Analytical Methods最新文献

Rapid and reliable determination of toxic dyes in complex aqueous environments remains challenging because matrix interference and complicated sample pretreatment often compromise analytical accuracy and practical applicability. Therefore, an integrated filtration-adsorption-SERS sensing platform coupled with one-dimensional convolutional neural network (1D-CNN) modeling was fabricated for rapid and quantitative detection of malachite green (MG) and methylene blue (MB) in complex water samples. Polypropylene filter cloth (PPFC) served as a porous fibrous support for sample filtration, while Ag nanoparticles (Ag NPs) were uniformly immobilized on the fiber surfaces via a seed-mediated in situ growth strategy to provide SERS-active sites. Activated carbon was incorporated into the PPFC/Ag filter configuration to provide adsorption of the target dyes during sample treatment. The resulting configuration integrates filtration, adsorption, and SERS detection within a common analytical workflow, while 1D-CNN modeling was employed for quantitative analysis of the acquired SERS spectra. Benefiting from the integrated filtration-adsorption configuration and porous fibrous architecture, the platform exhibited high signal uniformity and favorable analytical performance, with detection limits of 10^-9 M for both MG and MB. Adsorption kinetic analysis demonstrated that dye removal followed the pseudo-second-order model, with equilibrium removal efficiencies of 99.7% for MB and 98.6% for MG. To improve quantitative reliability in complex matrices, a 1D-CNN model was employed for SERS spectral analysis, yielding high accuracy with R² values of 0.992 and 0.982 for MB and MG, respectively, with low prediction errors. Furthermore, spiked Moyan Lake water samples showed recoveries of 98.14% and 96.37% for MB and MG, respectively, with RSD values of 4.53% and 4.91%, demonstrating satisfactory accuracy and precision in a real water matrix. By integrating these complementary functions within the same filtration configuration, the proposed platform establishes a continuous analytical workflow from sample filtration and adsorption to SERS detection and quantitative analysis. This integrated design simplifies the conventional multi-step procedure and provides a sensitive, reliable, and practical approach for rapid monitoring of trace organic dyes in complex water matrices.

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.

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.

A novel fluorescent probe EFC, featuring a 4H-furo[3,2-b]pyrrole scaffold, was designed, synthesized, and investigated for its optical properties. EFC was identified as a novel hydrazine probe, which undergoes a nucleophilic substitution reaction with hydrazine. In the presence of hydrazine, EFC exhibits a color change from yellow to colorless under daylight, and a fluorescence change from yellow to blue under UV illumination. Additionally, EFC demonstrates acid-base tolerance, coupled with high selectivity and sensitivity for residual N₂H₄. Furthermore, paper strips loaded with EFC were fabricated and used with a smartphone to enable point-of-care visual detection of N₂H₄. The use of EFC for detecting N₂H₄ in real water samples demonstrated recovery rates spanning from 98.60% to 101.36%, with relative standard deviations between 1.72% and 1.74%, thereby affirming the method's accuracy and reliability. Furthermore, the probe exhibits equivalent efficacy in identifying residual hydrazine within soil samples and a silica support. Consequently, this research introduces an innovative fluorescent probe for hydrazine detection, distinguished by its cost-effectiveness, facile fabrication process, and simple operation.

Adiponectin (ADI) and leptin (LEP) have been proposed as two specific biomarkers of dysfunctional adipose tissue. We aim to establish a one-step time-resolved fluorescence immunochromatographic assay (TRFICA) for simultaneous quantitative detection of ADI and LEP and to evaluate its application value in patients with metabolic syndrome (MS). The TRFICA, using europium(III) (Eu³⁺) fluorescent microspheres and based on the double-antibody sandwich format, for the quantitative detection of ADI and LEP was individually optimized and established. Subsequently, a dual-target test strip based on TRFICA (TRFICAS) was assembled and evaluated using the clinical samples. The dual-target TRFIAS allows for quantitative detection of serum ADI and LEP within 15 min. Its sensitivity for detecting ADI was 0.01 µg mL^-1, and for LEP it was 0.07 ng mL^-1, with high specificity. The intra-batch coefficient of variation (CV) and the inter-batch CV were both <11.62%, and recovery ranged from 105.33% to 119.17%. Moreover, strong Pearson correlations were observed between the results measured by the dual-target TRFICAS assay and those obtained from commercial kits (Pearson r = 0.9183 and 0.9051, respectively). Applied to sera from healthy donors and MS patients, the assay clearly distinguished MS patients from the healthy donors. The dual-target TRFICAS has good sensitivity, specificity, precision, a simple procedure, rapid detection and long-term stability, and it is suitable for rapid and quantitative determination of serum ADI and LEP on-site.

