Search Results (527)

Metal–organic frameworks (MOFs) have emerged as highly versatile materials for the development of next-generation optical biosensors owing to their tunable porosity, large surface area, and customizable chemical functionality. Recently, MOF-based platforms have shown substantial potential in various optical transduction modalities, including fluorescence, luminescence, and colorimetric sensing, enabling the highly sensitive and selective detection of biological analytes. This review provides a comprehensive overview of recent advancements in MOF-based optical biosensors, focusing on their applications in pathogen detection and environmental monitoring. We highlight key design strategies, including MOF functionalization, hybridization with nanoparticles or dyes, and integration into microfluidic and wearable devices. Emerging methods, such as point-of-care diagnostics, label-free detection, and real-time monitoring, are also discussed. Finally, the current challenges and future directions for the practical deployment of MOF-based optical biosensors in clinical and field environments are discussed. Full article

The colorimetric food freshness indicator (CFFI) is a promising technology in intelligent food packaging, offering the capability for real-time monitoring of food freshness through colorimetric changes. This technology holds significant promise in mitigating food waste and enhancing transparency across the supply chain. This paper provides a comprehensive review of the classification system for the CFFI, encompassing colorimetric films and sensor arrays. It explores their applications across key perishable food categories, including meats, seafoods, fruits, and vegetables. Furthermore, this paper offers an in-depth analysis of three critical challenges currently hindering technological advancement: safety concerns, stability issues, and limitations in sensitivity and selectivity. In addressing these challenges, this paper proposes forward-looking solutions and outlines potential research directions aimed at overcoming these bottlenecks, thereby fostering substantial progress in the development of this field. Full article

Effective water quality monitoring is very important for detecting pollution and protecting public health. However, traditional methods are slow, relying on costly equipment, central laboratories, and expert staffing, which delays real-time measurements. At the same time, significant advancements have been made in the field of plasmonic sensing technologies, making them ideal for environmental monitoring. However, their reliance on large, expensive spectrometers limits accessibility. This work aims to bridge the gap between advanced plasmonic sensing and practical water monitoring needs, by integrating plasmonic sensors with mobile technology. We present BioColor, a mobile platform that consists of a plasmonic sensor setup, mobile application, and cloud services. The platform processes captured colorimetric sensor images in real-time using optimized image processing algorithms, including region-of-interest segmentation, color extraction (mean and dominant), and comparison via the CIEDE2000 metric. The results are visualized within the mobile app, providing instant and automated access to the sensing outcome. In our validation experiments, the system consistently measured color differences in various sensor images captured under media with different refractive indices. A user experience test with 12 participants demonstrated excellent usability, resulting in a System Usability Scale (SUS) score of 93. The BioColor platform brings advanced sensing capabilities from hardware into software, making environmental monitoring more accessible, efficient, and continuous. Full article

Optical sensors have emerged as a popular technology for sensing biological and chemical analytes in various fields, including environmental monitoring, toxicology, disease/infection screening, and food processing, due to their ease of use, high sensitivity, and specificity. In this study, we introduce ColorX, an ultra-portable and smart spectrophotometric device based on a commercially available fitness tracker. ColorX exploits the in-built LEDs and photodiodes of a fitness tracker for wavelength-specific absorption measurements and can be controlled wirelessly using a companion smartphone app. The device’s raw data are transmitted via Bluetooth and stored on the app for analysis and data visualisation. We validated the performance of ColorX against a standard benchtop spectrophotometer by experimentally testing five different measurements related to water quality: nitrite (>0.07 mg/L, %avgCV: 1.06)), sulphate (>18 mg/L, %avgCV: 0.39), chromium (>0.002 mg/L, %avgCV: 0.51), free chlorine (>0.005 mg/L, %avgCV: 0.68), and turbidity (>2.97 NTU, %avgCV: 1.04). Our results showed that ColorX had comparable performance to the benchmark spectrophotometer (R² values > 0.9 in all cases). Due to its ultra-portability, water-proof design, wireless control, and smartphone-aided data analysis, we believe ColorX will be highly beneficial for a wide range of on-field spectrophotometric applications. Our work demonstrates the potential of frugal science to develop affordable and accessible technology for optical sensing. Full article

Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform—using CA19-9 as a model biomarker—that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications. Full article

The detection of glucose concentration has a wide range of applications and plays a significant role in the fields of the food industry, medical health, and illness diagnostics. The utilization of sensor technology for glucose concentration detection is an effective approach. Glucose sensors utilizing nanomaterials, with high sensitivity, strong resistance to interference, and compact size, exhibit tremendous potential in glucose concentration detection. Traditional enzyme-based sensors exhibit superior selectivity and high sensitivity; however, they are deficient in terms of interference resistance capabilities. With the development of nanotechnology, the performance of glucose sensors has been significantly improved. This review discusses the research progress in non-enzymatic electrochemical glucose nanosensors, including noble metal-based glucose sensors and non-noble transition metal compound-based glucose sensors, as well as the applications of multimetallic materials in nanosensors. Additionally, the application of nanosensors based on fluorescence and colorimetric principles in the detection of glucose concentration is introduced in this review. Finally, a perspective on the challenges and prospects of nanosensors in the field of glucose detection is presented. Full article

