In this manuscript, two newly designed near-infrared (NIR) emitting derivatives were synthesized by reacting dicyanoisophorone with 2-hydroxy5-nitrobenzaldehyde (IC25) and 2,4,5-trimethoxybenzaldehyde (IC27) through Knoevenagel condensation reactions. The compounds were structurally characterized...
Accurate detection of the widely used organophosphorus pesticide chlorpyrifos (CPF) is of great significance for the ecology and human health. In this study, a dual-recognition electrochemical sensing platform was constructed...
Plastic-mulched soils contain mineral particles, organic matter and weathered polymer fragments that complicate the joint measurement of microplastics and co-occurring antibiotics. We developed a matrix-adaptive workflow combining sequential Fenton-enzymatic digestion,...
Functionalized carbon dot nanoparticles are at the forefront of biological applications of carbon nanoparticles (CNPs), particularly in bioimaging and biosensing. In this study, both functionalized and unfunctionalized CNPs were utilized as fluorescent probes in cancer cell lines and Drosophila melanogaster. A human T lymphocyte (Jurkat), the MCF 10A cell line, and the wild-type Oregon strains of D. melanogaster were used in the study. N-doped CNPs were synthesized solely from o-phenylenediamine (oPD CNPs), and thiol-functionalized CNPs were synthesized from o-phenylenediamine and L-cysteine (SH-CNPs), both via a microwave-assisted synthesis. The synthesized SH-CNPs show maximum excitation and emission intensity at 340 nm and 412 nm, respectively. UV-vis absorption spectra present absorption peaks at 238 nm, ∼279 nm, and ∼297 nm, which are attributed to π → π* and n → π* transition states in thiol-containing aromatic systems. CNPs were spherical and polydisperse, with an average size of 64 ± 29 nm. Chemical characterization revealed the presence of the -SH functional group, characterized by an infrared absorption band at ∼2550 cm-1, alongside other functional groups (OH, CO, COO, CC, C-C, NH, NH2) on the surface of both the CNPs and their core. X-ray photoelectron spectroscopy analysis of S 2p also shows the binding energy of ∼164 eV allotted to C-SH. Scanning Electron Microscope/Energy Dispersive Spectroscope reveals the weight percentage (wt%) of the elemental composition of SH-CNPs to be 62.9 ± 0.2%, 17.2 ± 0.2%, 14.7 ± 0.1%, and 5.2 ± 0.1% for carbon, nitrogen, oxygen, and sulfur, respectively. A significant difference in the intensity of the SH-CNPs fluorescence between larvae treated with hydrogen peroxide (H2O2) and larvae treated with potassium superoxide (KO2), with the latter group showing reduced fluorescence intensity. Taken together, these results demonstrate that surface functionalization plays a critical role in modulating the biological response and sensing capability of carbon dot nanoparticles. While unfunctionalized oPD CNPs are effective for structural bioimaging, thiol-functionalized CNPs provide additional functional sensitivity to oxidative stress conditions.
Microplastics (MPs) are an emerging pollutant of global concern, creating an urgent need for rapid and accurate monitoring workflows. Deep learning-based computer vision has demonstrated strong performance in finding particles in microscopy images, but its use as a front-end module for automated IR/Raman microscope-based MP analysis remains insufficiently developed, particularly in workflows that convert image-level particle detection results into microscope-executable operations for particle morphological characterization and spectral acquisition planning. Here, we aim to address this gap. First, reference MPs were deposited on Anodisc filters and glass slides, from which 626 bright-field micrographs containing 7010 particles were collected. The dataset was randomly split into training, validation, and test sets in a 7 : 2 : 1 ratio, and an Ultralytics YOLO11 instance segmentation model was developed. At an intersection-over-union (IoU) threshold of 0.7, testing achieved precision, recall, and F1 scores of 0.86, 0.93, and 0.89, respectively, outperforming two benchmark methods: a threshold-based method (F1 = 0.25) and Mask R-CNN (F1 = 0.84). On another test set prepared from tap water (with a relatively clean background), the model achieved precision, recall, and F1 scores of 0.78, 0.90, and 0.83 at IoU = 0.7. However, particle detection performance decreased with dirtier image backgrounds, as shown by testing on the sample prepared from commercial salt. We addressed key technical challenges required for end-to-end automation, including per-field autofocus, recovering true microscope coordinates from YOLO style outputs, converting detections into particle descriptors for downstream characterization, and programmatic microscope control for image collection and spectrum acquisition planning. Full implementation, code, and well annotated data are released openly, enabling adoption and extension of this workflow for broader MP monitoring applications.
