Search Results (1,376)

Inorganic mercury ions (Hg²⁺) are highly toxic, posing a threat to aquatic ecosystems and human health. In this study, iron phthalocyanine (FePc) was anchored onto the surface of MXene via a self-assembly strategy to construct an FePc/MXene-x (F/M-x) heterostructure. Characterization by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption–desorption (BET) confirmed that the high specific surface area and good conductivity of MXene effectively inhibited FePc aggregation and increased the exposure of active sites. The F/M-x composite was then modified onto a glassy carbon electrode (GCE) to fabricate an electrochemical sensor, and the detection performance for Hg²⁺ was evaluated using square-wave anodic stripping voltammetry (SWASV). Under optimized conditions (pH = 5.0, accumulation at −1.2 V for 180 s), the F/M-100/GCE exhibited a linear range of 0.1–1.0 μM, a sensitivity of 19.02 μA/μM, and a detection limit of 5.9 nM. The sensor showed good anti-interference ability against coexisting metal ions such as Cd²⁺, Cu²⁺, and Pb²⁺, with a batch-to-batch RSD of 2.03% and a long-term stability RSD of 2.49%. Spike recovery experiments in real water samples (lake water and groundwater) verified the accuracy of the method. This study provides a new electrochemical platform for the rapid detection of trace Hg²⁺ in water environments. Full article

Background: Esophageal squamous cell carcinoma (ESCA) represents one of the most common aggressive malignancies worldwide. Insulin-like growth factor binding protein 1 (IGFBP1), a typical member of the IGF superfamily, is closely linked to adverse prognosis in numerous cancers. Up to now, little is known about its functional relevance to cell migration and tumor progression in ESCA. This work focuses on clarifying the relationship between IGFBP1 expression and the progression and migratory characteristics of ESCA. Methods: mRNA expression profiles from ESCA patients were obtained from the TCGA and GEO databases. Differential expression analysis was performed using R software(version 4.2.2), followed by an intersection of DEGs between datasets. The STRING database was applied to establish PPI networks. Cytoscape software(Version 3.7.2) was then used for visual presentation and hub gene identification. IGFBP1 expression was validated in ESCA tissues versus adjacent normal tissues. Prognostic correlation was assessed using GEPIA, while diagnostic and predictive values were evaluated through ROC analysis and Cox regression. Genetic alterations of IGFBP1 were analyzed via cBioPortal. Immune cell infiltration patterns were investigated using TIMER. Functional enrichment analyses (GO, KEGG) were performed on IGFBP1-associated DEGs. In the in vitro experiments, esophageal cancer cell lines (such as Eca109 and TE-1) and normal human esophageal epithelial cell lines (such as HEEC) were selected. The transcriptional level of IGFBP1 was examined using RT-qPCR, while Western blot analysis was conducted to validate its protein expression changes. Changes in the proliferative capacity of cancer cells after IGFBP1 silencing were detected by the CCK-8 assay, and cell migration capacity was determined via wound scratch assays to clarify the related biological effects. Results: Overall, 2870 DEGs were screened from the GEO database, 153 DEGs were screened from the TCGA database, and 34 genes were found to be common to both databases; 10 core genes were screened from the PPI network. IGFBP1 was abnormally expressed in esophageal cancer. Cox regression confirmed that IGFBP1 is an independent risk factor, and prognostic analysis indicated that IGFBP1 is closely associated with poor prognosis. Gene mutation analysis showed that amplification mutations are the most common type of IGFBP1 gene mutation, and genetic alterations in IGFBP1 in ESCA patients are significantly associated with overall survival (OS) (p = 0.0002568). GO analysis indicated that IGFBP1-related differentially expressed genes were enriched in organic anion transport, epidermal development, apical cell components, and metal ion transmembrane transporter activity. Pathway enrichment based on the KEGG database illustrated the main enrichment of target genes in neuroactive ligand–receptor interactions, calcium signaling and cAMP signaling pathways. Additionally, remarkable differences in immune cell infiltration were observed between IGFBP1 high-expression and low-expression subgroups through tumor immune profiling. IGFBP1 expression differed significantly between esophageal cancer cells and normal esophageal epithelial cells, as detected by RT-qPCR (p < 0.05). Moreover, knockdown of IGFBP1 markedly inhibited the proliferation (p < 0.05) and migration abilities (p < 0.05) of TE-1 and Eca109 cells. Conversely, IGFBP1 overexpression facilitated these cellular processes. Conclusions: As a key oncogenic driver for ESCA, IGFBP1 may participate in the oncogenesis of ESCA, possibly influencing clinical outcomes via IGF signaling and the tumor microenvironment. Its dual functions in tumor and immune systems suggest it might be a candidate for ESCA immunotherapy research. Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

