Analytical Methods最新文献

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.

Datura stramonium L. (DS) poisoning remains a significant toxicological concern in clinical and forensic contexts. In forensic casework, leaf ingestion is mainly associated with accidental exposure, whereas seed ingestion is more frequently linked to suspected intentional poisoning. However, conventional toxicological analysis based on tropane alkaloids cannot distinguish between these exposure sources, limiting forensic interpretation. This study developed a serum metabolomics strategy integrating machine learning to viably discriminate between seed- and leaf-derived Datura exposure using metabolic signatures. Serum samples from DSS (seed-derived) and DSL (leaf-derived) exposure models were analyzed using UPLC-Orbitrap-MS-based untargeted metabolomics. Multivariate analysis revealed distinct metabolic profiles between DSS and DSL groups, involving alterations in alkaloids, phenylpropanoids, lipids, and organic acids. Later, a supervised machine learning framework was developed to classify exposure sources based on metabolic signatures. The optimized model achieved an area under the curve (AUC) of 0.90. Feature interpretation further identified key metabolites contributing to classification, enabling biological interpretation of forensic decision boundaries. Meanwhile, higher metabolites in machine learning can also reflect different pathological mechanisms, providing a mechanistic basis for exposure differentiation. These results demonstrate that the integration of serum metabolomics and machine learning enables accurate and interpretable forensic source attribution of Datura poisoning. The proposed framework extends traditional toxicological analysis from exposure confirmation to data-driven forensic reconstruction, offering a novel approach to high-resolution forensic toxicology in complex plant poisoning cases.

Secondary electrospray ionisation high-resolution mass spectrometry (SESI-HRMS) is a powerful, non-invasive tool for real-time breath analysis. However, instrumental drift, capillary degradation and other technical variations compromise clinical applications, especially for long-term studies, which require robust quality control (QC) methodologies. In this work, we present the use of an evaporation chamber-based reference gas system to monitor SESI-MS system performance over almost two years. Targeted QC measurements with deuterated butyric acid were recorded three times on each measurement day. Analytical stability was systematically quantified across both positive and negative ion modes. In a standalone continuous characterization, the reference gas generator delivered a stable signal over several hours. A Linear Mixed-Effects Model (LMM) was constructed to evaluate the influence of time-of-day on instrument performance. This revealed significant effects: positive mode signal intensity progressively increased throughout the day, whereas for negative mode, an initial drop in signal intensity and subsequent stabilisation was observed. Furthermore, the longitudinal QC framework successfully tracked capillary performance over time, providing objective performance metrics to retrospectively evaluate the impact of targeted hardware maintenance on system performance. The implementation of a structured QC protocol provides a valid methodology for long-term SESI-HRMS performance monitoring, ensuring data integrity and supporting the translation of SESI-HRMS into large-scale clinical applications.

We introduce a dual-plane, independently addressable electrode architecture integrated with nanowell arrays for frequency-tuned dielectrophoretic control of 200 nm particles. Unlike conventional single-plane systems, vertical field decoupling enables array-wide p-DEP capture, n-DEP refinement and stable retention, and controlled ejection without auxiliary flow-based or mechanical confinement, establishing reversible on-chip particle manipulation.

Synthesis of silver (Ag) nanoparticles was done using chemical reduction of AgNO₃ in ice cold NaBH₄, and functionalization of the surface was done using cysteamine via Ag-S thiolate bonding at pH 9.0. Quaternization of the surface was achieved via exhaustive N-methylation of the terminal amine group at the surface using iodomethane (CH₃I). This converts the surface -NH₂ groups stepwise into permanently positively charged quaternary ammonium moieties (-N⁺(CH₃)₃). The resulting surface-modified nanoparticles (AgNPs-QCy) were comprehensively characterized using UV-visible spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, Transmission Electron Microscopy (TEM), Scanning Electron Microscopy coupled with Energy-Dispersive X-ray spectroscopy (SEM-EDX), Dynamic Light Scattering (DLS), and zeta potential measurements. Surface quaternization was confirmed by the red shift in the Localized Surface Plasmon Resonance (LSPR) band from ∼385 nm to 393 nm, attributed to changes in the local dielectric environment at the nanoparticle surface following quaternization, and accompanied by a decrease in the hydrodynamic diameter from 314.2 nm to 149.9 nm, consistent with improved colloidal uniformity upon surface charge stabilization. Zeta potential measurements confirmed the pH-independent positive surface charge, thus confirming the stability of the permanent quaternary ammonium group over a broad range of pH. AgNPs-QCy exhibited high selectivity toward Hg²⁺ ions in the presence of several competing metal ions, including Fe²⁺, Zn²⁺, Cd²⁺, Mn²⁺, Pb²⁺, Al³⁺, Co²⁺, Ni²⁺, Cu²⁺, As⁵⁺, Cr³⁺, and La³⁺. The selective response toward Hg²⁺ is attributed to its strong interaction with the functionalized AgNP surface, leading to pronounced changes in nanoparticle interfacial interactions and plasmonic properties. These changes result in aggregation and suppression of the LSPR signal, producing a distinct colorimetric response that can be visually observed. AgNPs-QCy-based sensors exhibit a limit of detection (LOD) of 50.38 nM and limit of quantification (LOQ) of 167.9 nM. The sensing platform was successfully validated in real water matrices including lake, tap, and underground water collected from the Riyadh region (Kingdom of Saudi Arabia), confirming its practical applicability for environmental Hg²⁺ monitoring, and supporting its potential as a rapid, low-cost, single-use screening tool for Hg²⁺ detection.

In this study, magnetic Fe₃O₄@ZnCr-LDH/ZIF-8 composites were fabricated by co-precipitation and in situ growth, combining the merits of LDHs and ZIF-8 to address Fe₃O₄ particle aggregation and limited enzyme loading capacity. Thrombin was efficiently immobilized within this material for rapid thrombin inhibitors screening. The composites were characterized by TEM, FTIR and other techniques, verifying successful synthesis with good paramagnetic property and hierarchical pores. Compared to free enzymes, the immobilized enzyme had better temperature and pH tolerance, retaining good activity after six cycles. Two potential inhibitors, cryptotanshinone and tanshinone I, were isolated from Salvia miltiorrhiza via combining immobilized enzyme with LC-MS. Molecular docking validated that both compounds bound to thrombin through hydrogen bonds and hydrophobic interactions, and cryptotanshinone exhibited a more favorable binding energy (-7.41 kcal mol^-1) than tanshinone I (-6.52 kcal mol^-1). This strategy provides a new pathway to rapidly screen bioactive natural compounds using functional magnetic nanomaterials.