Rapid analysis of food and agricultural products is important for evaluating quality and detecting contaminants, but the chemical complexity and physical heterogeneity of these matrices can make traditional workflows challenging and time-consuming. In our previous work, a touch sensor for sheath-flow probe electrospray ionization (sfPESI) was developed to enable automated analysis of liquid samples by detecting displacement currents upon surface contact. By coupling the sfPESI probe with a table-top 3-axis robot, consecutive point analyses of various complex samples were performed. However, its applicability was limited to liquid and wet solid matrices. In this work, a touch sensor equipped with a lock-in amplifier was developed to enable the automated analysis of both wet and dry samples. The system was demonstrated across a range of matrices, including vegetables, meats, insecticides, and insect repellents, substantially expanding the applicability of automated sfPESI for rapid, surface-resolved chemical analysis of complex food and agricultural products.
Paraquat (PQ), a highly toxic herbicide banned in several countries yet still widely used globally, poses severe threats to human health and ecosystems through acute poisoning and chronic exposure via contaminated food and water. Despite numerous analytical methods, rapid, sensitive, and field-deployable detection remains essential for effective monitoring and regulatory enforcement. Unlike existing reviews focusing narrowly on specific nanomaterials or detection techniques, this review represents a systematic cross-platform comparison evaluating metallic nanoparticles (Au, Ag, Pt), carbon-based materials (graphene, carbon nanotubes, quantum dots), metal-organic frameworks, and hybrid nanocomposites across electrochemical detection mechanisms. Critically, this review examines how food and environmental matrix effects influence sensor performance and discusses the analytical validation requirements necessary for reliable paraquat determination, thereby addressing a major gap in studies that predominantly report performance under idealized buffer conditions. Novel enhancement strategies are evaluated, including molecularly imprinted polymers, aptamer functionalization, and disposable electrode integration for point-of-use testing. This review uniquely addresses the translational research gap by identifying why promising laboratory sensors fail in real-world applications, including challenges associated with matrix interference, selectivity, long-term stability, reproducibility, and practical validation based on reports over the past decade. Furthermore, economic feasibility and lifecycle aspects of nanomaterial-enabled sensing platforms are critically discussed to evaluate their potential for deployment in resource-limited settings. Thus, the review provides clear guidance for developing next-generation sensors capable of protecting public health through effective paraquat monitoring.
Ethanol, as a food additive, can effectively extend product shelf-life and improve food texture. However, its dosage requires stringent control, making ethanol quantification a critical quality assurance requirement in baked goods. Consequently, developing a rapid and simple method for detecting ethanol in baked food products is of great practical significance. This study fabricated a three-dimensional photonic crystal (PC) membrane by integrating plant membranes and a PC, utilizing an onion epidermal membrane as the natural substrate. PC arrays were imprinted onto the onion membrane at room temperature. The resulting onion-derived PC membrane biosensor demonstrated not only high sensitivity but also excellent selectivity towards ethanol. During detection, the spectral reflection peak of the onion-PC film shifted towards the red as the ethanol concentration increased, achieving signal stabilization within 5 min. Moreover, ethanol detection induced visually observable structural color changes (yellow-green to orange-red) on the membrane. The biosensor exhibited excellent practicability and retained stable performance after multiple reuse cycles. Overall, this onion-PC membrane demonstrates significant potential for the visual and rapid detection of ethanol in baked foods.
N-Acetylcysteine (NAC), a thiol-containing mucolytic agent for COPD, exhibits significant pharmacokinetic variability among patients, motivating the need for convenient therapeutic monitoring. Herein, a colorimetric platform based on copper-doped chiral carbon dots (Cu-D-CDs) was synthesized via a one-pot hydrothermal route using D-histidine and CuCl2 as precursors. The Cu-D-CDs displayed enhanced peroxidase-like activity, catalyzing H2O2-mediated oxidation of TMB to blue oxTMB (λmax = 652 nm). Upon NAC introduction, oxTMB was reduced via a thiol-disulfide redox reaction, producing an absorbance decrease proportional to NAC concentration. Under optimized conditions, the assay exhibited a linear dynamic range of 10-90 µM, a detection limit of 3.74 µM (LOD = 3σ/S), and recoveries of 96.00-106.60% (RSD < 4%) in mouse serum, demonstrating its feasibility for NAC quantification in serum samples. Beyond the single-wavelength readout at 652 nm, the full-spectral data were further processed by an LSTM network, which significantly improved prediction accuracy (R2 > 0.9998) compared with single-wavelength calibration (R2 = 0.9979), effectively mitigating matrix interference. This integrated colorimetric-LSTM strategy shows promise for COPD therapeutic drug monitoring and pharmaceutical quality control.
