Tridax procumbens is recognized for its allelopathic potential; however, information on the distribution of allelochemicals across plant organs and extraction methods remains limited. This study investigated the organ-specific and extraction-dependent allelochemical profiles of T. procumbens using methanolic and aqueous extraction analyzed by gas chromatography–mass spectrometry (GC–MS) and volatile profiling of fresh leaves using headspace solid-phase microextraction coupled with GC–MS (HS-SPME–GC–MS). Methanolic extracts revealed clear organ-specific differences in chemical composition, with root tissues dominated by stigmasterol (32.75%) and eicosapentaenoic acid methyl ester (25.18%), while stem extracts showed high levels of palmitic acid (22.80%), stigmasterol (22.89%), and squalene (20.71%). In contrast, leaf extracts exhibited a broader distribution of compounds, including squalene (12.27%), phytol (7.68%), and β-sitosterol (7.03%). Several compounds, namely squalene, stigmasterol, n-hexadecanoic acid, and phytol, were consistently detected across all organs. Aqueous leaf extracts were dominated by fatty acid methyl esters, particularly 9,12-octadecadienoic acid methyl ester (23.16%), indicating the presence of water-mobilizable metabolites, whereas HS-SPME–GC–MS analysis revealed a volatile profile dominated by compounds such as 2-hexenal (18.06%) emitted from leaf tissues. These findings demonstrate that T. procumbens exhibits an organ-specific and extraction-dependent allelochemical strategy involving stored, mobilizable, and volatile compounds, highlighting the importance of integrating plant organ differentiation and extraction pathways in allelopathic studies.

Se@Ti3C2/CNs nanocomposites were successfully prepared through combining Ti3C2 MXene with ZIF-8-derived nitrogen-doped carbon materials (CNs) and employing a selenization strategy. The morphology and structural information of the material were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS). Electrochemical investigation showed that this material exhibited outstanding electrocatalytic performance to acetaminophen (AP) and p-aminophenol (4-AP) and could detect them simultaneously in wider linear ranges of 0.10–400.00 μM and 0.10–330.00 μM respectively with detection limits of 45.7 and 46.0 nM, respectively. Analysis of water samples and artificial urine shows that the sensor based on this material provides satisfactory results for AP and 4-AP with satisfactory selectivity (current change is less than 5% of the initial value) and stability (current retains above 95% of its initial response after 10 days of storage), which offers potential applications in drug analysis and environmental monitoring.

Hydrogen peroxide (H2O2) is widely utilized as a disinfectant in the food industry, environmental monitoring and biomedical fields. However, excessive H2O2 residues in food products pose significant health risks, necessitating rapid and reliable detection methods. This study presents a novel colorimetric detection system based on a histidine-functionalized CuSO4 (Cu-His) nanozyme which exhibits peroxidase mimetic activity. The Cu-His nanozyme catalyzes H2O2 decomposition to generate hydroxyl radicals (·OH), oxidizing 3,3′,5,5′-tetramethylbenzidine (TMB) to produce a blue-colored product (ox-TMB). A paper-based test strip integrated with smartphone RGB analysis was developed for on-site quantification. Under optimized conditions (pH 3.5, 40 °C, 1.0 mg/mL Cu-His, 2.5 mg/mL TMB), the system demonstrated a linear detection range of 1–10 µmol/L and a limit of detection (LOD) of 0.508 µmol/L. The hydrogen peroxide test strips prepared using this system, after process optimization, exhibited a linear detection range of 5–150 µmol/L and an LOD of 1 µmol/L. After dilution and clarification, the test strip detection of milk samples confirmed their compliance with national safety standards, as no H2O2 residues were detected. This method offers a cost-effective, portable, and highly sensitive approach for H2O2 monitoring in food safety and environmental applications.

