Background: Carbon Dots (CDs) offer many advantages, including tunable optical properties, excellent photostability, high aqueous solubility, low toxicity, chemical inertness, easy surface functionalization, and outstanding biocompatibility. Despite a sharp increase in publications, the field still faces several key limitations. These limitations include fragmented knowledge, insufficient integration of advanced computational strategies, contradictory structural models, and unresolved scalability challenges. These gaps prevent a systematic understanding and limit the ability to connect synthetic advancements, application breakthroughs, and cross-disciplinary knowledge dynamics. Methods: This review conducts a systematic bibliometric analysis to decode the evolving landscape of CDs research from 2020 to 2024. It examines emerging frontiers in this field and explores interdisciplinary dynamics. The researchers retrieve a dataset of 34,652 publications on CDs from the Web of Science Core Collection (WOSCC), Scopus, and ScienceDirect. They analyze the dataset using CiteSpace. This analysis identifies key contributors, including countries and institutions. It reveals collaborative networks, detects subject clusters, maps keyword co-occurrence to trace thematic shifts through timeline visualization, highlights disruptive innovations through citation bursts, and uses clustering techniques to dissect persistent debates. Results: Bibliometric data reveal a transition from foundational synthesis to functional applications. Research prioritizes quantum yield and size control through hydrothermal methods. Current trends shift toward translational utility. Evaluation indicators emerge as a key theme. These metrics standardize toxicity and efficiency assessments. Discussion: Standardization addresses scalability constraints and industrial barriers. Uniform metrics enable cross-study comparisons. Integration of toxicity benchmarks remains vital for commercialization. Evaluative frameworks bridge the gap between laboratory innovation and industrial manufacturing. Rigorous characterization resolves fragmented knowledge. Conclusion: This review elucidates the interdisciplinary evolution of CDs. It constructs a knowledge map to delineate research trajectories. The findings foster collaboration to address performance challenges. The study provides a strategic roadmap for sustainable applications.
The global increase in agri-food demand points to a strong need for rapid, sensitive, and accurate contaminant detection methods. Metal-organic frameworks (MOFs), with remarkable structural tunability, high surface areas, and well-defined porous structures, have shown great potential as fluorescent sensing platforms. In the context of fundamental emission mechanisms and key photophysical processes, this review provides a systematic survey of recent literature (mainly from 2020– 2025) on MOF-based fluorescent sensors, focusing on the intrinsic fluorescence pathways such as metal-centered, ligand-centered, and guests-induced luminescence, and further differentiates their unique fluorescence properties in solid- and wet-state. The literature suggests that both lattice rigidity and AIE effects can enhance quantum yields to near unity. Specific guest-host interactions, along with defined exciton migration pathways, enable ultra-sensitive detection at trace levels (nmol to pmol) of various contaminants, e.g., pesticides, veterinary drug residues, mycotoxins, unauthorized additives, metal ions, and indicators of food spoilage. An in-depth assessment of recent approaches, including nanoparticle integration, ligand functionalization, dual-emission (ratiometric) schemes, and smartphone-based, easily portable sensing, demonstrates significant improvements in sensitivity, selectivity, operational stability, and real-world feasibility. Nevertheless, issues such as the stability of MOFs in complex environments, sensor multi-functionality, scalability, and cost-effectiveness still need to be addressed. Novel methodologies combining AI/ML methods are emerging and hold great potential, providing pathways to adaptive, self-calibrating, and multiplexed MOF-based sensing systems. Taken together, this review summarizes key lessons from recent work that demonstrate both exciting advances and challenges, and outlines a perspective on the development and application of MOF-based fluorescent sensors that can have a transformative impact on agri-food safety, public health, and environmental sustainability.