Recovered graphite from spent lithium-ion batteries is an important secondary resource that can reduce reliance on primary graphite and lower the environmental footprint of battery production. In this work, graphite obtained as a carbon-rich residue after industrial hydrometallurgical leaching of NMC111 black mass (2 M H₂SO₄ + 3% H₂O₂) is subjected to three post-leaching washing treatments to assess how far simple, low-intensity steps can further clean the leach residue while preserving the carbon structure. The washing routes are water washing (GW), water washing with ultrasonication (GU) and mild sulfuric-acid washing with 0.1 M H₂SO₄ (GA). ICP-OES and SEM–EDX show that, relative to the leached black mass, all washing treatments reduce residual transition-metal contents by two to three orders of magnitude, and that the mild acid wash provides the lowest bulk metal levels, with several elements at or below detection limits. X-ray diffraction and Raman spectroscopy indicate graphite-dominated patterns and improved structural order, with the ID/IG ratio decreasing from 0.62 (GW) to 0.11 (GA) and the corresponding in-plane crystallite size increasing from 30.6 nm to 168 nm. Overall, the mild acid washing step is the most effective low-impact post-leaching purification route, yielding a thoroughly cleaned low-metal graphite fraction that preserves the graphite framework and constitutes a suitable intermediate for further upgrading or reuse in secondary applications. Full article

Food safety problems caused by pesticide residues, heavy metals, foodborne pathogens, mycotoxins and other hazards seriously threaten public health. Traditional detection methods have the limitations of cumbersome operation, high cost and poor stability, which make it difficult to meet the needs of rapid and sensitive detection on site. As a new material, nanozymes have the advantages of high stability, low cost and high catalytic activity, showing great application potential in food safety analysis. Among them, gold–platinum (AuPt) bimetallic nanozymes have attracted much attention due to their synergistic catalytic effect, good biocompatibility and modifiability. In this paper, the synthesis methods of AuPt bimetallic nanozymes were systematically reviewed, including chemical reduction, sol–gel, microemulsion, electrochemical deposition, and so on. The control effect of AuPt bimetallic nanozymes on catalytic activity was discussed from the aspects of composition, morphology, structure, external environment and composites with other nanomaterials. The research progress of AuPt bimetallic nanozymes in the detection of pesticide and veterinary drug residues, heavy metal ions, mycotoxins, foodborne pathogens, food additives and food freshness was introduced. Finally, the challenges and future development of AuPt bimetallic nanozymes in food safety analysis were prospected, aiming to provide theoretical reference and design ideas for the construction of a high-performance food safety rapid detection platform. Full article

