Analytical Methods最新文献

Lead contamination in seafood poses a significant risk to human health, necessitating reliable and environmentally sustainable analytical methods for routine Pb(II) monitoring. In this study, a sustainable solid-phase extraction (SPE) method was developed for the preconcentration and determination of Pb(II) using a bio-derived chelating resin prepared by immobilizing anthocyanins extracted from Clitoria ternatea (butterfly pea) flowers onto Amberlite XAD-1600N. Pb(II) retained on the functionalized resin was eluted and determined by flame atomic absorption spectrometry (FAAS). Key operational parameters, including solution pH, loading time and volume, eluent concentration and volume, and sorbent mass, were systematically optimized. Maximum Pb(II) retention was achieved at pH 6.0, and efficient elution was obtained using 5 mL of 1.50 mol L^-1 HNO₃. The method provided a detection limit of 0.069 mg L^-1 and acceptable overall precision, with a relative standard deviation of 6.6% (n = 20). Owing to the use of plant-derived ligands, low reagent consumption, and the absence of hazardous organic solvents, the method aligns well with the principles of green analytical chemistry. The validated procedure was successfully applied to digested shrimp and squid samples, demonstrating its practicality and suitability for routine, eco-friendly monitoring of Pb(II) contamination in seafood.

Extracellular vesicles (EVs) have been a key focus in biomarker discovery, with urinary EVs (uEVs), primarily derived from cells of the urogenital tract, providing valuable insights into kidney and urinary tract health and disease. However, progress in uEV-based metabolomics remains limited by variability in EV isolation and extraction approaches. Here, we systematically evaluated and optimised experimental conditions for untargeted metabolite profiling of human uEVs. We compared three different EV isolation methods, namely precipitation, size-exclusion chromatography, and pH-adjustment with resin separation, and found that precipitation yielded the highest particle count. However, the pH-adjustment with resin separation method produced the highest number of small EVs (30-150 nm), aligning with the primary focus of EV research. Transmission electron microscopy analysis confirmed the presence of well-structured exosomes in these isolates. Moreover, this EV isolation method generated the broadest metabolite coverage. To identify the most effective metabolite extraction conditions, we compared two established protocols (P. Liu, W. Wang, F. Wang, J. Fan, J. Guo and T. Wu, et al., J. Transl. Med., 2023, 21(1), 40 and C. P. Hinzman, M. Jayatilake, S. Bansal, B. L. Fish, Y. Li and Y. Zhang, et al., J. Transl. Med., 2022, 20(1), 199) with an in-house-developed method. Application of the protocol of Liu et al. led to the identification of the highest number of metabolites. Considering EV purity, contamination risks and metabolite yield, the combination of the pH-adjustment with resin separation method for uEV isolation with the metabolite extraction protocol of Liu et al. was the optimal approach for metabolomics analysis of the uEV cargo. This study provides an experimentally validated workflow for robust untargeted metabolomics analysis of human uEVs and supports the development of more standardised approaches for EV-based biomarker discovery.

The use of mussels as bioindicators for monitoring marine microplastic pollution has broad application prospects. However, the lack of standardized protocols for extracting microplastics from mussel tissues hinders the comparability of results across studies. Capitalizing on the efficient organic matter degradation capacity of Fenton's reagent, this study investigated its applicability for extracting microplastics from mussel tissues. Experimental results demonstrated that Fenton's reagent efficiently digests mussel tissues, achieving a digestion efficiency exceeding 88% even with a tissue mass of 12 g. Moreover, Fenton's reagent did not alter the morphology of six common marine microplastic types nor interfere with their identification. The method was further applied to wild mussel samples from the coast of Zhejiang to assess microplastic pollution. The results revealed microplastic abundances ranging from 1.8 to 9.0 items per g (wet weight), with 63% to 100% of the microplastics smaller than 50 µm. The predominant microplastics in mussels were chlorinated polyethylene (40.7%), polyvinyl chloride (32.2%), polyethylene terephthalate (6.8%), polyethylene (5.1%), and polyurethane (4.2%). The composition of microplastics in mussels is highly correlated with the characteristics of microplastics in the surrounding environment, confirming that mussels can be effectively used to monitor marine microplastic pollution. This study provides a valuable assessment of Fenton's reagent application for monitoring marine microplastic pollution and offers a reliable technical foundation for standardizing methods using bivalves as indicator organisms.

