Analytical Methods最新文献

2D metal nanosheets have recently attracted significant attention in material science due to their unique physicochemical properties and broad potential applications. However, the synthesis of 2D metal nanosheets is very challenging and tricky owing to the high probability of many metals to form their oxidized forms and the very high reactivity of metal nanosheets. This study introduced a novel, simple, and reproducible approach to synthesize metal nanosheets via probe ultrasonication. The probability of oxidation is controlled to achieve successful synthesis. The synthesis approach was quite successful for Ni, Ru, and Pd, resulting in metal nanosheets with distinct characteristics. The as-synthesized Ru nanosheets displayed highly crystalline single-element lattice structures, while the 2D Ni nanosheets exhibited polycrystallinity with strong bluish-green fluorescence. Overall, this work presents a promising and environmentally friendly strategy for generating non-conventional 2D metal nanosheets, unlocking their potential in advanced material applications as effective fluorescent sensors or biosensors that can be widely applied in diverse fields in the near future.

This critical review aims to (1) summarize methodologies for sampling and characterizing atmospheric microplastics (MPs), (2) evaluate their environmental prevalence and associated health hazards, (3) highlight deficiencies in toxicological research, and (4) provide strategies for mitigation. Atmospheric MPs (smaller than 5 mm) represent an emerging air pollution threat with significant health implications. This review consolidates findings from 156 peer-reviewed papers published between 2000 and 2025, reporting airborne MP concentrations ranging from 370 particles m^-2 day^-1 in the Pyrenees to 600 particles m^-2 day^-1 in urban areas of China. Synthetic fabrics account for 29-66% of atmospheric fibres, while tyre wear is the main source of larger fragments. MPs can remain airborne for 1 to 6.5 days, allowing for transcontinental distribution to remote regions such as the Arctic and Pyrenees via wind patterns. Vegetation acts as a temporary reservoir for MPs before they are ultimately deposited in soil and water, contributing to up to 80% of oceanic MPs in certain areas. Annual human inhalation exposure is estimated to exceed 1 million particles, and occupational studies have linked polyvinyl chloride (PVC)/polypropylene (PP) dust to interstitial lung disease and a 3.6-fold increase in respiratory symptoms. The research is crucial for addressing current gaps in toxicological knowledge and for developing effective mitigation strategies. Highlighting the gap in studying inhaled airborne MPs can inspire researchers to focus on this vital area, making them feel that their work is impactful and needed. There is a significant gap in the study of the toxicity of inhaled airborne MPs, with implications for toxicological investigations. Significant deficiencies remain in understanding the toxicological effects of airborne MP inhalation, ocular surface impacts, and atmospheric transition chemistry. Mitigation necessitates the control of sources (textile filters and tyre recycling) in conjunction with sophisticated detection methods (FTIR, Raman, Py-GC/MS). Urgent incorporation of policy measures into air quality standards is necessary to tackle this widespread atmospheric pollutant issue.

This study presents an enzyme immobilization strategy utilizing zeolitic imidazolate framework-8 (ZIF-8), a metal-organic framework (MOF) material synthesized in-house, for α-amylase immobilization, establishing a novel solid-phase extraction platform for targeted isolation of enzymatic inhibitors from Gynura medica aqueous extracts. The ZIF-8@α-amylase nanoparticles were prepared using the in situ encapsulation method and exhibited 91% enzyme encapsulation efficiency and enhanced aqueous stability. ZIF-8@α-amylase exhibited a broader operational range, retaining more than 50% activity across 20-70 °C and pH 4.0-9.0 (compared to 20-65 °C and pH 5.5-7.5 for the free enzyme). This immobilized system also demonstrated a high binding capacity of 189.5 mg g^-1 for acarbose. Using a mild alkaline buffer for elution, a concentrated fraction showing 18.1% α-amylase inhibition was obtained, which mainly contained phenolic acids, flavonoids, and hydrolyzable tannins as identified by LC-MS/MS. Dose-response analyses demonstrated significant pancreatic amylase inhibition by identified compounds: ellagic acid-4-O-glucoside: 98.86 ± 2.3% (1.0 g L^-1), quercetin-3-O-rhamnoside: 66.32 ± 1.9% (1.0 g L^-1), and methyl gallate: 33.56 ± 1.2% (1.0 g L^-1). Overall, ZIF-8@α-amylase is a promising solid-phase extraction platform for selectively enriching bioactive amylase inhibitors from complex botanical matrices, with potential in pharmaceutical discovery and diabetes management.

