Talanta最新文献

Covalent functionalization is always known for superior colloidal stability, improved selectivity, fluorescence stability, and prevents aggregation of molecules, which reflects higher sensitivity and is preferred over non-covalent interactions. Herein, we have demonstrated one-step covalent functionalization of high-purity carbon nanodots (CNDs) with ligands such as glutathione (GSH) and cysteine (CYS) for the enhanced specificity towards arsenic detection. An improved detection limit of 0.26 ppt and 1.7 ppb for As (III) with GSH and CYS functionalized CNDs was observed while comparing with previous non-covalent thiol-based probes with a linear and non-linear Stern-Volmer plot meant for both static and dynamic quenching processes. The arsenic fluorescent probe was thoroughly characterized with spectroscopic and morphological studies. Further, an attempt was also made to develop a paper analytical device (PAD) for instrument free arsenic detection in water, and the real-world application is demonstrated by analyzing the tap water and groundwater samples from arsenic arsenic-affected area.

Prostate cancer (PCa) exhibits significant intra-tumor metabolic heterogeneity. In this study, we developed a multi-platform workflow combining pathological staining, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and laser capture microdissection-assistant gas chromatography-tandem mass spectrometry (LCM-GC-MS/MS) to investigate the metabolic spatial heterogeneity of a specific PCa tissue. Four suspicious glandular regions were initially identified by histology and immunofluorescence. MALDI-MSI revealed three distinct metabolic patterns for further analysis. It was found that only one glandular region showed PCa metabolic features, enabling precise differentiation from other regions of interest. The information from LCM-GC-MS/MS complemented that from MSI by confirming and quantifying fatty acid alterations. This workflow broadened the spatial metabolic coverage and enhanced intra-tumor metabolic heterogeneity analysis in PCa.

This study presents the development and validation of an automated pressurized liquid extraction (PLE) method using the EXTREVA ASE system for the determination of 233 pesticide residues in coffee, a matrix considered challenging due to its complex composition. The method was evaluated at three fortification levels (10, 20, and 50 μg/kg), with nine replicates per level distributed over three days to assess inter-day reproducibility. At 20 and 50 μg/kg, all compounds met the established performance criteria of 70-120 % recovery and relative standard deviations (RSDs) below 20 %. At the 10 μg/kg level, only 12 compounds (5.2 %) were outside these criteria, primarily due to known issues such as co-elution with matrix components or the acidic nature of certain analytes. All analytes exhibited excellent linearity (R² ≥ 0.99) across a five-point procedural calibration from 0.005 to 0.1 mg/L (0.005, 0.01, 0.02, 0.05, 0.1 mg/L), confirming the method's suitability for quantitative analysis. Matrix effect evaluation showed that 44 % of analytes experienced low or negligible effects, 42 % moderate effects, and only 14 % were strongly affected. Real sample analysis confirmed the method's applicability for routine monitoring, while participation in the EUPT-FV-SC07 proficiency test for coffee demonstrated high analytical performance, with z-scores ranging from -0.7 to 0.3 and RSDs below 20 % for all compounds. Overall, this work provides a validated, reproducible and automation-ready solution for multiresidue pesticide control in complex matrices such as coffee, meeting regulatory standards and suited for high-throughput laboratories.

The adulteration of pure edible oils, particularly with cost-effective oils like palm oil, has become a significant concern due to its detrimental impact on oil quality and human health. This study examines how palm oil adulteration affects the dielectric, physical, and chemical properties of groundnut and sunflower oils. Additionally, this study explores the nutritional differences between pure and adulterated oils, highlighting potential risks to consumers. Microwave analysis showed decreased dielectric constant and loss in groundnut and sunflower oils after palm oil blending. Chemical parameters and fatty acid composition confirmed adulteration effects and potential health risks. To ensure the accuracy and reliability of our findings, we applied several chemometric methods, including Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN) to detect adulteration in oils. Among these, ANN and MLR were compared for predicting the dielectric constant. The results showed that ANN performed much better than MLR, explaining R² value of 0.94 in groundnut oil and 0.96 in sunflower oil, proving it to be a more accurate method for assessing oil quality. The features that made the ANN model work better were found using SHAP analysis. The result of the SHAP value shows that the refractive index (RF) in groundnut oil and the saponification (SAP) value in sunflower oil are the most influential predictors of dielectric constant, as both parameters vary significantly with adulteration. This finding demonstrates a powerful and accurate approach for detecting adulteration and assessing the quality of edible oils.