Metal–organic frameworks (MOFs) exhibit high specific surface area and porosity, which may facilitate electron transfer during electrochemical reactions. Therefore, it is clear that MOFs are promising materials for the development of electrochemical sensors. In particular, zinc (Zn) based MOFs offer several advantages such as high specific surface area, porosity, environmental friendliness and low cost. Thus, Zn-based MOF materials and their composites have been extensively utilized in the detection of various pollutants, biomolecules and food additives. The Zn-MOF-based materials have been extensively utilized in electrochemical and fluorescence sensing applications. Previously, various Zn-MOF-based sensing systems such as pristine Zn-MOF, carbon-supported Zn-MOF composites, MXene hybrids with Zn-MOF, and bimetallic/trimetallic Zn-based MOFs were explored to enhance sensing performance. Such materials exhibit remarkable analytical performance, such as a low limit of detection (LOD) (nM to pM range), wide linear response range (LR), fast response times, and high selectivity in the presence of interfering species. In electrochemical sensing, Zn-MOF-modified electrodes demonstrated improved charge-transfer kinetics and sensitivity, enabling accurate determination of the biomolecules, drugs and heavy metal ions in real samples. Similarly, Zn-MOF-based fluorescence sensors showed high luminescent properties and displayed sensitive detection of pollutants and biomolecules. Despite such promising sensing performances, some challenges, such as low stability, reproducibility and selectivity in real-time monitoring, etc., remain that need to be overcome. This review article summarizes the previously reported literature on the fabrication of Zn-MOFs, their composites and Zn-MOF-derived materials for the development of electrochemical and fluorescence sensors. We have also discussed the future directions for the rational design of the high-performance Zn-MOF-based sensing systems for environmental and biomedical applications. We believe that the present review article would be useful for the scientific community working on the fabrication of Zn-MOF-based sensors. Full article

Spent lithium-ion battery electrolytes contain fluorine-, sulfur-, and phosphorus-bearing toxins, necessitating deep detoxification and directional conversion into C₁–C₆ light hydrocarbons. To elucidate the specific catalytic roles and sequential activation of cathode metals (Li, Ni, Co, Mn), this work systematically deconvolutes their mono- and multi-metallic migration mechanisms over a CaO-ZSM-5* catalyst during vacuum catalytic pyrolysis (530 °C, 100 Pa). Results reveal that Li⁺ and Ni²⁺ dominate C–O bond cleavage in carbonates and CaO-ZSM-5*-assisted decarboxylation and oxygen fixation, significantly increasing the relative hydrocarbon content. Conversely, Co^2/3+ and Mn⁴⁺ release reactive oxygen species, causing deep oxidation of hydrocarbons into CO₂ and antagonizing the targeted conversion. In multi-metallic systems, forming composite metal oxides (M_xN_yO_z) increases the energy barrier for releasing active catalytic ions, hindering carbonate cleavage and leaving unreacted carbonate feedstocks. For detoxification, F and P are effectively immobilized as CaF₂ and Ca₂P₂O₇. The relative content of detected gas-phase nitriles is minimized to <2% due to the strong antagonistic effect of Ni²⁺ on Li⁺-promoted hexanedinitrile cleavage, while sulfur species derived from 1,3-propane sultone are converted to SO₂ and ultimately mineralized as calcium and metal-sulfur salts. Mechanistically, product distributions and crystallographic properties suggest a hypothesized sequential activation model—Li⁺ → Ni²⁺ → Mn⁴⁺—governing reactivity, whereas Co^2/3+ does not participate in the synergistic detoxification and selective upgrading process. This migration–reaction coupling framework provides critical insights for cathode-assisted in situ catalytic pyrolysis and closed-loop electrolyte recycling. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article

Metallic contamination is a critical manufacturing defect in lithium-ion batteries, but the degradation evolution and electrochemical signatures of Cu-contaminated cells remain insufficiently understood. In this study, Cu particles were intentionally introduced into graphite/NCM811 pouch cells to investigate Cu-induced internal short circuit, cycling degradation, and defect detection. The Cu-contaminated cells exhibit significantly higher initial self-discharge rates, indicating the formation of a cathode-to-anode type internal short circuit. X-ray microscopy and SEM/EDS characterization reveal local separator penetration, electrode deformation, Cu dissolution/migration/deposition, Al current collector dissolution, and deposit accumulation on the anode surface. After cycling, the Cu-contaminated cells showed accelerated capacity fade and increased direct current internal resistance, while their self-discharge rate gradually decreased, suggesting partial mitigation of the internal short circuit path. Incremental capacity analysis was used to evaluate the internal short circuit severity, while differential voltage analysis was further applied to distinguish a Cu-induced internal short circuit from normal aging. This work provides mechanistic insight into Cu-contamination-induced degradation and electrochemical signatures for identifying metallic-contamination defects in lithium-ion cells. Full article

