Talanta最新文献

Microplastics and nanoplastics (MNPs) as emerging pollutants present substantial risks to both the environment and human health. Developing highly sensitive methods to rapidly identify and detect low concentrations of MNPs in complex systems remains a considerable challenge. Here, an "off-on" switch-type photoelectrochemical (PEC) aptasensor was employed for the sensitive detection of polyvinyl chloride (PVC) and polystyrene (PS) MNPs. This PEC aptasensor was based on a two-dimensional organic-inorganic Z-scheme heterojunction and utilized an acetylferrocene-modified aptamer (Apt-AcFc) as PEC recognition and quencher probes. In general, combining Apt-AcFc with the photoelectrode efficiently quenches the photocurrent, transitioning it to the 'off' state. Conversely, the presence of MNPs greatly increases the photocurrent because the MNPs were specifically recognized by Apt-AcFc, causing Apt-AcFc detaching from the photoelectrode and transition to the 'on' state. The developed PEC aptasensor was employed for the detection of MNPs released from food packaging materials. Although the aptasensor displayed distinct sensitivities toward PVC and PS, it demonstrated a consistent linear dynamic range of 1-200 μg mL^-1 and a low detection limit of 0.1 μg mL^-1. This switching-type PEC aptasensor provides a rapid, sensitive, and reliable analytical platform for the determination of MNPs in food and environmental matrices.

This study compares methodologies and provides insights on best approaches for quantifying EU-listed critical raw materials. Europe's strategy towards greater resilience involves valorization of secondary resources such as mine tailings or by-products of metallurgical production that are typically discarded as waste, despite containing valuable resources. Thus, identifying the best methods to determine elemental concentrations in various resources is crucial for evaluating their recovery, both environmentally and economically. Elemental content is usually determined using techniques such as inductively coupled plasma mass spectrometry (ICP-MS) by digesting solid samples into liquid solutions, although challenging for some refractory materials. Direct analysis of solids by laser ablation (LA)-ICP-MS is another alternative and can be used for both bulk analysis and spatially resolved mapping of critical elements throughout the supply value chain, from raw materials to refined products. Here, we compare ICP-MS/MS analysis on dissolved samples, obtained after microwave-assisted acid dissolution, with LA-ICP-MS analysis on solid samples for determining rare earth element (REE) compositions of manganese ores and slags. For LA-ICP-MS, we present two approaches, particularly useful in cases where dissolution proves challenging: (i) analysis of glass beads obtained through borate flux fusion of raw powders, and (ii) direct analysis on pelletized powders using a novel non-matrix matched calibration strategy. Each method's feasibility and strengths are considered for the specific chemical composition of the samples of interests. The study shows that different methods have advantages and disadvantages related to particle size distributions and preparation costs that need to be considered during characterization of secondary resources.

Lateral flow assay (LFA) is the most widely used point-of-care (PoC) diagnostic tools due to their simplicity, rapid turnaround, portability, and low cost. Despite their extensive adoption in clinical diagnostics, food safety, and environmental monitoring, conventional LFA remain limited by inadequate sensitivity, poor quantification, and restricted multiplexing capability particularly for early-stage disease detection. This review systematically examines recent engineering strategies developed to overcome these limitations and advance LFA toward next-generation diagnostic platforms. We highlight progress in biorecognition element engineering, including antibodies, nanobodies, and aptamers, alongside innovations in nanomaterial-enabled engineering through advanced nanoparticle labels, signal reporters, such as nanozymes, fluorescent probes to improve signal amplification systems. Emerging approaches for sample preconcentration, nucleic acid amplification, and multiplex assay architectures are critically discussed in the context of analytical sensitivity enhancement and improving specificity. Importantly, recent integration of artificial intelligence (AI) and smartphone-based readout systems has transformed LFA into digitally enabled diagnostics, allowing objective interpretation, wireless data transmission and semi-quantitative analysis while preserving portability and low manufacturing cost. Collectively, these advances position modern LFA as adaptable, cost-effective, and scalable diagnostic tools capable of bridging the gap between rapid field tests and centralized laboratory assays, with significant implications for decentralized healthcare, large-scale screening, and global disease surveillance.

