The Royal Society of Chemistry最新文献

Safflower seed oil is expensive, and there are issues in the market such as the adulteration with cheaper edible oils, leading to inferior products. Near-infrared spectroscopy (NIR) is a non-destructive analytical method with the advantages of being fast, non-destructive, and pollution-free. However, in the case of low-concentration adulterated oil, the main components and their contents are nearly identical to those in pure oil, and the spectral feature peaks of the samples are similar, making it difficult for conventional analysis methods to select effective characteristic variables. Extreme Learning Machine (ELM), as a single-hidden-layer feedforward neural network model, possesses strong feature extraction and model representation capabilities. However, its random initialization of weights and biases leads to the issue of blind training. The Sparrow Search Algorithm (SSA) can effectively optimize the random initialization problem in ELM, but it is prone to issues such as being trapped in local optima and slower convergence. This study proposes a novel quantitative adulteration analysis model for safflower seed oil (ISSA-ELM), which combines ELM with an improved Sparrow Search Algorithm (ISSA). In the experiment, safflower seed oil was used as the base oil, and peanut oil, corn oil, and soybean oil were gradually added to prepare adulterated oil samples. To ensure the resolution of experimental samples and accurately capture spectral variations in the low-concentration range, a concentration gradient of 2% was used for adulteration levels between 2% and 70%, while a 5% gradient was applied for concentrations exceeding 70%. Each type of adulterant had 42 gradient concentrations, with 6 samples per concentration, totaling 252 samples. Original spectral data were collected, and the samples were randomly divided into a training set (70%) and a testing set (30%). Different preprocessing methods and feature extraction techniques were combined to discuss the results of three adulteration analysis models: PLS, SSA-ELM and ISSA-ELM. The final experimental results show that compared to the safflower seed oil adulteration prediction model based on SSA-ELM (R² = 0.8938, RMSE = 0.0835, RPD = 2.9018)and PLS(R_P² = 0.9015, RMSEP = 0.0876, RPD = 3.0128), the model based on ISSA-ELM (R_P² = 0.9934, RMSEP = 0.0207, RPD = 10.1457) offers higher prediction accuracy and stability, and the error detection limit of ISSA-ELM can reach 2%. The ISSA optimized the SSA's tendency to reduce population diversity, slow convergence, and get stuck in local optima in the later stages of iteration, greatly enhancing the global optimization ability of the algorithm. Therefore, ISSA-ELM can effectively identify adulterated safflower seed oil, providing a technological pathway and basis for research into the adulteration of safflower seed oil.

Tactile sensors based on triboelectric nanogenerators (TENGs) showed great potential for self-driven sensing in material identification. The existing TENG devices used strongly electrophilic materials as friction layers. For test materials with electrophilicity, their output signals are weak and difficult to efficiently recognize. Here, a TENG-based sensor with boron nitride nanosheets/waterborne epoxy (BNNSs/WEP) composites as the friction layer was proposed for improving the accuracy of identifying negative charged materials. During the process of contact friction with negative charged objects, the as-fabricated TENG device displayed excellent output performance, with a maximum output voltage of 2.7 V and a charge density of 88.32 nC m^-2. Combining deep machine learning and the friction electric effect, we developed a material recognition system for TENG sensors with integrated fatigue testing, data processing, and display modules. Following the training of the convolutional neural network (CNN) model with friction electrical signals generated by TENGs, the model demonstrated high accuracy in recognizing eight different materials, with a confusion matrix accuracy of 100%. Then, a sensor was developed for real-time device monitoring, with recognition accuracy of 100%, 100%, 55% and 49% for four kinds of materials. This work will further facilitate the development of a material perception system in the machine intelligence field.