Rizatriptan benzoate (RZB), a first-line therapy for the acute treatment of migraine, requires sensitive and reliable monitoring in pharmaceutical formulations and biological matrices to ensure effective quality control and therapeutic drug monitoring. In this work, a sustainable, novel and cost-effective electrochemical sensor was established for the sensitive determination of RZB by modifying a carbon paste electrode (CPE) with nickel-doped zinc oxide nanoparticles (Ni-ZnO NPs) via a simple precipitation procedure. The prepared nanoparticles were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX), and Fourier-transform infrared spectroscopy (FT-IR), confirming their crystalline nature. The Ni-ZnO/CPE demonstrated remarkable electrocatalytic efficiency for RZB oxidation in phosphate buffer solution at pH 6.0. Utilizing differential pulse voltammetry, the sensor showed linearity over the concentration interval of 0.04-20 µM. Moreover, the sensor showed excellent repeatability and intermediate precision, with relative standard deviation (RSD) values below 1.5%. The sensor was effectively employed for RZB detection in tablets and human plasma, exhibiting adequate recovery rates of 99.71-101.26% and 100.22-101.26%, respectively. The suggested approach revealed favorable analytical performance compared with previously reported DPV procedures, offering a lower limit of detection of 0.01 µM and enhanced sensitivity. Moreover, this is the first reported electrochemical method for RZB that combines drug determination with a comprehensive environmental assessment. Environmental sustainability was assessed utilizing modern metrics for greenness, blueness, and whiteness, and the findings revealed an excellent eco-profile with strong compliance with the principles of green analytical chemistry.
The gut microbiota influences host metabolism and synthesizes essential vitamins. Human Milk Oligosaccharides (HMOs), diverse non-conjugated polysaccharides, are the third major solid component in breast milk. We assessed maternal HMO...
In recent decades, smartphone-based analytical methods have gained increasing attention, mainly due to their widespread user-friendliness, enhanced computational capabilities, cost-effectiveness, and the ability to simultaneously acquire and process data. Accurate...
Near-infrared spectroscopy provides a non-destructive and rapid route for soil analysis. However, conventional chemometric models based on nonlinear supervised learning remain limited for complex samples and multi-characteristics prediction. This paper proposes a chemometric framework based on spectral-characteristics fusion and minimization of representation mapping or prediction error. First, spectral-characteristics fusion integrated spectral data with single or multiple soil characteristics. Subsequently, quantitative models were constructed using a supervised spectral-to-compositional representation mapping model and a dynamic series forecasting model, which minimize the spectral-to-compositional representation mapping error and the prediction error, respectively. The dynamic series forecasting model was constructed based on dynamic sequential data analogous to time series data derived from spectral-characteristics fused data, with sliding windows covering all soil-characteristic positions. Experiments on organic samples from the LUCAS 2009 topsoil data showed that fused data with great continuity facilitated model construction, and the proposed framework achieved higher predictive accuracy than the selected conventional supervised learning baselines. This paper provides a near-infrared spectral-characteristics fusion and error-minimized prediction strategy for compositional analysis of complex samples, with potential applicability beyond soil analysis.