Chloramphenicol (CAP), a broad-spectrum antibiotic, faces stringent regulatory restrictions in clinical and food safety applications due to its severe toxicity. In practical scenarios, achieving highly sensitive and rapid detection of ultralow-level CAP presents a significant challenge in sensor system design. Here, we propose an orchestrated multi-level signal strategy to boost the sensitivity of an electrochemical aptasensor, enabling picomolar-level CAP detection. Amino-functionalized cobalt-based metal-organic framework nanosheets (Co-BDC(NH₂)) and thionine-labeled gold nanoparticles (thio-AuNPs) are synthesized as electron transfer carriers and signal amplifiers, respectively. In the presence of picomolar concentrations of CAP, the catalytic hairpin assembly (CHA) reaction of DNA is triggered, linking the aptamer-modified Co-BDC(NH₂) nanosheets with thio-AuNPs and facilitating precise CAP detection at low concentrations. Under optimal conditions, the aptasensor delivers an ultralow limit of detection (LOD) of 3.33 pM with a wide linear range from 10 pM to 50 nM. Notably, the aptasensor also exhibits excellent anti-interference capability, reproducibility, and stability in real matrices like milk, bottled water, and lake water. This work presents a high-sensitivity sensor platform design strategy that enables effective detection of ultralow-level targets beyond environmental substances through multiple signal amplification process.

This study presents a dual-signal biosensing platform for glucose detection based on alginate hydrogel beads (AlgelBeads). Its primary innovation lies in the integration of enzyme co-encapsulation with advanced image processing and pattern-matching algorithms. Glucose oxidase, horseradish peroxidase, and bovine serum albumin-templated gold nanoclusters are co-encapsulated within the AlgelBeads, which are uniform spheres approximately 2.1 mm in diameter, featuring a unique vein-like surface pattern that provides sites for glucose recognition and substrate catalysis. Upon exposure to glucose, an enzymatic cascade reaction is initiated, producing a visible color change to blue and simultaneous fluorescence quenching. For high-throughput analysis, a custom array plate was designed. The interpretation of the dual signals (colorimetric and fluorescent) is automated and enhanced using a Hough circle algorithm for AlgelBeads localization and a dictionary-based spatial classification algorithm for robust pattern recognition. Under optimized parameters, these AlgelBeads enable quantitative detection of glucose from 0.0625 to 4.0 mg/mL. The limits of detection are 0.077 mg/mL (colorimetric) and 0.030 mg/mL (fluorescent), with both precision and accuracy falling within acceptable limits. The AlgelBeads were applied for determining 54 serum samples collected from 18 pregnant women undergoing the oral glucose tolerance test, yielding results consistent with Beckman Coulter Glucose Assay Kit. These findings affirm the AlgelBeads offer an accurate and effective platform for glucose detection, holding potential for assisting in the diagnosis of gestational diabetes mellitus and other diabetes types in clinical settings.

Undoped and Ag-doped tungsten oxide (WO₃) sensors were synthesized using the spray pyrolysis technique. The thin films are characterized by various techniques like XRD, UV-Visible spectroscopy, SEM and FTIR. The UV-Visible analysis confirmed the modification of optical properties of tungsten oxide due to silver doping and effective reduction in band gap from 2.76 to 2.70 eV. XRD analysis showed the decrease in crystallite size of tungsten oxide (from 13.98 to 11.39 nm) due to Ag-doping. The SEM analysis revealed the spherical shaped nanoparticles of pure and Ag-doped tungsten oxide with the embedded flakes like structures. The study of sensing characteristics revealed that 7 % Ag-doped WO₃ shows the highest selectivity and response (98.5) to NH₃ gas having the limit of detection (LOD) 18.31 ppb. The material also shows quick response (12 s) and recovery (36 s) times as well as the enhanced long term stability for the period of 30 days.