Search Results (317)

Degradable fluorescent sensors present a promising portable approach for heavy metal ion detection, aiming to prevent secondary environmental pollution. Additionally, the excessive intake of ferric ions (Fe³⁺), an essential trace element for human health, poses critical health risks that urgently require effective monitoring. In this study, we developed a thermally degradable fluorescent hydrogel sensor (Eu-CDs@DPPG) based on europium-doped carbon dots (Eu-CDs). The Eu-CDs, synthesized via a hydrothermal method, exhibited selective fluorescence quenching by Fe³⁺ through the inner filter effect (IFE). Embedding Eu-CDs into the hydrogel significantly enhanced their stability and dispersibility in aqueous environments, effectively resolving issues related to aggregation and matrix interference in traditional sensing methods. The developed sensor demonstrated a broad linear detection range (0–2.5 µM), an extremely low detection limit (1.25 nM), and rapid response (<40 s). Furthermore, a smartphone-assisted LAB color analysis allowed portable, visual quantification of Fe³⁺ with a practical LOD of 6.588 nM. Importantly, the hydrogel was thermally degradable at 80 °C, thus minimizing environmental impact. The sensor’s practical applicability was validated by accurately detecting Fe³⁺ in spinach and human urine samples, achieving recoveries of 98.7–108.0% with low relative standard deviations. This work provides an efficient, portable, and sustainable sensing platform that overcomes the limitations inherent in conventional analytical methods. Full article

A novel near-infrared (NIR) squaraine-based chemosensor, SQ-68, has been designed and synthesized for the sensitive and selective detection of Cu²⁺ and Ag⁺ ions, offering a compact solution for multi-analyte sensing. SQ-68 demonstrates high selectivity, with its performance influenced by the solvent environment: It selectively detects Cu²⁺ in acetonitrile and Ag⁺ in an ethanol–water mixture. Upon binding with either ion, SQ-68 undergoes significant absorption changes in the NIR region, accompanied by visible color changes, enabling naked-eye detection. Spectroscopic studies confirm a 1:1 binding stoichiometry with both Cu²⁺ and Ag⁺, accompanied by hypochromism. The detection limits are 0.09 μM for Cu²⁺ and 0.38 μM for Ag⁺, supporting highly sensitive quantification. The sensor’s practical applicability was validated in real water samples (sea, lake, and tap water), with recovery rates ranging from 73–95% for Cu²⁺ to 59–99% for Ag⁺. These results establish SQ-68 as a reliable and efficient chemosensor for environmental monitoring and water quality assessment. Its dual-analyte capability, solvent-tunable selectivity, and visual detection features make it a promising tool for rapid and accurate detection of heavy metal ions in diverse aqueous environments. Full article

Cell-free biosensors harness the selectivity of cellular machinery without living cells’ constraints, offering advantages in environmental monitoring, medical diagnostics, and biotechnological applications. This review examines recent advances in cell-free biosensor development, highlighting their ability to detect diverse analytes including heavy metals, organic pollutants, pathogens, and clinical biomarkers with high sensitivity and specificity. We analyze technological innovations in cell-free protein synthesis optimization, preservation strategies, and field deployment methods that have enhanced sensitivity, and practical applicability. The integration of synthetic biology approaches has enabled complex signal processing, multiplexed detection, and novel sensor designs including riboswitches, split reporter systems, and metabolic sensing modules. Emerging materials such as supported lipid bilayers, hydrogels, and artificial cells are expanding biosensor capabilities through microcompartmentalization and electronic integration. Despite significant progress, challenges remain in standardization, sample interference mitigation, and cost reduction. Future opportunities include smartphone integration, enhanced preservation methods, and hybrid sensing platforms. Cell-free biosensors hold particular promise for point-of-care diagnostics in resource-limited settings, environmental monitoring applications, and food safety testing, representing essential tools for addressing global challenges in healthcare, environmental protection, and biosecurity. Full article