A selective and efficient method was developed for the extraction and trace determination of titanium(IV) using integrated supercritical fluid extraction (SFE) with calixarene hydroxamic acid (CC4AHA), coupled online with inductively coupled plasma mass spectrometry (ICP-MS). The extraction conditions were systematically optimized with respect to temperature, pressure, extraction time, acid molarity, and organic modifier to achieve quantitative recovery of Ti(IV). Optimal extraction was obtained at 50 °C and 17 MPa using dichloromethane-modified supercritical CO₂ in the presence of 8.0 M HCl with an extraction time of 5-6 min, yielding extraction efficiencies greater than 99.9%. Under the optimized conditions, the method showed excellent linearity over the concentration range of 0.23-2.18 ng mL^-1 with a correlation coefficient (R²) exceeding 0.999. The limits of detection and quantification were 0.78 ng mL^-1 and 2.34 ng mL^-1, respectively. Method precision, evaluated by repeatability studies, resulted in relative standard deviation values below 5%, while accuracy assessed through recovery experiments ranged from 96% to 104%. The proposed method was successfully applied to the determination of titanium in certified NBS geological reference materials and real samples, including steel, brass, and ilmenite, with results in good agreement with certified values. The developed SFE-ICP-MS approach offers a rapid, sensitive, and environmentally benign alternative for the ultra-trace determination of titanium in complex matrices.

In recent years, honey products have faced increasing issues of adulteration, posing significant challenges to their authenticity and quality. LED-induced fluorescence (LED-IF) has the characteristics of being non-destructive, rapid and efficient, offering significant advantages in detecting honey adulteration. A hybrid architecture integrating a transformer module and CNN (TransCNN) is proposed in this paper for processing spectroscopic fluorescence data. For honey adulteration detection tasks, a lightweight transformer module is introduced before the fully connected layer of a convolutional neural network, leveraging multi-head self-attention to enhance global feature modeling capabilities in spectroscopic fluorescence data. The TransCNN model demonstrated the best performance, with an average accuracy of 98.75% and a root mean square error (RMSE) of 3.91% to 4.33%, outperforming the traditional CNN (93.25% and 5.54% to 6.24%) and SVM (88.5% and 8.07% to 9.88%). This study demonstrates that the proposed TransCNN framework provides an effective analytical strategy for modeling long-range spectral dependencies in fluorescence-based detection.

Hepatitis B virus (HBV) infection screening includes hepatitis B surface antigen (HBsAg) and HBV DNA detection. HBsAg detection can only screen for overt HBV infection; HBV DNA can screen for occult HBV infection (OBI), but the method is complex, expensive, and often excluded from routine examinations, risking missed OBI diagnoses. Using plasma near-infrared (NIR) spectral pattern recognition, two-classification discriminant models for HBsAg⁺-HBsAg^- and OBI-healthy, and a three-classification discriminant model for HBsAg⁺-OBI-healthy were established. A total of 657 plasma samples (HBsAg⁺ 213, OBI 204, and healthy 230) were collected; the NIR spectra were measured and were divided into training, prediction, and external validation sets. Partial least squares-discriminant analysis (PLS-DA) and k-nearest neighbor (kNN) were used as classifiers; Norris derivative filtering (NDF) was used for spectral preprocessing; the integrated algorithm of equidistant combination (EC) and wavelength step-by-step phase-out (WSP) was used for wavelength optimization. For above two binary classifications, the numbers of wavelengths (N) of the optimal EC-WSP-PLS-DA model with NDF were 26 and 36, respectively; in the independent external validation, the sensitivity and specificity reached 100%. For the above three-classification discriminant, the N of the optimal EC-WSP-kNN model with NDF was 24; in the independent external validation, the total recognition-accuracy rate reached 98.1%. The results showed that plasma near-infrared spectral pattern recognition can accurately perform two-classification and three-classification discrimination of HBsAg⁺, OBI, and healthy individuals. This method is reagent-free, rapid, and simple, which can simultaneously detect overt and occult HBV infections. The proposed few-wavelength model can be used for the development of small-scale dedicated spectrometers.