Blood iron levels are related to many health conditions, affecting hundreds of millions of individuals worldwide. To aid in the prevention and treatment of iron-related disorders, previous research has developed a low-cost, accurate, point-of-care method for measuring iron from a single finger-prick blood sample. This study builds upon that work by introducing an improved imaging method that accurately reads sensor images irrespective of variations in environmental illumination and camera quality. Smartphone cameras were used as analytical tools, demonstrating an average coefficient of variation of 5.13% across different phone models, and absorbance results were found to be improved by 8.80% compared to the method in a previous study. The proposed method successfully enhances iron detection accuracy under diverse lighting conditions, paving the way for smartphone-based sensing of other colorimetric reactions involving various analytes. Full article

Narcotics trafficking is a fundamental part of organized crime, posing significant and evolving challenges for forensic investigations. Addressing these challenges requires rapid, precise, and scientifically validated analytical methods for reliable identification of illicit substances. Over the past five years, forensic drug testing has advanced considerably, improving detection of traditional drugs—such as tetrahydrocannabinol, cocaine, heroin, amphetamine-type stimulants, and lysergic acid diethylamide—as well as emerging new psychoactive substances (NPS), including synthetic cannabinoids (e.g., 5F-MDMB-PICA), cathinones (e.g., α-PVP), potent opioids (e.g., carfentanil), designer psychedelics (e.g., 25I-NBOMe), benzodiazepines (e.g., flualprazolam), and dissociatives (e.g., 3-HO-PCP). Current technologies include colorimetric assays, ambient ionization mass spectrometry, and chromatographic methods coupled with various detectors, all enhancing accuracy and precision. Vibrational spectroscopy techniques, like Raman and Fourier transform infrared spectroscopy, have become essential for non-destructive identification. Additionally, new sensors with disposable electrodes and miniaturized transducers allow ultrasensitive on-site detection of drugs and metabolites. Advanced chemometric algorithms extract maximum information from complex data, enabling faster and more reliable identifications. An important emerging trend is the adoption of green analytical methods—including direct analysis, solvent-free extraction, miniaturized instruments, and eco-friendly chromatographic processes—that reduce environmental impact without sacrificing performance. This review provides a comprehensive overview of innovations over the last five years in forensic drug analysis based on the ScienceDirect database and highlights technological trends shaping the future of forensic toxicology. Full article

The prosperity of enzyme-mimicking catalysis has promoted the development of nanozymes with diversified activities, mainly including catalase-like, oxidase-like, peroxidase-like, and superoxide dismutase-like characteristics. Thus far, the reported nanozymes can be roughly divided into five categories, comprising noble metals, metal oxides, carbon-based nanostructures, metal–organic frameworks, and covalent organic frameworks. This review systematically summarizes the research progress of nanozymes for improving catalytic activity toward sensing applications in food safety monitoring. Specifically, we highlight the unique advantages of nanozymes in enhancing the performance of colorimetric, fluorescence, and electrochemical sensors, which are crucial for detecting various food contaminants. Moreover, this review addresses the challenges faced in food safety detection, such as the need for high sensitivity, selectivity, and stability under complex food matrices. Nanozymes offer promising solutions by providing robust catalytic activity, adjustable enzyme-like properties, and excellent stability, even in harsh environments. However, practical implementation challenges remain, including the need for a deeper understanding of nanozyme catalytic mechanisms, improving substrate selectivity, and ensuring long-term stability and large-scale production. By focusing on these aspects, this review aims to provide a comprehensive overview of the current state of nanozyme-based sensors for food safety detection and to inspire future research directions. Full article

The visual colorimetric sensing of total volatile basic nitrogen (TVB-N) allows for convenient dynamic monitoring of animal-derived food freshness to ensure food safety. The agarose hydrogel loaded with the natural dye juglone (Jug@AG) prepared in this study exhibits visible multicolor changes from yellow to grayish-yellow and then to brownish with increasing TVB-N gas concentration, achieving sensitive detection of TVB-N gas at concentrations as low as 0.05 mg/dm³ within 8 min. The minimum observable amounts of TVB-N in spiked pork and fish samples are 8.43 mg/100 g and 8.27 mg/100 g, respectively, indicating that the Jug@AG hydrogel possesses sensitive colorimetric sensing capability in practical applications. The Jug@AG hydrogel also shows significant changes in color difference value (∆C) under both room temperature (25 °C) and cold storage (4 °C) conditions, with the changing trends of ∆C showing consistency with the measured TVB-N and total viable counts (TVC) during the transition of pork and fish samples from freshness to early spoilage and then to spoilage. The results indicate that the Jug@AG hydrogel can be used as a colorimetric sensor to achieve real-time dynamic freshness monitoring of animal-derived food. Full article