Circulating tumor cells (CTCs) are critical biomarkers of breast cancer metastasis, yet their low abundance and phenotypic heterogeneity present formidable challenges for reliable detection. Here, we report the design and validation of an electrochemical biosensor based on three-dimensional gold nanodendrites (AuNDs) functionalized with dual aptamers targeting the epithelial cell adhesion molecule (EpCAM) and Mucin 1 (MUC1). The AuND platform, synthesized via potentiostatic electrodeposition, exhibited a mesoporous dendritic morphology with a specific surface area of 172.2 m² g^-1 and crystalline Au(111) facets, confirmed by SEM, TEM, XRD, BET, and XPS analyses. Stepwise assembly of the aptamer-modified electrode produced distinct electrochemical signatures, with charge transfer resistance (R_ct) increasing from ∼50 Ω for bare AuNDs to ∼2000 Ω after BSA blocking. Optimization identified 1.0 µM aptamer concentration and 60 min incubation as optimal sensing conditions. Using MCF-7 breast cancer cells as CTC-like model cells, the sensor achieved an exceptionally low detection limit of 3 cells per mL and displayed a wide dynamic range from 10 to 10⁶ cells per mL, with a linear regression equation of ΔI (µA) = 5.8 log[C_cell] + 2.5 (R² = 0.998). Selectivity tests showed negligible responses (<5%) toward HeLa and Jurkat cells, while reproducibility across five electrodes yielded a relative standard deviation of 3.5%. Stability evaluation demonstrated retention of 92.5% activity after 28 days of storage at 4 °C. Recovery studies in 10% human serum spiked with MCF-7 cells (as a model CTC system) showed excellent accuracy (96.5-104.2%) with RSD ≤ 4.5%. Collectively, these results demonstrate strong analytical performance for MCF-7 model CTCs in buffered suspensions and in a spiked (10%) serum matrix, supporting the AuND/dual-aptamer platform as a proof-of-concept CTC biosensing strategy; validation using patient-derived CTCs in whole-blood samples will be required in future studies to establish clinical utility.

The hybridisation chain reaction (HCR) is a robust isothermal amplification technique widely used for nucleic acid detection, often paired with silver nanoclusters (AgNCs) for fluorescence-based readouts. However, conventional HCR-AgNC assays are prone to circuit leakage and unpredictable AgNC formation, which can compromise assay reliability. In this study, we present a modular HCR-AgNC hybrid sensor employing a cluster transfer strategy, in which pre-formed AgNCs are introduced separately into the HCR system. This design minimises interference between AgNCs and HCR components, enhancing signal specificity and predictability. Using a DNA analogue of miRNA-141 (DNA-141), a potential prostate cancer biomarker, we demonstrate selective red fluorescence activation upon target recognition. The sensor design was guided by NUPACK simulations, reducing trial-and-error and lowering assay costs. Gel electrophoresis and fluorescence spectroscopy confirmed the system's specificity and sensitivity, achieving a limit of detection of 46 nM. This work establishes a foundational framework for modular and adaptable nucleic acid biosensing, with potential for future sensitivity enhancements and multiplexed detection.

Forensic analysis of dyes present on fabric can provide a wealth of information about the relationship between the suspect and a crime scene. Such analysis requires analytical approaches that provide confirmatory information about the chemical structure of the dyes. A growing body of evidence indicates that near-Infrared excitation Raman spectroscopy (NIeRS) can be used for non-invasive and non-destructive detection and identification of colored fabric. However, it remains unclear how environmental factors such as water exposure could alter the accuracy of dye identification by NIeRS. To answer this question, we exposed cotton fabric colored by blue and magenta dyes to ocean and lake waters for 14 weeks. During this time, fabric was analyzed biweekly using a hand-held NIeRS spectrometer. Chemometrics was used to process and learn from the acquired NIeRS spectra. Our results indicate that both ocean and lake water cause substantial fading of dyes. NIeRS revealed that fading rates are greater for lake than ocean water. Such water-triggered fading substantially lowered accuracy of NIeRS-based dye identification. We observed a dye-dependent decrease in prediction accuracy from 100% at week 0 to 30-80% by week 6. Nevertheless, built spectroscopic libraries enabled ∼98% accurate identification of both dyes on fabric. These results indicate that water exposure possesses a great risk to NIeRS-based identification of dyes on fabric. However, a combination of NIeRS and artificial intelligence allowed for overcoming these limitations.