Zearalenone (ZEA), an environmental mycotoxin produced by Fusarium fungi, is widely present in grains and grain products. Due to its potent carcinogenicity and toxicity, it poses a serious threat to human and animal health, necessitating the development of simple, sensitive, and reliable ZEA detection methods. This study constructed a novel electrochemiluminescence (ECL) probe based on Cu₂O@Au. This probe synergistically interacts with luminol, utilizing enzyme-catalyzed glucose decomposition to generate H₂O₂, which is further catalyzed to hydroxyl radicals (OH˙), thereby significantly amplifying the ECL signal. To enhance catalytic efficiency, a Tb-Cu metal-organic framework (Tb-Cu MOF) with horseradish peroxidase-mimetic activity was integrated onto the sensor interface to accelerate hydrogen peroxide decomposition. This integrated sensing platform achieves highly sensitive detection of ZEA, with a detection limit as low as 0.017 pg mL^-1 and a linear range spanning 0.10 pg mL^-1 to 100 ng mL^-1. This strategy provides a powerful analytical tool for detecting trace toxins in complex biological matrices, demonstrating broad application prospects in fields such as food safety and environmental analysis.

The spectral reflectance characteristics of drone materials are one of the key factors enabling accurate spectral detection of drones. In this study, an experimental setup was independently constructed to measure the diffuse reflectance of drone fuselage materials. The reflectance spectra and their derivative spectral features of 15 different materials, including glass fiber, polypropylene, and polytetrafluoroethylene in various colors, were systematically analyzed. Based on this, machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) were applied to classify the aforementioned materials. The results show that the KNN algorithm demonstrated the best classification performance. Under the condition of a total sample size of 210 (training set: 147, test set: 63), the training set achieved an accuracy of 0.9731 through five-fold cross-validation, while the test set achieved a perfect accuracy of 1, indicating excellent model stability and classification precision. This research provides an important material study foundation for the spectral recognition and tracking of drone targets.

This study presents an improved ethanol-mediated solvothermal method for synthesizing malic acid/urea-derived carbon dots (MUCDs) for the highly selective and sensitive detection of mercury ions (Hg²⁺). The detection limit was enhanced by over an order of magnitude compared to previous methods employing similar precursors, achieving a limit of detection of 88.46 nmol L^-1, which meets the European Union standard for mercury discharge in industrial wastewater (250 nmol L^-1). Pyrophosphate (P₂O₇^4-) has been used as a highly effective masking agent for Fe³⁺, substantially boosting the probe's performance in complex matrices. With practical application in mind, the environmental safety of these carbon dots was proactively evaluated using a zebrafish model. The assessment demonstrated no significant toxicity at concentrations up to 100 mg L^-1, establishing a 10-fold safety margin relative to the operational dosage. Fluorescence imaging revealed accumulation specifically in the intestinal tract, with no distribution to vital organs, confirming their environmental safety. Under optimized conditions, the MUCDs achieved a quenching efficiency of 0.82 in the presence of 25 µmol per L Hg²⁺ at a concentration of 10 mg L^-1, while also demonstrating thermal stability and pH tolerance. Spike-recovery tests in tap water, river water, and metallurgical wastewater samples yielded satisfactory recoveries ranging from 93.85% to 101.86%. Based on the characterization of MUCDs and the MUCDs-Hg²⁺ complex, the observed quenching mechanism was explained by chelation between Hg²⁺ and surface functional groups, leading to aggregation, static quenching, and enhanced Rayleigh scattering. This study highlights the critical importance of incorporating comprehensive environmental safety assessments into the development of functional nanomaterials.

This study reports the development and characterization of a novel optical fiber sensor designed for the sensitive detection of dissolved oxygen. The sensing architecture was fabricated on the distal end of an optical fiber using a dip-coating technique. The core sensing layer consists of the oxygen-responsive luminophore, ruthenium(II)-tris(4,7-diphenyl-1,10-phenanthroline) dichloride (Ru(dpp)₃Cl₂), physically immobilized within a fluorinated xerogel matrix derived from the co-condensation of 3,3,3-trifluoropropyltrimethoxysilane (TFP-TMOS) and propyltriethoxysilane (PTEOS). To enhance durability and prevent indicator leakage, a secondary layer of cellulose acetate was applied as a protective barrier over the sensing film. Comprehensive structural and morphological analyses of the resulting composite film were conducted utilizing Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), while optical properties were evaluated via fluorescence spectrophotometry. The sensor operates on the principle of fluorescence quenching, demonstrating a robust response to oxygen in aqueous environments. Analytical performance testing revealed excellent linearity in the Stern-Volmer plot (I₀/I) across a broad dissolved oxygen concentration range of 0 to 30.7 mg L^-1. The device achieved a low detection limit of 0.05 mg L^-1 and a rapid response time of 5 seconds. Furthermore, the prepared sensor exhibited superior stability and resistance to dye leaching, confirming its significant potential for reliable, long-term monitoring of dissolved oxygen in diverse aquatic applications.