R-loops are three-stranded nucleic acid structures composed of an RNA : DNA hybrid and a displaced single-stranded DNA strand, and they exert dual roles in transcriptional regulation, DNA damage repair, and the maintenance of genome stability. Over the past decade, a diverse array of techniques has been developed to detect and characterize R-loops at three complementary levels: genome-wide mapping, subcellular localization imaging, and interactome analysis. This review summarizes the principal methodological routes in these three areas, compares the basic principles and applicable scenarios of different strategies, and discusses their differences in specificity, resolution, sample requirements, and possible sources of assay-dependent variation. On this basis, we further consider the major challenges and future directions in the field, with the aim of providing a methodological reference for understanding the biological functions of R-loops and their relevance to disease diagnosis and treatment.

Growing demand for rapid, sensitive, and affordable analytical tools in healthcare, environmental monitoring, and food safety is driving interest in sustainable carbon nanomaterials for biosensors. Biochar, derived from biomass via pyrolysis or hydrothermal carbonization, has emerged as a promising candidate due to its tunable porosity, high surface area, rich surface functionalities, and low cost from waste feedstocks. This review highlights biochar as a sustainable biosensing component, beginning with an overview of modern biosensor technologies and carbonaceous nanomaterials' role in enhancing transduction efficiency, sensitivity, and selectivity. This review examines how thermochemical conversion routes, process parameters (temperature, heating rate, and residence time), and feedstock characteristics govern key biochar properties-pore structure, graphitization, surface functional groups, and heteroatom content-which determine electrochemical behavior and bioreceptor immobilization capacity. Special attention is given to engineered and nano-scaled biochars, where activation, heteroatom doping, and composite formation with metals, metal oxides, and carbon allotropes address limitations including moderate surface area, structural heterogeneity, and limited electroactive sites. Applications of biochar-based electrochemical and optical biosensors for detecting small molecules, heavy metals, pollutants, and clinical biomarkers are consolidated, emphasizing limits of detection, sensitivity, linear range, response time, and stability. Sustainability and techno-economic aspects-feedstock availability, energy requirements, co-product valorization, life-cycle impacts, and cost competitiveness-are examined. Persistent challenges including batch-to-batch variability, lack of standardization, scalable functionalization difficulties, and environmental fate uncertainties are analyzed. Finally, emerging opportunities at the interface of biochar science, artificial intelligence, and advanced manufacturing are outlined, defining priorities for translating laboratory progress into reliable real-world diagnostic platforms.

Phosphates are commonly used food additives; however, their excessive use or long-term consumption may pose health risks, making their accurate detection crucial for food safety and human health. In this study, 2-(4-carboxyphenyl)imidazo[4,5-f]-1,10-phenanthroline (HNCP) and pyrazine-2,5-dicarboxylic acid (H₂PZDC) were used as ligands to prepare a rare-earth ratio-based fluorescent probe (Eu-NCP-PZDC). This fluorescent probe was successfully applied to the detection of PO₄^3-, demonstrating advantages such as a fast response time (2 min), a low limit of detection (48 nM), a wide linear range (0-320 µM), and good selectivity. Furthermore, by loading this probe onto filter paper and combining it with color recognition software on a smartphone, a fluorescent test strip platform was developed for the visual on-site detection of PO₄^3- in milk, with spiked recovery rates ranging from 97.64% to 105.96% and a relative standard deviation (RSD, n = 6) below 3.28%. Additionally, a hierarchical clustering algorithm was introduced to automatically classify the R/B signals from the test strips, providing an objective and visual means of categorizing the detection data. This work provides a rapid, portable, and simple platform for the detection of PO₄^3- in food samples and holds promising prospects for application in the field of food safety regulation.