The increasing demand for rapid, sensitive, and eco-friendly methods for the detection of trace heavy metals in environmental samples, attributed to their serious threats to health and the environment, has spurred considerable interest in the development of sustainable sensor materials. Toxic metal ions, namely, lead (Pb²⁺), cadmium (Cd²⁺), mercury (Hg²⁺), arsenic (As³⁺), and chromium, are potential hazards due to their non-biodegradable nature with high toxicity, even at trace levels. Acute health complications, including neurological, renal, and developmental disorders, arise upon exposure to such metal ions. To monitor and mitigate these toxic exposures, sensitive detection techniques are essential. Pre-existing conventional detection methods, such as atomic absorption spectroscopy (AAS) and inductively coupled plasma-mass spectrometry (ICP-MS), involve expensive instrumentation, skilled operators, and complex sample preparation. Electrochemical sensing, which is simple, portable, and eco-friendly, is foreseen as a potential alternative to the above conventional methods. Carbon-based nanomaterials play a crucial role in electrochemical sensors due to their high conductivity, stability, and the presence of surface functional groups. Biochar (BC), a carbon-rich product, has emerged as a promising electrode material for electrochemical sensing due to its high surface area, sustainability, tunable porosity, surface rich in functional groups, eco-friendliness, and negligible environmental footprint. Nevertheless, broad-spectrum studies on the use of biochar in electrochemical sensors remain narrow. This review focuses on the recent advancements in the development of biochar-based electrochemical sensors for the detection of toxic heavy metals such as Pb²⁺, Cd²⁺, and Hg²⁺ and the simultaneous detection of multiple ions, with special emphasis on BC synthesis routes, surface modification methodologies, electrode fabrication techniques, and electroanalytical performance. Finally, current challenges and future perspectives for integrating BC into next-generation sensor platforms are outlined. Full article

The widespread and persistent occurrence of micropollutants—such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)—has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic sensors, including biosensors, have gained prominence as versatile and transformative tools for real-time environmental monitoring, enabling precise and rapid detection of trace-level contaminants in complex environmental matrices. Their miniaturized design, low reagent consumption, and compatibility with portable and smartphone-assisted platforms make them particularly suited for on-site applications. Recent breakthroughs in nanomaterials, synthetic recognition elements (e.g., aptamers and molecularly imprinted polymers), and enzyme-free detection strategies have significantly enhanced the performance of these biosensors in terms of sensitivity, specificity, and multiplexing capabilities. Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into microfluidic platforms has opened new frontiers in data analysis, enabling automated signal processing, anomaly detection, and adaptive calibration for improved diagnostic accuracy and reliability. This review presents a comprehensive overview of cutting-edge microfluidic sensor technologies for micropollutant detection, emphasizing fabrication strategies, sensing mechanisms, and their application across diverse pollutant categories. We also address current challenges, such as device robustness, scalability, and potential signal interference, while highlighting emerging solutions including biodegradable substrates, modular integration, and AI-driven interpretive frameworks. Collectively, these innovations underscore the potential of microfluidic sensors to redefine environmental diagnostics and advance sustainable pollution monitoring and management strategies. Full article

We developed a novel and portable magnetic nanochannel electrochemical sensor for the sensitive detection of cadmium ions (Cd²⁺), which pose serious risks to food safety and human health. The sensor was fabricated by co-modifying an anodic aluminum oxide (AAO) nanochannel membrane with a composite of glutathione (GSH) and ferric oxide nanoparticles (Fe₃O₄), denoted as GSH@Fe₃O₄. This modified membrane was then integrated with a screen-printed carbon electrode (SPCE) to construct the GSH@Fe₃O₄/GSH@AAO/SPCE sensing platform. The performance of the sensor was evaluated using differential pulse voltammetry (DPV), which demonstrated a strong linear correlation between the peak current response and the concentration of Cd²⁺ in the range of 5–120 μg/L. The calibration equation was I_DPV(μA) = −0.31 + 0.98·C_{Cd²⁺(μg/L)}, with an excellent correlation coefficient (R² = 0.999, n = 3). The calculated limit of detection (LOD) was as low as 0.1 μg/L, indicating the high sensitivity of the system. These results confirm the successful construction of the GSH@Fe₃O₄/GSH@AAO/SPCE portable nanochannel sensor. This innovative sensing platform provides a rapid, sensitive, and user-friendly approach for the on-site monitoring of heavy metal contamination in agricultural products, especially grains. Full article