Graphitic carbon nitride (g-C₃N₄), an emerging metal-free semiconductor material, has attracted considerable attention in the field of photoelectrochemical (PEC) sensing due to its unique electronic structure, excellent chemical stability, and visible-light responsiveness. This article systematically reviews recent advances in research on g-C₃N₄-based PEC sensors applied to water environment monitoring. First, the fundamental physicochemical properties of g-C₃N₄ are introduced, along with its advantages and limitations in PEC sensing applications. Subsequently, four main performance enhancement strategies are outlined: heterojunction construction (including type II, Z-scheme, and S-scheme heterojunction), elemental doping and defect engineering, morphology control and nanostructure design, as well as various signal amplification approaches such as self-powered systems, dual-mode detection, and cyclic amplification. Furthermore, the current application status of these sensors in detecting typical water pollutants, including heavy metal ions (e.g., Pb²⁺, Cu²⁺, Cd²⁺, Hg²⁺), antibiotics (e.g., tobramycin, norfloxacin, kanamycin), pesticide residues (e.g., chlorpyrifos, atrazine, glyphosate), and pathogenic microorganisms (e.g., Salmonella, Candida albicans), is comprehensively reviewed, with particular emphasis on detection sensitivity, selectivity, and real-sample performance. Finally, the remaining challenges in terms of long-term stability, anti-interference capabilities in complex matrices, portability, and multifunctional integration are analyzed, and future development directions are proposed, including smartphone-based intelligent sensing, CRISPR/Cas12a-assisted signal amplification, and multi-target high-throughput detection. This review aims to provide a reference for the rational design and practical application of g-C₃N₄-based PEC sensors in the field of water environment monitoring. Full article

Copper is an indispensable trace element for maintaining metabolic homeostasis; however, the dysregulation and subsequent accumulation of Cu²⁺ are critically linked to neurodegenerative pathologies, including Alzheimer’s disease in humans. Consequently, the development of robust analytical tools for Cu²⁺ monitoring is of paramount importance. Here, we report a 2,2′-dipicolylamine porphyrin (DPAP)-based fluorescent sensor designed for the precise detection of metal cations. Photophysical investigations reveal that DPAP operates via a rapid turn-off fluorescence mechanism, achieving high-performance sensing in the parts-per-million range. Notably, the probe demonstrates exceptional sensitivity with detection limits of 26.3 nM for Cu²⁺ and 34.8 nM for Ni²⁺. Interference studies demonstrated the selectivity of DPAP for Cu²⁺ over a diverse range of competing metal ions such as Na⁺, Ag⁺, Ni²⁺, Cr³⁺, Pb²⁺, Al³⁺, Fe²⁺, Cd²⁺, and Zn²⁺. These results indicate that DPAP is a sensitive and selective probe suitable for copper ion detection. Full article

The literature collected from various search engines and high-quality scientific databases reveals that amino acid (AA)-functionalized nanoparticles have emerged as a promising field for selective detection and remediation of heavy metals (HMs). Among the various nanoparticles (NPs), gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) have drawn considerable attention, attributed to their unique optical, catalytic, and surface plasmon resonance properties. Functionalization with amino acids significantly enhances nanoparticle stability, biocompatibility, and metal-binding affinity through diverse functional groups. AA-functionalized AuNPs, including glycine, cystine, leucine, methionine, tyrosine, aspartic acid, histidine, and lysine-capped systems, exhibit tunable selectivity toward heavy metal ions. Bifunctionalization strategies further enhance sensitivity by inducing nanoparticle aggregation or signal amplification. Beyond single amino acids, polypeptides and protein-functionalized AuNPs offer enhanced molecular recognition and multivalent binding, expanding their applicability in complex matrices. Similarly, amino acid-functionalized AgNPs, such as those capped with similar amino acids stated above, exhibit strong interactions with heavy metals, AA bifunctionalization, and bimetallic nanoparticles (BNPs), particularly amino acid-functionalized Au–Ag systems, which combine the advantages of both metals, leading to improved sensitivity, selectivity, and signal strength. Although these advances have been made, a major gap remains in the systematic comparison of different amino acids, peptides, and bimetallic systems under real-world conditions. This gap can be addressed by standardized testing methods, clearer structure–function relationships and combined experimentation to guide the rational design of more efficient AA-functionalized nanoparticles. Full article