The increasing demand for decentralized healthcare has accelerated the development of portable point-of-care (POC) diagnostic systems capable of providing rapid, accurate, and user-friendly glucose monitoring. Herein, we report an Fe-Zn bimetallic nanozyme-based colorimetric sensing platform that integrates smartphone-assisted digital image analysis and Arduino-based signal acquisition for portable glucose determination. The Fe-Zn bimetallic nanoparticles (Fe-Zn BNPs) were synthesized through a simple aqueous route and exhibited excellent intrinsic peroxidase-like activity toward the hydrogen peroxide-mediated oxidation of 3,3',5,5'-tetramethylbenzidine (TMB). Coupling the nanozyme with a glucose oxidase (GOx)-mediated cascade reaction enabled sensitive and selective glucose detection through catalytic signal amplification. The synthesized Fe-Zn BNPs exhibited excellent peroxidase-like catalytic activity toward hydrogen peroxide with a broad linear range of 10-700 µM and an excellent correlation coefficient (R2 = 0.9996). Steady-state kinetic analysis revealed a low apparent Km of 0.0647 mM and a Vmax of 1.75 × 10-9 M s-1 confirming the high substrate affinity and catalytic efficiency of the developed nanozyme. The proposed glucose sensing platform displayed a wide linear range of 10-500 µM with excellent analytical performance using UV-Vis spectrophotometry (R2 = 0.9980), Arduino-assisted detection (R2 = 0.9975), and smartphone-based digital image analysis (R2 = 0.9967). The platform demonstrated high selectivity toward glucose over common biological interferents and was successfully validated in human serum providing a glucose concentration of 5.07 ± 0.15 mM which was in excellent agreement with that of the reference clinical method (4.93 ± 0.14 mM). The environmental sustainability of the developed platform was comprehensively assessed using the Nanomaterial Assessment Tool (NAT), Blue Applicability Grade Index (BAGI), Red Analytical Performance Index (RAPI), and Environmental Performance-Practicality Index (EPPI) achieving scores of 78, 75, 70, and 84.8, respectively, demonstrating excellent compliance with the principles of green and white analytical chemistry. By integrating efficient Fe-Zn nanozyme catalysis with dual smartphone-Arduino portable detection, the proposed platform provides a sensitive, sustainable, and practical strategy for next-generation point-of-care glucose diagnostics.
The objective of this study was to establish an effective sample preparation protocol for the determination of pesticide residues in tobacco. Two distinct cleanup approaches were evaluated: the classical QuEChERS method and a protocol utilizing EMR-Lipid. All prepared samples were analyzed via gas chromatography-tandem mass spectrometry (GC-MS/MS), and the optimally selected approach was subjected to method validation. This is the first study to use EMR-Lipid as a dispersive solid-phase extraction (d-SPE) sorbent for pesticide detection in tobacco. Compared with the conventional PSA-based cleanup approach, the EMR-Lipid method substantially alleviated matrix interference, reducing the number of pesticides exhibiting high matrix effects from 30 to 11. It also improved recovery performance, with 94% of the target pesticides showing recoveries within 70-110% at the 0.5 mg kg-1 spiking level. Validation experiments for the EMR-Lipid method showed good linearity, with coefficients of determination (R2 > 0.99). The limits of quantification (LOQs) ranged from 2.0 to 10.0 µg kg-1. The RSDs for all selected pesticides ranged from 0.7% to 15.9%. This method was applied for the first time to determine pesticide residues in tobacco samples from Jiangxi Province, China. The methodology demonstrated acceptable accuracy and precision for multi-residue analysis.
Aquaculture expansion has exacerbated microplastics (MPs) contamination in aquaculture water bodies. MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks. Conventional detection techniques including microscopic observation, FTIR and Py-GC-MS are limited by low identification accuracy, matrix interference, destructiveness and cumbersome procedures, which cannot support rapid large-scale monitoring. Raman spectroscopy enables non-destructive detection with unique molecular fingerprinting, yet spectral overlap, background noise and subtle crystallinity differences among similar plastics hinder accurate manual classification. In this study, Raman spectroscopy was integrated with machine learning to establish a rapid plastic particle classification approach. Raman spectra of thirteen typical standard MPs were acquired, preprocessed and dimensionally reduced by PCA-LDA. Seven machine learning models were constructed and optimized by cross-validation, and further validated using spiked aquaculture wastewater samples. All models achieved classification accuracies above 98%. Among them, K-nearest neighbor (KNN), naive Bayes (NB), support vector machine (SVM), and logistic regression (LR) exhibited superior classification performance due to their effective feature discrimination capability and adaptability to high-dimensional Raman spectral data, enabling accurate classification of plastic particles according to polymer types under complex aquatic matrices. The proposed method integrates the molecular fingerprinting capability of Raman spectroscopy with the feature-learning advantages of machine learning, effectively overcoming the limitations of conventional spectral interpretation. This portable and non-destructive strategy provides a reliable approach for rapid plastic particle classification, pollution source tracing, and ecological risk assessment in aquatic environments.
The concentration of iron ions is a crucial indicator for assessing water quality. In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and urea as precursors, thereby establishing a fluorescence sensing platform for the detection of alkaline pH and Fe3+. During Fe3+ detection, the fluorescence intensity of NCDs was specifically quenched as the concentration of Fe3+ increased, demonstrating good linearity across the ranges of 1-10 µM and 10-100 µM, with a detection limit of 0.55 µM. By integrating smartphone-based image analysis, visual semi-quantitative detection of alkaline pH and Fe3+ was achieved. To enhance prediction accuracy across a broad concentration range, a machine learning model was introduced to develop a high-precision quantitative analysis method for Fe3+. The spiked recovery rates in actual water samples ranged from 99.26% to 101.14%, with relative standard deviations below 3%. This platform combines fluorescence sensing, smartphone imaging, and machine learning technologies, offering the advantages of simple operation and low cost, thus providing a novel strategy for the on-site rapid detection of Fe3+ in water environments.