In recent years, heavy metal pollution in farmland soil has become a crisis due to human activities or natural impacts, with particular emphasis on cases from China, where this issue is prominent, greatly affecting crop production and food safety. In the context of a low heavy metal (HM) content in farmland soil, which is difficult to monitor in real time, effective and rapid monitoring of soil plays a decisive role in subsequent targeted protection measures. To this end, this paper provides a narrative review of the application of spectral sensing technology on the basis of the quantitative inversion of heavy metal content in farmland soil using different platforms (ground, airborne, and spaceborne). The sensing process evaluates the mechanism by which soil produces different weak spectral features from the perspective of the heterogeneity of farmland soil. Different methods used for the quantitative inversion of heavy metals (by studying the correlation between soil heavy metals and organic matter, clay minerals, metal oxides, crop vegetation index, etc.) and their feasibility were clarified. At the same time, relevant research on key technologies used in various processes—such as follow-up pretreatment, spectral feature extraction, and the establishment of inversion models for spectral data of different farmland soil types—was summarized, with a primary focus on cases in China. Finally, the challenges, applications, and research directions related to heavy metal spectral inversion in farmland soil were discussed. Full article

Soil heavy metal pollution threatens agricultural safety and human health, with Cd exceeding standards being the most common problem in contaminated farmland. The development of hyperspectral remote sensing technology has provided a novel methodology of quickly and non-destructively monitoring heavy metal contamination in soil. This study aims to explore the potential of an interpretable Stacking ensemble learning model for the estimation of soil Cd contamination in farmland hyperspectral data. We assume that this method can improve the modeling accuracy. We chose Zhangjiagang City, Jiangsu Province, China, as the study area. We gathered soil samples from wheat fields and analyzed soil spectral data and Cd level in the lab. First, we pre-processed the spectra utilizing fractional-order derivative (FOD) and standard normal variate (SNV) transforms to highlight the spectral features. Second, we applied the competitive adaptive reweighted sampling (CARS) feature selection algorithm to identify the significant wavelengths correlated with soil Cd content. Then, we constructed and compared the estimation accuracy of multiple machine learning models and a Stacking ensemble learning method and utilized the Optuna method for hyperparameter optimization. Ultimately, the SHAP method was used to shed light on the model’s decision-making process. The results show that (1) FOD can further highlight the spectral features, thereby strengthening the correlation between soil Cd content and wavelength; (2) the CARS algorithm extracted 3.4–6.8% of the feature wavelengths from the full spectrum, and most of them were the wavelengths with high correlation with soil Cd; (3) the optimal estimation precision was achieved using the FOD1.5-SNV spectral pre-processing combined with the Stacking model (R² = 0.77, RMSE = 0.05 mg/kg, RPD = 2.07), and the model effectively quantitatively estimated soil Cd contamination; and (4) SHAP further revealed the contribution of each base model and characteristic wavelengths in the Stacking modeling process. This research confirms the advantages of the interpretable Stacking model in hyperspectral estimation of Cd contamination in farmland wheat soil. Furthermore, it offers a foundational reference for the future implementation of quantitative and non-destructive regional monitoring of heavy metal contamination in farmland soil. Full article

Groundwater remains a critical water source for urban communities, particularly in rapidly urbanizing countries such as the Philippines. However, intensifying anthropogenic pressures have contributed to widespread contamination from heavy metals, nutrients, pathogens, volatile organic compounds (VOCs), and emerging pollutants, including pharmaceuticals and personal care products (PPCPs). This review synthesizes findings from 130 peer-reviewed studies on groundwater monitoring and remediation, emphasizing technological advancements and their application in urban environments. The literature is categorized into five thematic areas: monitoring technologies, contaminant profiles, remediation strategies, Philippine-specific case studies, and alignment with global frameworks. Recent innovations—such as Internet of Things (IoT)-enabled systems, remote sensing, biosensors, and artificial intelligence/machine-learning (AI/ML) models—show strong potential for real-time and predictive monitoring. Despite these advancements, technology adoption in the Philippines remains limited due to regulatory, technical, and infrastructural constraints. This review identifies key research and implementation gaps, particularly in the monitoring of emerging contaminants and the integration of data into policy-making and urban planning. To address these challenges, a conceptual framework is proposed to support more sustainable, technology-driven, and context-sensitive groundwater management in heavily urbanized areas. Full article