Isoniazid is a first-line therapy for the treatment of latent tuberculosis. However, the side effects associated with this drug can be very harmful to human health; thus, careful monitoring of the drug’s concentration in the human body is required. Electrochemical biosensors are a superior alternative to non-electrochemical methods of drug monitoring due to their simplicity, speed, miniaturization, and real-time capability. Nanomaterials have been used to develop advanced electrochemical biosensors by addressing limitations such as low sensitivity and selectivity. The unique properties, such as large surface area and conductivity, facilitate electron transfer and improve detecting limits and response times. Hence, the focus of this review is on various nanotechnology-based sensor developments for sensing and signaling isoniazid. The review introduces the use of electrochemical sensors modified using various nanomaterials and an enzyme-based bioreceptor for detecting isoniazid in diverse samples. Furthermore, the review focuses on the recent innovations in the use of nanomaterials and bioreceptors to design highly efficient electrode surfaces. Additionally, a detailed summary of the electrochemical approaches used for isoniazid analysis is reviewed.

This study presents an electrochemical sensor for nonenzymatic glucose determination operating in neutral media using Pt-Cu bimetallic hydrogels. For preparation, a two-step reduction method was employed for the mixed metal precursors of Pt and Cu. The synthesized bimetallic hydrogels were characterized using scanning electron microscopy (SEM), energy dispersive X-Ray spectroscopy (EDS), X-ray diffractometry (XRD), X-ray photoelectron spectroscopy (XPS), and electrochemical techniques. They featured a three-dimensional porous structure, good flexibility, and a high specific surface area. Under neutral pH conditions, the sensor exhibited high electrocatalytic efficiency toward glucose oxidation. It showed a rapid response to glucose at 0.2 V, with a linear detection range spanning 137 µM to 6 mM and retained considerable catalytic activity after one month. Significantly, it exhibited the same linear response up to 6 mM in diluted serum and artificial sweat samples. The sensor’s detection range covered the glucose concentrations in serum and sweat under physiological conditions, further demonstrating the feasibility of this nonenzymatic glucose sensor for real sample analysis and providing new insights for the research on nonenzymatic glucose sensors.

Vitrification is an effective method for treating municipal solid-waste incineration fly ash (MFA). However, the high melting temperature of MFA leads to substantial energy consumption and secondary volatilization of heavy metals. In this study, MFA was blended with coal gasification fine slag ash (FSA) and coal gangue ash (CGA) for co-vitrification. The influence of the blending ratio on heavy-metal solidification, vitreous-phase formation and environmental risk assessment of vitrified slags was investigated. Results indicated that when vitrified at 1300 °C, raw MFA achieved only ∼45% vitreous phase content, whereas blending with 50% FSA or CGA increased this value to >85%, successfully achieving complete vitrification. A 50% MFA + 50% FSA/CGA mixture exhibited higher solidification ratios for Cd, Pb, Zn, Ni and Cu than raw MFA. Cd, Cu, Pb and Zn predominantly existed in oxidizable and residual states within the vitrified slags, whereas Cr and Ni were almost exclusively in residual states. After co-vitrification with 50% FSA/CGA at 1300 °C, the comprehensive heavy-metal toxicity index dropped to ∼400, markedly lower than the 28700 value observed for MFA treatment.

The article describes a new type of edge-computing architecture which is able to do real-time signal denoising at sensor nodes in remote electrochemical environmental sensor networks. Electrochemical sensors are chemical sensors that identify the chemical species by electrode reactions and provide analog signals that need denoising to enable proper environmental analysis. The three-tier system, which is proposed offers (i) sensor nodes with local processing, (ii) edge gateways to do the aggregation, and (iii) the cloud infrastructure to store and do analytics and integrate adaptive filtering, variational modal decomposition (VMD), and neural network-based denoising at sensor nodes. The architecture performance is assessed by a discrete-event simulation system that examines the performance of the architecture with 100 distributed sensor nodes under different noise levels (0–20 dB signal-to-noise-ratio). Comparative analysis against conventional cloud-only processing demonstrates substantial improvements: signal-to-noise ratio increases by 15 dB, per-node energy consumption reduces by 40%, and data transmission volume decreases by 70%. The edge-optimized strategy allows the real-time reduction of noise and extends the network life and minimizes the communication expenses. The robustness in different environmental conditions and sensor densities is proved by statistical analysis with the help of Analysis of Variance (ANOVA) and Monte Carlo simulations. The results of the simulation show that edge-computing can be used to provide more accurate, energy-efficient, and scalable electrochemical sensor networks to remotely monitor the environment.