Zearalenone (ZEN) is an estrogenic mycotoxin commonly found in cereals, animal feed, and processed foods, making it an important concern for food safety and public health. Conventional chromatographic and immunological methods can detect ZEN; however, they often require expensive instruments, lengthy sample preparation, and skilled personnel, which restrict their use for rapid and on-site testing. Electrochemical sensors have attracted enormous interest of the scientific community because of their high sensitivity, rapid response, low cost, miniaturization potential, and compatibility with portable systems. The analytical performance of the electrochemical sensors is strongly influenced by electrode materials, morphology, conductivity, porosity, surface functionality, and the efficiency of bioreceptor immobilization. Despite several reviews on mycotoxin detection, a systematic assessment connecting electrode-material design, modification strategies, sensing mechanisms, and electroanalytical performance specifically for ZEN sensing remain limited. This review critically evaluates recent advances in metal oxides, carbon-based materials, metal-organic- and covalent organic frameworks, MXenes, polymers, and hybrid composites for electrochemical ZEN detection. Particular attention has been given to their roles in electron transfer, analyte enrichment, selectivity, and real-sample analysis. The review also compares the major limitations of current sensing systems, including complex fabrication, matrix interference, insufficient long-term stability, poor inter-electrode reproducibility, and limited scalability. Finally, future directions for developing robust, cost-effective, portable, and commercially viable ZEN sensors are discussed.
Tau protein is an important biomarker which is associated with neurodegenerative disorders, particularly Alzheimer's disease, where abnormal tau aggregation and hyperphosphorylation contribute to neuronal dysfunction and cognitive decline. The sensitive and early detection of tau protein is therefore crucial for the diagnosis, prognosis and therapeutic monitoring of neurodegenerative diseases. Owing to its clinical significance, the development of rapid, highly sensitive and reliable analytical platforms for tau detection has gained considerable attention in biomedical diagnostics. In this work, a Ru(II) based homogeneous sandwich ECL immunosensor was developed for the turn-on detection of tau. A polyclonal antibody was conjugated with Ru(II)@AuNP, which served as the ECL signal probe, and further integrated with monoclonal antibody conjugated L-Cys-CuNC to construct the ECL immunoassay system. Upon the addition of tau protein, the ECL intensity was enhanced due to the specific antigen-antibody interaction. The probe is selective and sensitive towards tau with a limit of detection of 0.23 pg mL-1. Also, the probe is validated with spiked human serum samples with a recovery percentage of 90 to 104%.
Esterases constitute vital hydrolases participating in lipid metabolism, prodrug activation, and tumor progression. Due to the significant heterogeneity in their expression levels across different types of tumor microenvironments, esterases have emerged as promising biomarkers for cancer diagnosis. This probe achieves fluorescence quenching through an intramolecular photoinduced electron transfer (PET) mechanism. Under the catalysis of the esterase, the amide bond is hydrolyzed and the heptafluorobutyramide group is cleaved, restoring the electron-donating capacity of the amino group, blocking the PET effect, and restoring the fluorescence signal. CYF exhibits high sensitivity (limit of detection as low as 0.04749 U mL-1), excellent selectivity, and good photostability. Molecular docking and theoretical simulations show that CYF can stably bind to the esterase's active pocket through multiple molecular forces, with a binding affinity of -9.3 kcal mol-1. The HOMO-LUMO band gap clearly elucidates the probe's fluorescence response mechanism. CYF demonstrates good biocompatibility and low cytotoxicity, effectively distinguishing disparities in esterase activity between normal thyroid cells (Nthy-ori3-1) and thyroid cancer cells (TPC-1), with significantly upregulated esterase activity observed in TPC-1 cells. In 4T1 tumor-bearing mice, the fluorescence intensity of probe CYF in the tumor region was significantly lower than in normal tissue; this signal was further suppressed following pretreatment with the inhibitor AEBSF. These results confirm the low expression of esterase activity in the breast cancer intratumoral microenvironment and demonstrate CYF's capability for the time-dependent, in situ dynamic tracking of esterase activity in vivo.