A conductive hydrogel with double network structure was prepared by embedding Ag nanoparticles onto polyvinylpyrrolidone through in situ reduction, and subsequently crosslinking the mixture with polyvinyl alcohol and sodium lignosulfonate. This unique architecture imparted the hydrogel with good mechanical properties and fatigue resistance. Notably, the hydrogel exhibited remarkable antibacterial efficacy, achieving inhibition rates of 99.9 % against E. coli and 95.8 % against S. aureus. Furthermore, the conductive network, formed through the synergistic interaction of free ions and AgNPs, significantly enhanced both the conductivity and durability of the hydrogel sensor. The results demonstrated that the sensor maintained stable sensitivity even after 1000 cycles of stretching at 3 % and 30 % strain. In practical applications, this hydrogel sensor was successfully employed for real-time monitoring of various human body parts, including fingers, elbows, knees and facial expressions, underscoring its significant potential in the fields of flexible electronics and wearable sensing technologies.

A sample pretreatment method based on extraction induced by emulsion breaking (EIEB) was developed for the determination of selenium in milk powder by inductively coupled plasma mass spectrometry (ICP-MS). Validation with a certified reference material confirmed the method's excellent accuracy (recoveries: 82.90-97.80 %), good precision (relative standard deviation, RSD = 1.68 %, n = 6), and favorable robustness (RSD = 22.03 %, n = 6). The limit of detection (LOD) was 2.2 μg/kg. Compared to microwave-assisted acid digestion, the EIEB approach reduced acid consumption by 80 % (1 mL vs. 5 mL), simplified the workflow, and eliminated the need for specialized equipment, while maintaining comparable analytical performance. Additionally, Triton X-100 was found to enhance selenium signal intensity, acting as a sensitizer in ICP-MS. Analysis of commercial milk powder samples showed selenium concentrations consistent with nutritional reference values. Overall, the EIEB-ICP-MS method provides a robust and sustainable alternative for selenium quantification in dairy products. Aligned with the principles of green analytical chemistry, it also holds promise for application in the analysis of other hydrophilic food matrices.

Lactate, a crucial metabolic indicator, plays a vital role in diagnostics and treatment monitoring. Current lactate analysis often relies on enzymatic methods, employed in both clinical analyzers and point-of-care testing (POCT) devices. While these enzymatic assays offer selectivity and sensitivity, they suffer from limitations including higher costs, shorter lifetime, and reduced stability, particularly impacting POCT applications. This work introduces a novel POCT platform for lactate determination, utilizing a non-enzymatic approach. This analytical system is based on the photoreduction of iron(III)-lactate complexes, followed by detection of the resulting iron(II) with Ferrozine. Miniaturization of the system was achieved through two key innovations. First, the entire analytical procedure is conducted on 6 mm diameter reaction disks fabricated from cellulose and glass microfiber filters, facilitating easy storage and disposal while minimizing reagent and sample consumption (a few microliters). Second, an optoelectronic photometric system, incorporating an RGB LED emitter and a photodiode detector, enables both photoreduction (using blue light) and detection of the Fe(II)-Ferrozine complex (using green light). This POCT platform allows for rapid lactate determination in cerebrospinal fluid within 50 s, with analytical parameters supporting sample dilution up to even 100-fold.