Hypochlorous acid (HClO) serves as a biological mediator and is widely utilized as a disinfectant in food processing and water treatment. However, excessive HClO residues in food and environmental water raise concerns due to the potential formation of carcinogenic chlorinated byproducts and disinfection byproducts (DBPs). Despite its importance, traditional methods for HClO detection often involve complex sample preparation, sophisticated instrumentation, and skilled operators. Herein, we report an aggregation-induced emission (AIE) small molecule fluorescent probe (NYV) that integrates colorimetric and ratiometric fluorescence responses for the detection of HClO. This probe exhibits high sensitivity, with a detection limit of 0.35 $\mathsf{\mu }$M, a rapid response time of 1 min, and a wide linear range (0–142.5 $\mathsf{\mu }$M), along with anti-interference capabilities, making it suitable for real-time monitoring. Furthermore, we have developed a portable solid-state sensor based on probe NYV for the rapid visual detection of HClO. The potential applications of this probe in real sample analysis and bioimaging experiments are demonstrated. Our findings contribute to the development of innovative fluorescent probes for HClO detection, with broad applications in food safety, environmental monitoring, and biomedical research on oxidative stress and ferroptosis. Full article

Aflatoxin B₁ (AFB₁) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB₁ using color-sensitive arrays (CSAs). Twenty self-developed CSAs were employed to react with samples, with reflectance spectra collected using a portable spectrometer. Spectral data were optimized through seven preprocessing methods, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), first-order derivative (1st D), second-order derivative (2nd D), wavelet denoising, and their combinations. Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). The results demonstrated significant AFB₁-responsive characteristics in three dyes: (2,3,7,8,12,13,17,18-octaethylporphynato)chloromanganese(III) (Mn(OEP)Cl), Bromocresol Green, and Cresol Red. The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient ($R_p^2$ = 0.87), root mean square error (RMSEP = 0.057), and relative prediction deviation (RPD = 2.773). This method provides an efficient solution for silage AFB₁ monitoring. Full article

Ketones, which are key biomarkers of fat oxidation, are relevant for metabolic health maintenance and disease development, making continuous monitoring essential. In this study, we introduce a novel colorimetric sensor designed for potential continuous acetone detection in biological fluids. The sensor features a polydimethylsiloxane (PDMS) shell that encapsulates a sensitive and specific liquid-core acetone-sensing probe. The microsphere sensors were characterized by evaluating their size, PDMS shell thickness, colorimetric response, and sensitivity under realistic conditions, including 100% relative humidity (RH) and CO₂ interference. The microsphere size and sensor sensitivity can be controlled by modifying the fabrication parameters. Critically, the sensor showed high selectivity for acetone detection, with negligible interference from CO₂ concentrations up to 4%. In addition, the sensor displayed good reproducibility (CV < 5%) and stability under realistic storage conditions (over two weeks at 4 °C). Finally, the accuracy of the microsphere sensor was validated against a gold standard gas chromatography-mass spectrometry (GC-MS) method using simulated and real breath samples from healthy individuals and type 1 diabetes patients. The correlation between the microsphere sensor and GC-MS produced a linear fit with a slope of 0.948 and an adjusted R-squared value of 0.954. Therefore, the liquid-core microsphere-based sensor is a promising platform for acetone body fluid analysis. Full article

Eugenol-based azo dyes illustrate how bio-sourced compounds like eugenol can be transformed through synthetic processes into functional and colorful compounds. The main purpose of the present work was to develop new responsive colorimetric sensors for metal cations based on eugenol-derived azo compounds. The incorporation of the azo group into the eugenol framework allows for strong electronic interactions with metal cations, leading to distinct color changes observable to the naked eye. These azo-eugenol dyes exhibit shifts in their UV-Vis absorption spectra upon complexation with metal cations such as copper (Cu²⁺) and lead (Pb²⁺), making them effective sensors for environmental and analytical applications. The eugenol-based azo dyes were subjected to photophysical studies to understand selectivity, response time, and stability in relation to metal cations, which will be a starting point for the monitoring of toxic metal contaminants in aqueous environments. Full article

The COVID-19 pandemic highlighted the need for rapid, cost-effective tools to monitor transmission and immune response. We developed two novel paper-based colorimetric biosensors using glutaraldehyde as a protein dye—its first use in this context. Glutaraldehyde reacts with amino groups to generate a brown color, enabling detection of SARS-CoV-2 antibodies. Wathman filter paper was functionalized with (3-aminopropyl)triethoxysilane (APTES) to immobilize virus-like particles (VLPs) and nucleocapsid protein (N-protein) as biorecognition elements. Upon incubation with antibody-containing samples, glutaraldehyde enabled colorimetric detection using RGB analysis in ImageJ software. Both sensors showed a linear correlation between antibody concentration and RGB values in buffer and serum. The VLP sensor responded linearly within the range of 1.0–20 µg/mL (green coordinate) in 500-fold diluted serum and the N-protein sensor from 1.0–40 µg/mL (blue coordinate) in 250-fold diluted serum. Both sensors demonstrated good selectivity, with glucose causing up to 18% interference. These biosensors represent a paradigm shift, as they provide a sensitive, user-friendly, and cost-effective option for semi-quantitative serological analysis. Furthermore, their versatility goes beyond the detection of SARS-CoV-2 antibodies and suggests broader applicability for various molecular targets. Full article