The present work describes a new and practical approach for the one-pot preparation of 4-aryl-NH-1,2,3-triazoles and 4-glycosyl-NH-1,2,3-triazoles from a multicomponent reaction involving aldehydes, nitroalkanes, and sodium azide in the presence of 5 mol% of silicomolybdic acid (SMA) as a versatile and biocompatible catalyst. One-pot sequential formation of C-C and C-N bonds, high reaction yield, biocompatible nature of catalyst, easy handling, and broad substrate scope of 1,2,3-triazoles are the notable advantages of the present protocol.

This report develops a coumarin-nicotinic hydrazide-based sensor for detection of CN^- ions. The ligand is prepared by simple method and has been characterized by ¹H NMR, ¹³C NMR, FTIR, and mass spectral analyses. The sensing behavior of coumarin-nicotinic hydrazide (CNH) probe was investigated in the presence of different anions using UV-visible and fluorescence methods. The sensor showed a selective naked-eye, colorimetric, and fluorescence detection particularly in the presence of CN^- ions over the other anions. The sensing involves a ratiometric red shift in absorption and photoluminescence profile in presence of incremental addition of CN^- ions. This provides a multi-detection point at two different wavelengths. This sensor involves a displacement type approach through a nucleophilic substitution-based chemodosimeter sensor proposed based on ¹H and ¹³C NMR along with D₂O exchange experiment, and mass and photophysical studies. Interestingly, the sensor affords the lowest limit of detection up to 5.97 × 10^-7 M. The practical utility of the sensor is illustrated in molecular logic gate operation and real water analysis.

Exploring earth-abundant electrocatalysts for efficient hydrogen evolution reaction (HER) is important for the development of clean and renewable hydrogen energy. Herein, we demonstrated that fluorine anion doping into CoPS significantly enhanced its hydrogen evolution reaction (HER) activity. Controlled fluorination modulated the surface electronic structure of CoPS active sites, forming an optimized F-CoPS electrocatalyst that exhibited superior performance, achieving an overpotential of only 74 mV at 10 mA cm⁻² and 141 mV at 50 mA cm⁻², along with remarkable stability over 50 hours. This study provides a novel and feasible approach to enhance the HER performance of ternary pyrite-type transition-metal phosphosulfides.

Copper selenide (Cu₂Se) has been extensively studied due to its promising thermoelectric properties in bulk form. However, the miniaturization of thermoelectric devices using thin films is highly desired for smart applications. To date, there are few reports on composite thin films of Cu₂Se for thermoelectric applications, primarily due to their lower conversion efficiency. In the present work, Cu₂Se-based multiphase nanocomposites are presented to demonstrate enhanced conversion efficiency. The detailed structural characterization reveals that thermally evaporated Te-doped Cu₂Se thin films have multiphase compositions. The electrical conductivity decreases after Te-doping, due to enormous scattering of carriers against secondary phases and lattice defects. However, upon further increasing Te-doping concentration, both the electrical conductivity and Seebeck coefficient start increasing simultaneously, due to the formation of Cu₂Te nanoclusters and Te–Se solid solution, in the matrix of Cu₂Se. We emphasize the power factor, with the highest value reaching 234.0 μW mK⁻² at 400 K, as a key indicator of thermoelectric performance. A slightly overestimated value of dimensionless figure-of-merit (ZT) of 0.2 was obtained using the power factor and merely the electronic part of the thermal conductivity. The current synthesis route synergizes the effects of a multiphase system in thin film research to enhance the thermoelectric efficiency of Cu₂Se and related materials classes.

Chitosan-type I collagen hydrogels are paradigms of polysaccharide-protein assemblies with applications as biomaterials. However, preparing physical hydrogels combining them at comparable, high concentrations (>20 mg mL^-1) within interpenetrated networks remains challenging. Here, we could combine chitosan and collagen solutions at 25 mg mL^-1 to prepare two different types of concentrated hydrogels. When neutralized under ammonia vapours, mixed solutions form composite hydrogels, where collagen fibers exhibiting an unusual, branched morphology occupy a chitosan network porosity. In contrast, neutralization by immersion in liquid ammonia yielded hybrid networks where collagen microfibrils were associated with chitosan nanoaggregates. Structural variations impacted the mechanical behaviour and biological properties, assessed by 2D cultures of fibroblasts, of these hydrogels. Differences in gelation kinetics between the two biomacromolecules in the two processes appeared as a key factor driving the mixed network structuration. This work discloses a new route to obtain dense hydrogels from binary biopolymer systems and offers additional insights into the underlying gelation process.

