits a wide linear response ranging from 0.5 to 40 nmol/L, with a low detection limit of 2.69 nmol/L (S/N = 3). Furthermore, the applicability of the sensor is validated in real-world scenarios, demonstrating excellent recoveries in tap water, lake water, and complex biological matrices such as HepG2 cell lysates (recoveries, 96.3–108.5

Accurate on-site detection of aflatoxin B1 (AFB1) is often compromised by complex matrix effects in food samples. Here, we develop a self-powered dual-modal biosensing platform that overcomes this challenge by integrating a zinc-air battery (ZAB) with a machine learning-assisted multimodal fusion strategy. The platform utilizes a target-responsive nucleic acid amplification cascade to release a MXene-Pt nanocomposite, which acts as a bifunctional transducer. It generates an electrochemical current via catalyzing the oxygen reduction reaction at the air cathode and produces a photothermal response under 808 nm laser irradiation. To address the limitations of single-signal readouts in complex matrices, we implement a machine learning model that fuses the orthogonal electrochemical and photothermal data. This intelligent fusion effectively suppresses background noise and matrix interference, significantly enhancing detection robustness. The system demonstrates a broad linear range from 10-15 to 10- 7 g/L for AFB1. Validation using spiked maize, peanuts, and naturally contaminated peanut oil shows that the dual-mode fusion predictions correlate exceptionally well with the HPLC-PCD reference method, markedly outperforming predictions based on either signal alone. This work provides a robust and intelligent solution for field-deployable food safety monitoring, addressing the critical challenge of matrix interference through synergistic self-powered operation and multimodal data fusion.

Natural antimicrobial agents are both environmentally friendly and safe, and exhibit excellent biocompatibility. However, their instability and rapid release characteristics limit their application in food preservation packaging. In this study, a photothermal-responsive packaging film was developed by incorporating a Pickering emulsion of soy protein isolate-tannic acid-Fe3+(STF) nanoparticles stabilizing tea tree oil (TTO) into a chitosan (CS)/gelatin (Gel) matrix. The STF nanoparticles, synthesized via coordination-driven self-assembly, stabilized the Pickering emulsion and achieved an encapsulation efficiency of 84% for TTO. Under near-infrared (NIR) irradiation, STF nanoparticles transform light energy into thermal energy. On the one hand, the localized increase in temperature provides the film with a certain sterilizing effect. On the other hand, the film allows for the rapid, controlled release of tea tree oil as needed. Through the synergistic effect of localized thermal therapy and controlled release of essential oils, the film exhibits over 98% antibacterial activity against E. coli and S. aureus. Furthermore, the resultant film has outstanding mechanical strength, barrier properties, and antioxidant effects. Preservation experiments were ultimately carried out using strawberries and grapes as representative berries, and the results showed that the film can extend the shelf life by 3-8 days depending on the fruit type. These results demonstrate the application potential of this photothermal controlled-release film in the field of berry preservation.

The excessive use of sulfadiazine (SDZ) poses significant health risks due to toxic residues in food and ecosystems. This work introduces a novel molecularly imprinted polymer (MIP) sensor featuring a carbon dot-based covalent organic framework (CD-COF) core as a high-performance electrochemiluminescence (ECL) emitter. Upon SDZ rebinding to imprinted sites, the sensor leverages SDZ's inherent redox activity to enable complementary differential pulse voltammetry (DPV) detection alongside ECL quenching. This dual-signal output establishes an intrinsic cross-validation mechanism that inherently corrects environmental interferences. The sensor achieves ultrasensitive detection of SDZ, with impressively low detection limits of 0.28 nM (ECL) and 1.6 nM (DPV). Validation in food matrices demonstrates exceptional accuracy and reliability, yielding recoveries of 91-110%. This study not only provides a robust tool for trace SDZ monitoring but also establishes a new paradigm for CD-COF-powered MIP sensors integrating dual-mode signals for highly reliable contaminant analysis in real-world samples.

An asymmetric hydrogel membrane based on aramid nanofiber (ANF) composite was developed, successfully embedding silver nanoparticles (AgNPs) as a surface-enhanced Raman scattering (SERS) sensor. Polymerization was completed within 8 min under UV irradiation, yielding a stable hydrogel membrane composed of silver nanoparticles/aramid nanofibers-polyacrylamide (AgNPs/ANFs-PAM) with an asymmetric structure. The upper layer of the hydrogel membrane near the light source exhibits a smooth, dense cortex, while the bottom layer far from the light source features a "hill-like" wrinkled structure. The dense network structure and porous structure form a distinct "gradient sieving" effect, enabling small-molecule targets to readily enter the hydrogel membrane and form hydrogen bonds with the amide bonds of ANFs on the SERS substrate, while large-molecule targets are retained outside the SERS substrate. The unique "hill-like" structure actively compresses AgNPs during contraction to form 3D "hotspots". Thiram and sulfamethoxazole were employed as probe molecules, yielding limits of detection (LOD) of 89.0 ng/L and 1.00 mu g/L, respectively. In the detection of thiram in celery and pears, and sulfamethoxazole in chicken, pork, and crucian, the recovery rates ranged from 85.0% to 110.0%, and the relative standard deviations (RSD) were all below 6.2%. The relative errors were less than 7.7% compared to traditional chromatographic methods. Taking advantage of the AgNPs/ANFs-PAM hydrogel membrane SERS sensor's fast response, high sensitivity, and excellent reproducibility, it offers a novel technical solution for detecting small molecules in food.