Objective: To develop and validate a robust ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for the simultaneous quantification of polymyxins (PMXs) B1, B2, E1, and E2 in human plasma. Methods: Human plasma samples were obtained from blood specimens provided by the hospital Department of Laboratory Medicine after written informed consent. PMX B1, B2, E1, and E2 were determined by UPLC-MS/MS with isotope-labeled Internal Standards (ISs). Plasma, calibrator, or Quality Control (QC) sample (50 μL) was mixed with 150 μL IS-containing precipitant, vortexed, centrifuged, diluted two-fold, and injected at 5 μL. Separation was performed on a C18 column using acetonitrile and 0.1% formic acid-water, followed by positive electrospray ionization mode detection. The method was validated for selectivity, carry-over, lower limit of quantitation (LLOQ), linearity, accuracy, precision, stability, matrix effect, and recovery. Results: The method exhibited good linearity (r≥0.9978) for all analytes, acceptable LLOQ performance (Coefficient of Variation (CV) 3.47%-7.11%, bias -7.29%-3.74%), and reliable QC accuracy/ precision (bias ±9.33%, CV ≤11.31%). Analytes and IS were stable (deviation within ±15%), with mild matrix effects (98.1%-103.8%) and high extraction recoveries (93.2%-95.8%). Discussion: Isotope-labeled IS effectively reduces variation during sample preparation and instrumental analysis, thereby ensuring accurate quantification. The linearity, LLOQ performance, and stability of this method complied with international bioanalytical guidelines. Its mild matrix effects and high extraction recoveries proved strong anti-interference capacity against plasma matrices, making this approach suitable for routine clinical testing and scientific research. Conclusion: The developed UPLC-MS/MS method is robust, accurate, and precise, suitable for the simultaneous quantification of PMX B1, B2, E1, and E2 in human plasma for bioanalytical applications.
Introduction/Objective: Macro- and microplastic contamination in food and pharmaceutical products requires the rapid identification of contaminant materials, typically via FTIR spectroscopy. This study introduces SPRIT (Spectral Pattern Recognition and Interpretation Tool), an OCR (Optical Character Recognition)-based tool that utilises previously generated laboratory spectral images for contaminant identification. Methods: SPRIT extracts annotated peak positions and calculates relative intensities directly from spectral images. The custom pipeline employs 2x scaling and Otsu’s binarisation to optimise Tesseract OCR accuracy. The extracted spectral features are then compared against a reference library of validated FTIR spectra using a transparent, rule-based pattern-matching algorithm. Results: The reference library creation demonstrated a high image-text extraction success rate of 98.76%. Validation studies demonstrated the correct identification of pure polymer test samples and the successful detection of both constituent polymers in 9 of 10 blend samples. It successfully reduces the analysis time from minutes to seconds. Discussion: Unlike opaque deep learning models, SPRIT mimics expert spectral interpretation and generates a transparent, peak-by-peak comparison report. It functions as a readily deployable decision- support system that does not require massive training datasets or high-performance computing resources. Conclusion: SPRIT is a resource-efficient, audit-friendly tool that relies on an OCR framework to extract spectral pattern data for sample analysis. It addresses key bottlenecks in product quality control and forensic laboratories by providing fast results and consuming fewer resources.
Background Photonic crystals (PC) change light's phase and angle through periodic refractive index structures. PC with Dense Wavelength Division Multiplexing (DWDM) are used as filters, multiplexers, and demultiplexers. The existing optical sensing system has drawbacks for sensing ethanol in the liquid phase, including limited tunability, selectivity, and integration.Methods A T-Shaped Photonic Crystal Narrowband Filter (TS-Phc)-based sensor is proposed with a two-dimensional square-lattice photonic crystal structure. The TS-Phc sensor has air holes with a radius of 0.112 mu m, etched in silica with a lattice constant of 550 nm, on a 13 & times; 12 mu m wafer. A line defect is introduced to form bus and drop waveguides in a T-shaped configuration. Point defects are added to improve selectivity. The design was optimized using the two-dimensional Finite-Difference Time-Domain (FDTD) method. A narrowband filter is used in TS-Phc for liquid ethanol concentration detection using DWDM. A Near-infrared (NIR) diode (OD-800L) is used as a light source. The OD-800L near-infrared diode is used as an independent proof-of-concept illumination source for optical signal acquisition and preprocessing studies. The 800 nm source does not directly excite the 1541-1556 nm photonic resonance mode. The optical signal passing through a liquid ethanol sample in a test tube and the photonic sensor was acquired by an APT 0800-3111-111 detector through the DAQ system. Detector signals are pre-processed using Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), and Transverse Dyadic Wavelet Transform (TyDWT). Sub-band energy was extracted from the acquired signal using Tunable Q-factor Wavelet Transform (TQWT) and Rational Dilation Wavelet Transform (RADWT). Furthermore, Bayesian optimization is used to predict ethanol concentration.Results and Discussion The TS-Phc sensor achieves accurate parts per million (PPM (mg/L)) prediction through Bayesian-optimized multiple regression and is verified through spectroscopic analysis. Bayesian-optimized multiple regression has strong agreement between the predicted and reference ethanol concentrations, achieving R- square value of 0.965.Conclusion The proposed TS-Phc sensor provides a solution for detecting ethanol PPM (mg/L) levels and attained 96.5% accuracy. TS-Phc sensor system is suitable for environmental monitoring and biomedical diagnosis.