Soil heavy metal contamination poses significant risks to ecosystems and human health, necessitating accurate prediction methods for effective monitoring and remediation. We propose a multi-model and variable combination framework to improve the prediction of soil heavy metal content by integrating diverse environmental and spatial features. The methodology incorporates environmental variables (e.g., soil properties, remote sensing indices), spatial autocorrelation measures based on nearest-neighbor distances, and spatial regionalization variables derived from interpolation techniques such as ordinary kriging, inverse distance weighting, and trend surface analysis. These variables are systematically combined into six distinct sets to evaluate their predictive performance. Three advanced models—Partial Least Squares Regression, Random Forest, and a Deep Forest variant (DF21)—are employed to assess the robustness of the approach across different variable combinations. Experimental results demonstrate that the inclusion of spatial autocorrelation and regionalization variables consistently enhances prediction accuracy compared to using environmental variables alone. Furthermore, the proposed framework exhibits strong generalizability, as validated through subset analyses with reduced training data. The study highlights the importance of integrating spatial dependencies and multi-source data for reliable heavy metal prediction, offering practical insights for environmental management and policy-making. Compared to using environmental variables alone, the full framework incorporating spatial features achieved relative improvements of 18–23% in prediction accuracy (R²) across all models, with the Deep Forest variant (DF21) showing the most substantial enhancement. The findings advance the field by providing a flexible and scalable methodology adaptable to diverse geographical contexts and data availability scenarios. Full article

This study investigates the development and sensing profile of synthetic melanin nanoparticle-coated electrodes for the electrochemical detection of heavy metals, including lead (Pb), cadmium (Cd), cobalt (Co), zinc (Zn), nickel (Ni), and iron (Fe). Synthetic melanin films were prepared in situ by the deacetylation of diacetoxy indole (DAI) to dihydroxy indole (DHI), followed by the deposition of DHI monomers onto indium tin oxide (ITO) and glassy carbon electrodes (GCE) using cyclic voltammetry (CV), forming a thin layer of synthetic melanin film. The deposition process was characterized by electrochemical quartz crystal microbalance (EQCM) in combination with linear sweep voltammetry (LSV) and amperometry to determine the mass and thickness of the deposited film. Surface morphology and elemental composition were examined using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). In contrast, Fourier-transform infrared (FTIR) and UV–Vis spectroscopy confirmed the melanin’s chemical structure and its polyphenolic functional groups. Differential pulse voltammetry (DPV) and amperometry were employed to evaluate the melanin films’ electrochemical activity and sensitivity for detecting heavy metal ions. Reproducibility and repeatability were rigorously assessed, showing consistent electrochemical performance across multiple electrodes and trials. A comparative analysis of ITO, GCE, and graphite electrodes was conducted to identify the most suitable substrate for melanin film preparation, focusing on stability, electrochemical response, and metal ion sensing efficiency. Finally, the applicability of melanin-coated electrodes was tested on in-house heavy metal water samples, exploring their potential for practical environmental monitoring of toxic heavy metals. The findings highlight synthetic melanin-coated electrodes as a promising platform for sensitive and reliable detection of iron with a sensitivity of 106 nA/ppm and a limit of quantification as low as 1 ppm. Full article

Food contaminants, including harmful microbes, pesticide residues, heavy metals and illegal additives, pose significant public health risks. While traditional detection methods are effective, they are often slow and require complex equipment, which limits their application in real-time monitoring and rapid response. Surface-enhanced Raman scattering (SERS) technology has gained widespread use in related research due to its hypersensitivity, non-destructibility and molecular fingerprinting capabilities. In recent years, Au-Ag bimetallic nanoparticles (Au-Ag BNPs) have emerged as novel SERS substrates, accelerating advancements in SERS detection technology. Au-Ag BNPs can be classified into Au-Ag alloys, Au-Ag core–shells and Au-Ag aggregates, among which the Au-Ag core–shell structure is more widely applied. This review discusses the types, synthesis methods and practical applications of Au-Ag BNPs in food contaminants. The study aims to provide valuable insights into the development of new Au-Ag BNPs and their effective use in detecting common food contaminants. Additionally, this paper explores the challenges and future prospects of SERS technology based on Au-Ag BNPs for pollutant detection, including the development of functional integrated substrates, advancements in intelligent algorithms and the creation of portable on-site detection platforms. These innovations are designed to streamline the detection process and offer guidance in selecting optimal sensing methods for the on-site detection of specific pollutants. Full article