An ultrasensitive and selective molecularly imprinted photoelectrochemical (PEC) sensor utilizing a Bi2S3/BiOCl heterojunction is fabricated for the precise detection of oxytetracycline (OTC). The Bi2S3/BiOCl heterojunction is synthesized via a single-step hydrothermal process, where rod-shaped Bi2S3 epitaxially grows on BiOCl nanosheets to form an intimate interface. This unique heterostructure significantly enhances charge separation efficiency, achieving a photocurrent intensity 4.2-fold and 2840-fold higher than pure Bi2S3 and BiOCl, respectively. A molecularly imprinted polymer (MIP) film is electrochemically polymerized on the heterojunction surface. After template removal, the MIP provides spatially matched cavities for selective OTC recognition, where rebinding of OTC molecules modulate photogenerated carrier recombination, resulting in a concentration-dependent photocurrent quenching. The sensing platform exhibits a wide linear response across a concentration range of 1.0 to 1.0 × 105 nmol/L, with an ultralow detection limit of 0.035 nmol/L, outperforming most reported methods. Superior selectivity against structural analogs is demonstrated, stemming from the cooperative influence of MIP specificity and heterojunction-enhanced signal stability. Practical applicability is validated through OTC detection in tap water and milk matrices, achieving recoveries of 92.9-107.1 % with RSD<5 %. This work introduces a universal approach for developing heterojunction-driven MIP-PEC platforms for antibiotic monitoring in complex samples.

Sugarcane smut, responsible for 35-60 % yield losses and 18-22 % sugar content reductions, demands early diagnostic solutions to mitigate its agricultural impact. Current methods like qPCR remain constrained by limited sensitivity and reliance on centralized laboratory infrastructure. To address these challenges, a field-deployable biosensing platform is developed via the integration of three synergistic innovations: a rolling circle amplification-engineered catalytic "nanozyme forest" that achieves high-density immobilization of Au-Pt bimetallic nanozymes and glucose oxidase active sites; an entropy-driven DNA strand displacement amplification system employing multi-recognition probes for sequence-specific capture and signal amplification of the pathogen biomarker bE4'; and a dual-functional Au@CoV-MOF electrode that simultaneously enables pseudocapacitive energy storage and enhances interfacial electron transfer. The biosensor demonstrates a linear detection range from 10-16 to 10- 8 M with a 24.86 aM detection limit, surpassing qPCR sensitivity by two orders of magnitude while maintaining single-base mismatch discrimination. Field validation using sugarcane samples shows complete diagnostic concordance with qPCR results but reduces assay time through elimination of nucleic acid extraction and thermal cycling requirements. By synergizing enzymatic cascade amplification with selfpowered energy storage, this work establishes a new paradigm for plant pathogen diagnostics that bridges the gap between laboratory-grade sensitivity and field-deployable practicality.

Atherosclerosis (AS) is a risk factor for various cardiovascular diseases (CVD), and early diagnosis and plaque removal are challenges in the management of AS and CVD. To address these challenges, we designed a tungsten‑doped polydopamine nanoparticle (W-PDA NPs). The near-infrared (NIR) optical absorption and enzymatic properties of W-PDA were tuned by adjusting the ratio of W6+ to PDA. It was found that the W-PDA NPs contain a mixture of W6+ and W5+, which generated large amounts of free electrons, thus conferring both second near-infrared (NIR-II) light absorption capability and superoxide dismutase (SOD)- and catalase (CAT)-like activities. Plaque-targeting was conferred by appending an anti-osteopontin antibody via coordination between carboxyl moieties of the antibody with W6+ ions on the W-PDA surface. The NIR-II light absorption ability can be utilized for photoacoustic diagnosis of AS, while the SOD- and CAT-like activities facilitate the conversion of superoxide into H2O2 and subsequently O2, thereby clearing reactive oxygen species and mitigating inflammation, enabling therapy of AS. Thus, the proposed theranostic agent not only can dynamically report the progression of AS but also can effectively treat AS plaques. This strategy holds broad application prospects in the early and accurate diagnosis and treatment of AS.

