Analyst最新文献

Fluorescent supramolecular materials have emerged as versatile tools for detecting nitroaromatic compounds (NACs), which are key components of explosives and persistent environmental pollutants. Among these analytes, 2-nitrobenzaldehyde (2-NBZ) warrants particular attention due to its high toxicity, environmental persistence, and industrial relevance. 2-NBZ exhibits ecotoxicity (GHS H412 Harmful to aquatic life with long-lasting effects) and is classified by the US EPA as a priority pollutant because of its persistence and aquatic toxicity. However, selective and sensitive detection of 2-NBZ in complex environmental matrices remains challenging. This study reports a series of 2,6-dihydroxyacetophenone[4]arene based derivatives (DAP-AAQ: S_3.1-S_3.4). All sensors operate efficiently in aqueous solution at neutral pH and exhibit a selective "turn-on" fluorescence response toward 2-NBZ; among them, S_3.1 demonstrated the strongest binding constant (K_a = 2.0 × 10⁵ M^-1) and the lowest detection limit (LOD = 0.25 µM), while showing negligible response to other common nitroaromatics. HR-MS titration and density functional theory (DFT) calculations reveal the formation of a 1 : 1 host-guest complex stabilized by hydrogen bonding and dipole-dipole interactions, driving an interamolecular charge-transfer quenching mechanism. Furthermore, the optimized fluorescent sensor was successfully integrated with a microcontroller (Arduino) RGB detection platform. The developed device demonstrated good analytical performance in water samples, with recovery values of 93.5-96.5%, % RSD below 1.5%, while maintaining relative errors below 6% compared to a standard fluorescence spectrometer. Overall, the proposed approach presents a simple, cost-effective, and reliable platform for on-site 2-NBZ detection.

In this study, we present the development of micro-spatially offset Raman spectroscopy (micro-SORS) methods and data analysis routines for the study of pigment degradation processes in the cultural heritage field, exploiting micro-SORS ability to non-invasively investigate the inner portions of turbid materials. The purpose of the study is to demonstrate an automated reference-free method to visualize through micro-SORS mapping the distribution of degradation both on and below the surface. The need arises from the handling of large datasets provided by micro-SORS mapping, which are often troublesome to analyse manually and usually require prior knowledge of the sample composition. Unaged and artificially aged painted mock-up samples were analysed with micro-SORS mapping, and conventional map reconstruction was compared with both supervised and unsupervised learning methods. Representative features in the micro-SORS spectra, able to distinguish unaltered pigments and degradation products, were automatically selected through machine learning techniques, revealing hidden patterns and correlations. Through the important spectral features (wavenumbers) and clustering analysis, quantitative micro-SORS degradation maps were created to identify degradation patterns also below the sample surface. Unlike previous studies that only use supervised or unsupervised learning, both are combined in this study to ensure the relevance of the selected spectral features and discover correlations among spectra through clustering techniques. This approach can be valid also for other scientific fields, such as forensic or biomedical, where data visualization and pattern identification are essential.

Microfluidic techniques for high-throughput encapsulation are powerful tools in single-cell analytics and cytokine profiling. Inertial focusing microfluidics is widely used to align particles in uniform sequences, enhancing encapsulation efficiency. However, on-chip sample dilution strategies to further optimize efficiency remain largely unexplored in deterministic encapsulation approaches, both experimentally and through theoretical modeling. Here, we present a high-yield microparticle encapsulation method that combines inertial and hydrodynamic focusing to enable precise tuning of microparticle spacing and modulation of capture efficiency, thereby offering enhanced operational flexibility for controlled particle encapsulation. We first investigate the microparticle self-ordering behavior within the spiral loop and characterize both flow dynamics and droplet formation regimes. By varying the sheath-to-sample flow rate ratio from 0 to 2, we observe that higher ratios increase the interparticle spacing and shift particles closer to the channel wall. These trends align with both analytical modeling and 3D numerical simulations. Notably, at higher sheath flow ratios (e.g., 1 and 2), single-particle encapsulation exceeds 76%, significantly surpassing Poisson distribution predictions. Moreover, single-cell capture efficiency exceeds 60% under these conditions. In co-encapsulation experiments, we achieved a one-cell-multiple-beads co-encapsulation efficiency near 40%, marking a significant improvement over the Poisson limit. For single-cell applications, we performed co-encapsulation of THP-1 monocytes and streptavidin-coated magnetic beads for TNF-α cytokine detection following lipopolysaccharide stimulation. Cytokine secretion was successfully detected at the single-cell level in both aqueous droplets and alginate hydrogels. We anticipate that this method will offer a promising platform for probing cell-cell interactions and immune responses at single-cell resolution.