Quantifying the fission product ¹⁴⁷Nd in nuclear debris samples is an important component of post-detonation nuclear forensics. The most accurate quantifications are obtained when Nd is purified from all other fission products, actinides, activation products, and environmental matrix contained within the debris. In this study, a recently developed method for Nd purification was tested, purifying ¹⁴⁷Nd from solutions of mixed fission products using high-speed counter-current chromatography (HSCCC). Importantly, the new method allowed for faster elution of Nd from the column as compared with established high performance liquid chromatography (HPLC) methods, and resulted in accurate/precise ¹⁴⁷Nd quantification by gamma-ray spectrometry. While the up-front equipment costs associated with HSCCC may be higher, its operational costs are on par with those of HPLC (solvents, extractants, power). Gas-flow proportional beta decay counting revealed contamination from the nearest neighbor lanthanide ¹⁴³Pr (a gamma-silent radioisotope) in the HSCCC-purified samples, but the activity contribution from ¹⁴⁷Nd could still be quantified. Remarkably consistent elution profiles were observed for the HSCCC method, spanning rare earth element (REE) loadings of more than 10 orders of magnitude (tracer to mmol quantities). The reliability and speed of the new method suggest utility for the rapid separation and quantification of ¹⁴⁷Nd in unknown samples.

MicroRNAs (miRNAs) are promising molecular markers for early-stage cancer, enabling advancements in early diagnosis, precision treatment, and prognosis evaluation. However, their detection remains challenging due to low abundance, driving the demand for highly sensitive and accurate sensing platforms. Herein, we developed a cascade amplification strategy integrating catalytic hairpin assembly (CHA) with a light-initiated chemiluminescent assay (LiCA) for ultrasensitive detection of serum miRNA-21. The platform employs H1-functionalized chemibeads (¹O₂-luminescent acceptors) and H2-conjugated sensibeads (photosensitizer donors), wherein target miRNA triggers ternary complex formation and initiates autonomous CHA cycling. This process facilitates femtomolar-sensitive, low-background detection without RNA extraction by utilizing proximity-driven (<200 nm) singlet oxygen transfer to generate a collective chemiluminescence signal. The assay exhibits high sensitivity (LOD: 0.21 pM) and excellent linearity (R² = 0.9958), coupled with strong matrix tolerance in 2 % human serum, as evidenced by accurate and consistent recoveries (98.8-112 %) with <8 % deviation at spiked concentrations from 0.1 to 5 pM. When applied to clinical serum samples (n = 78), the platform demonstrated perfect diagnostic discrimination (AUC = 1.00) with 100 % sensitivity and specificity at a cutoff of 0.667 pM across breast, gastric, and colorectal cancers. A strong correlation with qPCR (r = 0.78) was observed, and miRNA-21 levels were significantly elevated in cancer patients compared to healthy controls. This platform combines the programmability of CHA with the anti-interference advantages of LiCA, offering a robust tool for low-abundance miRNA analysis in complex matrices.

Bone defects, commonly associated with osteoporosis, result in fragile bones prone to fractures in both men and women. Osteoporotic fractures often lead to prolonged healing due to impaired cell differentiation. Understanding cellular crosstalk during bone regeneration is crucial for developing effective treatments. Periosteum-derived progenitor cells (PDPCs) and adipose-derived stem cells (ADSCs) play essential roles in bone formation because ADSCs secret key growth factors, such as bone morphogenetic protein-2, Wnt family member 1, Periostin, and platelet-derived growth factor subunit A, that promote osteogenesis. To investigate cellular interactions in bone healing process in the natural bone microenvironment, we developed a biomimetic folding paper co-culture system that mimics the inflamed three-dimensional bone structure. This system enables co-culture of PDPCs and ADSCs, allowing the study of their protein crosstalk and osteogenic potential. Moreover, neutralizing assays were conducted to inhibit specific cytokines and evaluate their influence on osteogenesis. Our findings confirm that ADSCs promote osteogenesis through their secreted growth factors that enhance the differentiation and activity of PDPCs. Disrupting these cellular interactions through cytokine inhibition led to a significant reduction in osteogenic potential. It was evidenced by decreased protein expression and gene activation associated with bone formation. This biomimetic folding paper co-culture system effectively mimics the natural bone microenvironment and provides a novel platform to study bone healing mechanisms. This approach may lead to the development of more effective treatments for bone fractures.