Suaeda salsa, a halophytic plant species, exhibits a remarkable salt tolerance and demonstrates a significant phytoremediation potential through its capacity to absorb and accumulate saline ions and heavy metals from soil substrates, thereby contributing to soil quality amelioration. Furthermore, this species serves as a critical habitat component for avifauna populations and represents a keystone species in maintaining ecological stability within estuarine and coastal wetland ecosystems. With the development and maturity of UAV remote sensing technology in recent years, the advantages of using UAV imagery to extract weak targets are becoming more and more obvious. In this paper, for Suaeda salsa, which is a weak target with a sparse distribution and inconspicuous features, relying on the high-resolution and spatial information-rich features of UAV imagery, we establish an adaptive contextual information extraction deep learning semantic segment model (ACI-Unet), which can solve the problem of recognizing Suaeda salsa from high-precision UAV imagery. The precise extraction of Suaeda salsa was completed in the coastal wetland area of Dongying City, Shandong Province, China. This paper achieves the following research results: (1) An Adaptive Context Information Extraction module based on large kernel convolution and an attention mechanism is designed; this module functions as a multi-scale feature extractor without altering the spatial resolution, enabling a seamless integration into diverse network architectures to enhance the context-aware feature representation. (2) The proposed ACI-Unet (Adaptive Context Information U-Net) model achieves a high-precision identification of Suaeda salsa in UAV imagery, demonstrating a robust performance across heterogeneous morphologies, densities, and scales of Suaeda salsa populations. Evaluation metrics including the accuracy, recall, F1 score, and mIou all exceed 90%. (3) Comparative experiments with state-of-the-art semantic segmentation models reveal that our framework significantly improves the extraction accuracy, particularly for low-contrast and diminutive Suaeda salsa targets. The model accurately delineates fine-grained spatial distribution patterns of Suaeda salsa, outperforming existing approaches in capturing ecologically critical structural details. Full article

Carbon quantum dots (CQDs), with their excellent photoluminescence, tunable surface chemistry, and low toxicity, have emerged as versatile nanomaterials in sensing technologies. Meanwhile, metal–organic frameworks (MOFs) possess exceptionally porous architectures and extensive surface areas, and tunable functionalities ideal for molecular recognition and analyte enrichment. The synergistic integration of CQDs and MOFs has significantly expanded the potential of hybrid materials with enhanced selectivity, sensitivity, and multifunctionality. While several reviews have addressed QD/MOF systems broadly, this review offers a focused and updated perspective on CQDs@MOFs composites specifically tailored for food safety and environmental sensing applications. This review provides a comprehensive analysis of recent advances in the design, synthesis, and surface functionalization of these hybrids, emphasizing the mechanisms of interaction, photophysical behavior, and performance advantages over conventional sensors. Special attention is given to their use in detecting food contaminants such as heavy metals, pesticides, antibiotics, mycotoxins, pathogens, and aromatic compounds. Key strategies to enhance stability, selectivity, and detection limits are highlighted, and current challenges and future directions for practical deployment are critically discussed. Full article

Detecting heavy metal ions is essential for maintaining environmental safety, ensuring industrial quality control, and protecting public health. In this study, we have synthesized a novel Rhodamine B-based fluorescent probe, RhB-DCT, which is functionalized with 2,4-dichloro-1,3,5-triazine (DCT) to enhance selectivity and sensitivity for metal ions detection. The probe functions through a “turn-on” fluorescence mechanism activated by the opening of the spiro-lactam ring induced by Fe³⁺ ions, resulting in a distinct color change from colorless to deep pink. The RhB-DCT probe demonstrated a rapid and robust fluorescence response within seconds, exhibited a broad pH stability from 4 to 13, showed excellent reversibility, and possessed a low detection limit of 0.0521 μM, surpassing numerous existing fluorescent probes. The RhB-DCT probe exhibited significant selectivity for Fe³⁺ than other competing metal ions. The integration of high sensitivity, rapid response, and strong stability positions RhB-DCT as a viable option for real-time detection of Fe³⁺ ions in aqueous settings. This study demonstrates the efficacy of the RhB-DCT probe in environmental monitoring, water quality assessment, and analytical sensing platforms, serving as an effective and dependable tool for detecting heavy metal ions. Full article