A self-healing /i-cyclodextrin-polyvinyl alcohol-Ag nanoparticles (/i-CD-PVA-AgNPs) hydrogel was fabricated as a surface-enhanced Raman scattering (SERS) substrate by incorporating /i-cyclodextrin (/i-CD) and Ag nanoparticles (AgNPs) into a polyvinyl alcohol (PVA) network via a dual crosslinking strategy combining boraxmediated chemical crosslinking and freeze-thaw physical crosslinking. The substrate enables selective capture of target molecules through host-guest recognition of /i-CD, while drying-induced volume shrinkage drives dense packing of AgNPs to generate Raman "hot spots" for significant SERS signal enhancement. Additionally, the selfhealing property effectively repairs microcracks formed during drying, improving surface flatness and signal uniformity. To ensure quantitative accuracy, 4-mercaptobenzoic acid was employed as an internal standard to calibrate signal fluctuations. The SERS performance was evaluated using melamine and paraquat as target analytes, demonstrating high detection sensitivity with limits of detection of 36.7 mu g/L and 11.6 mu g/L, respectively. The hydrogel also exhibited excellent selectivity by effectively distinguishing target molecules from their structural analogues, along with good homogeneity and reproducibility. When applied to the detection of melamine in dairy products and paraquat in vegetables, the substrate achieved recoveries of 90.5%-108.8% with relative standard deviations below 6.7%, showing good agreement with high-performance liquid chromatography (HPLC) results. Notably, the entire "adsorption-enrichment-detection" workflow was completed within merely 9 min. Integrating selective capture, enrichment, and rapid detection into an all-in-one substrate, this substrate offers the combined advantages of simplified sample pretreatment and high sensitivity, holding considerable promise for on-site rapid detection applications.

Early cancer diagnosis in remote regions demands point-of-care platforms that are sensitive, self-powered, and robust without specialized infrastructure. Conventional enzyme-based biofuel cells suffer from poor environmental stability. Herein, we developed self-powered biosensor integrating cascade DNA walking amplification with a Pt/h-Co-Nc nanozyme-modified hydrogel air-fuel cell for precise detection of miRNA-221. The air-fuel cell combines high energy density of metal-air batteries with fuel cell simplicity, enabling stable power generation without external electricity. The sensor operates via target-triggered, Mg2+-dependent DNAzyme walking that releases a Pt/h-Co-Nc nanozyme from the hydrogel cathode. This nanozyme, featuring synergistic Pt/Co-Nx active sites, exhibits exceptional and durable catalytic activity for the oxygen reduction reaction, overcoming the denaturation issues of natural enzymes under ambient temperature fluctuations. The device generates a current signal proportional to miRNA-221 concentration, achieving a wide linear range (10 fM to 100 pM) with a low detection limit of 10.8 fM (S/N = 3). Crucially, it demonstrates outstanding operational stability, with signal decay below 3% over 12 days at room temperature, and high specificity against interfering miRNAs. Its practical utility is validated by analyzing miRNA-221 in clinical human serum samples with satisfactory recoveries. By synergistically merging advanced nanozyme catalysis with a robust, self-sustaining air-fuel cell design, this work establishes a powerful paradigm for field-deployable cancer biomarker detection, offering a transformative approach for on-site diagnostics in remote and resource-limited healthcare settings.

Sugarcane pokkah boeng disease poses a serious threat to global sugar production, highlighting an urgent need for on-site detection tools that are both highly sensitive and operable without sophisticated instrumentation. Herein, we report a self-powered biosensing platform integrating electrochemical, colorimetric, and photothermal tri-modal readouts assisted by machine learning for ultrasensitive and specific detection of the pokkah boeng pathogen. The sensing interface is constructed using a MWCNT@ZIF-8/AuNPs nanocomposite, which facilitates electron transfer and offers abundant active sites. By leveraging a cascaded amplification strategy involving DNAzyme cleavage and Exonuclease III (ExoIII)-assisted recycling, the system achieves significant signal enhancement without requiring multiple enzyme systems or complex sequence designs. The incorporation of a G-quadruplex/hemin DNAzyme enables not only colorimetric signaling through TMB oxidation but also robust near-infrared photothermal conversion, thereby complementing the electrochemical and colorimetric modes. Furthermore, machine learning algorithms, including Linear Regression, Ridge Regression, Lasso Regression, and SGD Regressor, are employed to model the multi-modal data, substantially improving prediction accuracy and robustness through cross-validation. The proposed biosensor demonstrates exceptional sensitivity with detection limits of 20.41 aM (electrochemical), 25.36 aM (colorimetric), and 18.91 fM (photothermal), across broad linear ranges. Practical applicability is confirmed through spike-recovery assays in real sugarcane leaf extracts, yielding satisfactory recoveries (99.0-108.0%) and high reproducibility (RSD < 5%). This work provides a reliable and portable strategy for early plant disease monitoring and showcases the promising integration of multimodal sensing with machine learning for agricultural diagnostics.