Aqueous biphasic systems are investigated here, from standard liquid-phase to solid-phase extraction configurations for human serum pretreatment. Using prostate-specific antigen as a model biomarker, this strategy achieves >90% depletion of high-abundance proteins and demonstrates strong potential for bioanalytical applications compared to conventional precipitation methods.

A simple colorimetric immunosensor using a paper-based analytical device (PAD) and a sandwich immunoassay was developed for the detection of high-sensitivity C-reactive protein (hs-CRP), a crucial risk predictor for cardiovascular diseases (CVDs). The capture antibody was immobilized onto magnetic beads to facilitate immunomagnetic separation (IMS) of CRP from the sample matrix, while the detection antibody was labelled with biotin. This biotinylated antibody subsequently linked to streptavidin conjugated with β-galactosidase (βgal), allowing a colorimetric assay due to the enzymatic reaction between βgal and chorophenol red-β-D-galactopyranoside (CRPG). The use of the highly sensitive CPRG substrate significantly enhanced the performance of the colorimetric hs-CRP detection with an LOD of 0.10 mg L^-1 and a detection range of 0.30-3.0 mg L^-1, which is clinically sufficient for the assessment of CVD risk. Furthermore, both intraday and interday measurements confirmed satisfied precisions with % RSD of less than 5%. Finally, the developed method was applied to determine CRP in a certified human serum reference material. The obtained result was found to be insignificantly different from the true value, confirming the reliability of the sensor. This simple, portable and instrument-free method, therefore, holds great promise for development as a screening test kit for CVD risk assessment in resource-limited settings.

Fenamiphos (FEN), an organophosphorus insecticide, has been frequently used in recent years to control many nematode pests. Nonetheless, its serious adverse effects have currently raised concerns about human health and the ecological environmental safety. In the present study, a novel flow injection-type portable quartz crystal microbalance (QCM) sensor based on a boron and sulphur co-doped ultra-thin graphitic carbon nitride (g-C₃N₄)-incorporated Cu-MOF (BS-g-C₃N₄-CuMOF) nanocomposite was developed and used for FEN determination in apple juice samples. For this, the BS-g-C₃N₄-CuMOF nanocomposite was prepared via an in situ solvothermal procedure with high synthesis yields. Then, a molecularly imprinted QCM sensor based on the BS-g-C₃N₄-CuMOF nanocomposite was prepared using methacryloylamidoglutamic acid (MAGA) as a monomer and N,N'-azobisisobutyronitrile (AIBN) as an initiator via UV polymerization. This QCM sensor revealed a linearity of 1.0 × 10^-9-2.0 × 10^-8 mol L^-1 with a limit of detection (LOD) of 3.3 × 10^-10 mol L^-1 for the FEN molecule, suggesting the successful development of the sensitive molecularly imprinted QCM sensor. The prepared sensitive molecularly imprinted QCM sensor was applied to an apple juice sample with high recovery. Moreover, the high selectivity of the molecularly imprinted QCM sensor was confirmed in the presence of competing agents in the apple juice sample, and the prepared QCM sensor was shown to detect FEN at least 5 times more selectively than the other competing agents. In addition, the proposed sensor was validated in correlation with standard high-end measurements like GC-MS, and there was no significant difference between the results of the proposed sensor and GC-MS method. The low relative standard deviation (RSD) value of 20 independent QCM signals also suggested the high reproducibility of the QCM sensor. Finally, its high repeatability and reusability are presented herein in detail.

To address the lengthy analytical cycles and high solvent consumption of conventional HPLC, this study focused on Phyllanthi Fructus (PF) and, by optimizing gradient elution conditions, established a time contracted chromatographic fingerprint. Combined with a one-dimensional convolutional neural network (1D-CNN), this approach enabled quantitative analysis of multiple components. Compared to conventional methods, the analysis time per sample was reduced by approximately 60 minutes and mobile phase consumption decreased by nearly 79%. The model results demonstrate high concordance between predicted and measured values for most target compounds, with five exhibiting correlation coefficients exceeding 0.91. The Wilcoxon signed-rank test and rank-biserial correlation analysis further validate this reliability. Overall, this method significantly enhances detection efficiency and reduces environmental impact while maintaining accuracy, offering a green and efficient new approach for quality control of natural products with considerable potential for widespread application.