Osteoarthritis (OA) is a prevalent degenerative joint disorder characterized by cartilage degradation, chronic inflammation, and progressive joint dysfunction. Despite rising incidences driven by ageing and increasing obesity, potent treatments remain elusive, exacerbating the socioeconomic burden. OA pathogenesis involves an imbalance in extracellular matrix (ECM) turnover, mediated by inflammatory cytokines and matrix-degrading enzymes, leading to oxidative stress, chondrocyte apoptosis, and ECM degradation. Additionally, synovial inflammation (synovitis) plays a critical role in disease progression through molecular crosstalk with cartilage and other joint tissues. Existing in vitro and in vivo OA models face significant limitations in replicating human pathophysiology, particularly the complex interplay between joint tissues. Equine models, due to their anatomical and cellular similarities to humans, offer translational relevance but remain underutilized. This study aims to develop an advanced 3D coculture system using equine chondrocytes and synoviocytes to simulate tissue-level interactions and fluid mechanical forces involved in OA. By incorporating inflammatory stimuli and gravity-driven cyclic fluid actuation, this model enables the study of OA-related molecular interactions in both healthy and diseased conditions under dynamic fluid conditions. Findings from this research provide important insights into pathological tissue crosstalk. In turn, this can help to better understand underlying inflammatory pathways and the potential contribution of fluid flow as an influential factor on the tissue microenvironment.

This study pioneers a sustainable strategy for synthesizing yellow-emissive carbon dots (Y-CDs) using expired rabeprazole sodium tablets, thereby transforming pharmaceutical waste into valuable nanomaterials. The as-prepared Y-CDs displayed a high quantum yield of 48.89%, strong photostability, and pronounced environmental resilience. These attributes establish their potential as reliable fluorometric probes. The fluorescence of Y-CDs was effectively quenched by vitamin B12 through a dual mechanism involving the inner-filter effect (IFE) and static quenching. Under optimized conditions, the fluorescence intensity ratio (F⁰/F) showed excellent linearity in the range of 0-300 μM and achieved a detection limit of 8.0 nM (S/N = 3). The developed method demonstrated high accuracy (recoveries of 96.8-105.9%) for pharmaceutical formulations. Beyond its analytical merits, this work introduces a green nanotechnology route that addresses pharmaceutical waste management by converting expired drugs into efficient, multifunctional nanomaterials.

Recent uses of chlorine gas in violation of the Chemical Weapons Convention are difficult to identify through chemical analysis as unique signatures of exposure have not been identified. We exposed living pine seedlings and English ivy to chlorine gas, extracted the pine needles, and analyzed the extracts by liquid chromatography-time of flight mass spectrometry (LC-qTOF) and comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-TOF). Data from exposed seedlings was compared to unexposed seedlings and bleach-treated seedlings using commercial and Battelle proprietary software to identify unique or elevated markers of exposure. Battelle also used targeted mass spectrometry to evaluate 3-chlorotyrosine and 3,5-chlorotyrosine as chlorine exposure biomarkers that were expected to be present in exposed pine needles. We discovered ten (10) chlorine exposure biomarkers in chlorine gas-exposed pine needle and ivy leaf extracts using survey mass spectrometry methods. Additional survey mass spectrometry analysis suggested additional biomarkers (chlorinated glycosylated flavonoid analogs) may be present but that sufficient levels were not generated for detection in extracts from the chlorine gas-exposed samples. Targeted analysis for 3-chlorotyrosine and 3,5-dichlorotyrosine indicated presence of 3-chlorotyrosine in extracts from exposed ivy.