Overlapping fingerprints are frequently encountered at crime scenes, yet remain challenging to interpret due to the difficulty of resolving individual ridge patterns. This limitation significantly diminishes their forensic value in personal identification. Here, we present a method combining matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with multivariate statistical analysis for spatially resolved chemical differentiation of overlapping fingerprints. Principal component analysis (PCA) revealed that samples from Contributor A and Contributor B were distinctly separated along the first principal component, demonstrating high intra-contributor consistency and clear inter- contributor discrimination. Further classification using an orthogonal partial least-squares discriminant analysis (OPLS-DA) model yielded strong performance metrics (R2X = 0.976, R2Y = 0.965, Q2 = 0.943), enabling effective resolution of overlapping marks from different individuals. Notably, a discriminative compound at m/z 228.2566 was identified and localized to a single contributor. This study demonstrates that MALDI-MSI, in conjunction with chemometric modeling, provides a rapid, cost-efficient, and high-resolution strategy for the forensic analysis of complex fingerprint mixtures, and holds promise for operational integration into routine casework.

This study reports the development and optimization of analytical procedures for arsenic speciation in marine and freshwater microorganisms, including Ascophyllum nodosum, Fucus vesiculosus L., and Arthrospira platensis. Total arsenic content and extraction efficiencies were evaluated using both conventional and enzyme-assisted extraction methods. Enzymatic protocols employing hemicellulase and pectinase were particularly effective in enhancing the release of arsenic species from complex biological matrices. Arsenic speciation analysis was performed using liquid chromatography coupled with inductively coupled plasma mass spectrometry (LC-ICP-MS), which enables the detection of toxic inorganic species of arsenic (As). The methodology enabled the identification of arsenobetaine (AB), dimethylarsinic acid (DMA), monomethylarsonic acid (MMA), and inorganic arsenic (As(III) and As(V)) in all tested samples. Results indicated significant interspecies variation, with the highest arsenic concentration found in brown algae (62.1 μg g−1 dry weight) and the lowest in Arthrospira platensis (0.682 μg g−1). Enzyme-assisted extraction consistently outperformed traditional methods in terms of both yield and species preservation. The proposed method is reliable, reproducible, and applicable to food, environmental, and cosmetic quality control, where arsenic profiling is essential for safety and regulatory compliance. These findings provide new insights into arsenic behavior in microorganisms and offer a basis for improved monitoring of arsenic species in natural products. Future work will focus on identifying and quantifying arsenosugars and arsenic-phytochelatin complexes to further elucidate arsenic speciation pathways and their implications for human exposure and environmental health.

Watermelon soybean paste (WSP) is a traditional Chinese delicacy valued for its unique flavor and health benefits. Its volatile components, shaped by production methods and geographical factors, reflect WSP’s distinct characteristics and may influence consumer purchasing decisions. This study investigates the volatile compound characteristics in WSP samples from different regions and develops a rapid, accurate, and nondestructive method for their identification. To accomplish this objective, volatile compounds in WSP were extracted using headspace solid-phase microextraction (HS-SPME) and subsequently determined by gas chromatography-mass spectrometry (GC-MS). Multivariate statistical analyses, including correlation analysis, principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA), were employed to comprehensively evaluate the data. The results show that terpenes and esters are the main volatile compounds. OPLS-DA effectively distinguishes the samples and identifies 10 key differential compounds. A non-targeted GC-MS method combined with multivariate analysis provides a rapid and efficient way to differentiate WSP samples according to geographical origin.