BACKGROUND:Ensuring the safety of dairy products requires rapid, sensitive, and cost-effective detection of chemical contaminants. RESULTS:We developed a green and versatile plasmonic aerogel SERS substrate via covalent cross-linking of κ-carrageenan and dialdehyde cellulose (DAC), decorated with in situ-synthesized silver nanoparticles (Ag NPs). The aerogel exhibits a hydrothermal-responsive structural transition: its 3D porous network enables efficient adsorption of contaminants in aqueous phase, while thermal drying induces a controlled collapse into a 2D layered configuration, uniformly aggregating Ag NPs and generating high-density electromagnetic hotspots. This enables ultrasensitive SERS detection with excellent reproducibility and stability. Detection limits for melamine (Mel) and thiram (TH) in milk are 53 μg/L and 75 μg/L, respectively, and 87 μg/L for Mel in milk powder. SIGNIFICANCE:These detection limits meet regulatory requirements (EFSA: Mel/TH ≤ 1 mg/kg; Chinese Standard: Mel ≤1 mg/kg, TH ≤ 2 mg/kg). Real milk samples achieved recoveries of 97.6-107.1% (Mel) and 96.3-105.6% (TH), demonstrating high accuracy and reliability. The entire process is completed within 30 min, offering operational simplicity, low cost, environmental friendliness, and a promising platform for on-site monitoring of chemical contaminants in dairy products.

A DNA-logic-gated, trimodal, self-powered biosensing platform is developed for the rapid, simultaneous, and on-site detection of two major sugarcane pathogens, Sporisorium scitamineum (smut) and Fusarium sacchari (pokkah boeng). Conventional diagnostic techniques such as qPCR are considered time-consuming, laboratory-dependent, and unsuitable for field deployment. To overcome these limitations, the proposed device integrates electrochemical, colorimetric, and photothermal signal readouts into a single portable system, powered by an enzymatic biofuel cell and operated via a smartphone interface. The sensing mechanism is orchestrated by target-specific DNA logic circuits. In the presence of smut DNA, glucose oxidase is released, generating an electrochemical current and a color shift in a methylene blue-based electrolyte. For pokkah boeng, an AuCo nanozyme is activated, catalyzing the oxidation of TMB to yield a blue color and a distinct photothermal response under NIR laser irradiation. Both pathways are triggered orthogonally without cross-interference. Multimodal signals are processed using a machine learning-assisted random forest regression model. The smut detection model achieves an R2 value of 0.982 with a learning rate of 0.024 and 63% training data allocation, while the pokkah boeng model attains perfect accuracy (R2 = 1.000) at a learning rate of 0.001 and a 90% training split. The platform demonstrates high sensitivity, with detection limits as low as 3.7 × 10-16 M for smut and 2.3 × 10-16 M for pokkah boeng, along with excellent repeatability and stability. Validation using field samples collected from infected sugarcane plants shows high consistency with standard qPCR assays. The system provides a powerful, low-cost, and user-friendly tool for early disease warning and represents a universal strategy for multiplex pathogen screening in smart agriculture frameworks.

This study pioneers a enzymatic biofuel cell that fundamentally redefines portable detection of sugarcane pokkah boeng disease through synergistic nanomaterial engineering and molecular circuitry design. As a single-enzyme architecture, Exo III-mediated molecular circumvents nonspecific interference inherent in multi-enzyme systems while achieving dual amplification cycles. This enzymatic machinery drives a magnetic nanomachine for target recycling, synergizing with heterostructured MoS2-AuNPs that exhibits exceptional photothermal conversion (62.71% efficiency) and engineered electron transfer pathways. Bimetallic CuCo-MOF@C/PDA nanocomposites amplify system performance through their dual functionality, which can enhance glucose oxidase activity at the bioanode (Km = 0.0557 mM for H2O2). Crucially, the platform introduces a mathematical validation framework that formalizing triple-mode agreement to eliminate false positives. The integrated three-mode detection platform achieves unprecedented sensitivity, with detection limits of 3.42 × 10-17 M (electrochemical), 1.08 × 10-17 M (colorimetric), and 1.78 × 10-15 M (photothermal), maintaining over 96% signal fidelity after long-term storage. Validated in real sugarcane samples (97.3-104.4% recovery), this work establishes a new paradigm for portable mobile plant pathogen diagnostics, where self-powered operation converges with built-in analytical redundancy to deliver laboratory-grade accuracy in mobile detection.