Metal-organic frameworks (MOFs), particularly ELM-11, are promising sorbents for CO₂ capture due to their gate-opening phenomenon and excellent reusability. Since actual exhaust gases contain impurities such as NO₂, in this study, the effect of NO₂ on the CO₂ sorption performance of ELM-11 was investigated. ELM-11 was exposed to 1000 ppm NO₂ for varying durations, ranging from short to long, and subsequent CO₂ sorption was evaluated using several methods: gravimetric analysis (TG-DTA), volumetric analysis (sorption isotherms), FT-IR spectroscopy (to detect chemical bond changes), TG-MS (to analyze decomposition products), and PXRD (to observe structural changes). The TG-DTA results indicated that long-term NO₂ exposure (e.g., 20 h) generally reduced CO₂ sorption, whereas short-term exposure (3 h) could enhance it. This finding was supported by volumetric sorption isotherm measurements. FT-IR and TG-MS analyses revealed that NO₂ underwent both physical and chemical sorption in small amounts, with chemical sorption occurring through reactions with Cu²⁺ ions. Consequently, 20 h of NO₂ exposure resulted in approximately a 6 or 10% reduction in CO₂ recovery capacity. However, since the degradation was only 6 or 10% despite exposure to a relatively high concentration of NO₂ (1000 ppm), these results suggest that ELM-11 exhibits high resistance to NO₂, making it suitable for practical applications. Full article

Chromochloris zofingiensis is a promising source of high-value bioproducts, particularly carotenoids and fatty acids. In this study, three selected chemical agents, including methylene blue (MB), salicylic acid (SA), and zinc sulfate heptahydrate (ZN), representing their roles as an oxidant, a signal transducer, and a metal ion, respectively, were applied at 96 h post-inoculation to stimulate metabolite accumulation via a two-stage cultivation approach. None of the treatments significantly affected algal growth. Among the treatments, HPLC analysis showed that 2.5 mM ZN significantly exhibited a dual stimulatory effect on astaxanthin (1.679 ± 0.122 mg g⁻¹) and lutein (4.257 ± 0.183 mg g⁻¹) accumulation, which were 2.28- and 2.91-fold higher than the control, respectively. The 1 µM MB significantly enhanced the canthaxanthin content to 2.382 ± 0.210 mg g⁻¹ (a 3.57-fold increase). Different SA concentrations selectively induced the target pigments of astaxanthin and lutein. APCI-QTOF analysis enabled the detection of echinenone in the microalgal extracts. Its identity and quantification were subsequently validated by HPLC, with the highest content detected under the 0.2 mM SA treatment. GC-FID analysis revealed changes in the composition of six major fatty acids, with C18:1 n-9 representing 50.01% of the total fatty acids under the 2.5 mM ZN treatment. These findings suggest that the two-stage approach could offer a practical and feasible strategy for microalgal biorefineries. Full article

Heavy metal ions are common environmental contaminants that threaten aquatic ecosystems and human health. However, conventional analytical techniques often require expensive instrumentation and complex sample pretreatment. This work presents a peptide-based multichannel solid-state potentiometric microelectrode array for simultaneous detection of Cu²⁺, Cd²⁺, and Pb²⁺. The array consists of one shared Ag/AgCl reference electrode and three groups of gold indicator microelectrodes (10 μm in diameter), with each group containing three parallel electrodes for replicate measurements. Each group is functionalized with a distinct peptide receptor specific to one target metal ion. The proposed array exhibits near-Nernstian responses and good selectivity against common interfering ions. Practical applicability is demonstrated by analyzing spiked lake water samples under a fixed chloride condition, with recoveries ranging from 94% to 105%. This work provides a miniaturized, reproducible, and versatile platform for simultaneous potentiometric detection of multiple heavy metal ions, and the peptide-based recognition strategy can be extended to other targets by simply replacing the peptide sequences. Full article