The widespread use of lithium (Li) has resulted in lithium-ion (Li⁺) contamination across a wide range of concentrations in the environment, posing potential risks to environmental safety and ecological health. However, the accurate and reliable quantification of Li⁺ over such a broad concentration range remains challenging. Herein, a lithium ion sieve (H₂TiO₃)-engineered electrochemical sensing platform was developed by integrating selective ion recognition with artificial neural network (ANN)-assisted signal modeling. Lithium titanate (Li₂TiO₃) was prepared via a sol-gel route and subsequently converted to H₂TiO₃ through proton exchange in an acidic medium. The resulting H₂TiO₃ was then immobilized onto a pencil graphite electrode to obtain a working electrode for Li⁺ detection. Electrochemical characterization revealed that the working electrode markedly enhanced the redox peak currents of potassium ferrocyanide and lowered the charge-transfer resistance after incubation in a lithium-ion solution. To overcome the difficulty of pinpointing signals when the entire cyclic voltammetry curve varies rather than individual peak intensities, an ANN module was applied to correlate the cyclic voltammetry response with the Li⁺ concentration. The electrode exhibited a concentration-dependent response toward Li⁺ in an ultra-wide dynamic range of 1.0 × 10^-11 to 1.0 × 10^-4 g L^-1. The sensor exhibited excellent stability and reproducibility, with relative standard deviations remaining below 8%, and delivered recoveries between 97.9% and 103.9% when applied to water samples. The integration of ion-sieve recognition with machine learning modeling enabled Li⁺ quantification across an unprecedentedly wide concentration window.

Rapid and reliable detection of toxic heavy metal ions in water remains a critical challenge for environmental monitoring and public health protection, highlighting the growing need to develop environmentally friendly, scalable, and sustainable sensing materials that minimize ecological impact and support green analytical technologies. Here, we report a plasma-assisted approach for converting low-cost folic acid, serving as a single carbon and nitrogen source, into carbon quantum dots (CQDs) with intrinsic dual-mode optical sensing capabilities. The resulting CQDs exhibit strong blue fluorescence emission, well-defined absorption feature, and a large Stokes shift (110 nm), enabling highly sensitive and selective detection of copper (Cu²⁺) and lead (Pb²⁺) ions through both fluorescence quenching and absorbance modulation. Excellent linearity (R² > 0.99) was obtained for both Cu²⁺ and Pb²⁺ using fluorescence (0-10 ppm) and absorbance (0-20 ppm) responses. The proposed sensor achieved detection limits of 54.4 ppb and 47.4 ppb for Cu²⁺ and 100.0 ppb and 61.6 ppb for Pb²⁺ by fluorescence and absorbance detection, respectively, while exhibiting excellent selectivity against competing ions. Key features of the platform include its applicability to binary-mixture analysis and effective correction of matrix effects across diverse water matrices. Binary-mixture analysis of coexisting Cu²⁺ and Pb²⁺ was independently demonstrated using both fluorescence and absorbance measurements. Separately, matrix effects were systematically accounted for in individual-ion measurements, enabling reliable quantification of Cu²⁺ and Pb²⁺ in tap water, river water, and seawater, with recoveries of 94.07-114.74% and 94.05-116.11%, respectively, using both analytical modes. The results were further validated against the inductively coupled plasma (ICP) method, showing good agreement with the expected spiked values and demonstrating robust performance in complex real-world matrices. This work establishes a simple plasma-engineered CQD platform that combines green precursor conversion, dual-signal optical readout, binary Cu²⁺/Pb²⁺ analysis, and matrix-effect correction within a single sensing framework. By integrating these capabilities with low-cost and less hazardous materials, the platform offers a promising and potentially scalable approach to heavy-metal monitoring, with satisfactory analytical performance demonstrated in diverse real-water matrices.