Imran Ali

Microplastic contamination has become an emerging environmental concern in marine ecosystems worldwide. This study investigated the occurrence, abundance, size, color, and polymer composition of microplastics in surface water, marine sediments, and the gastrointestinal tracts of commercially important fish species collected from the coastal regions of Sousa and Alhaniyah along the northeastern Mediterranean coast of Libya. Microplastics were detected in 10 of 15 (66.7%) surface water samples from Sousa and in 5 of 15 (33.3%) samples from Alhaniyah. In bed sediments, microplastics were detected in 5 of 15 samples (33.3%) at both study sites. Microplastics were detected in 46.73% (71/149) of the fish specimens collected from Sousa and in 20.30% (25/123) of those collected from Alhaniyah. The mean microplastic abundance in surface water was 2.5 ± 2.6 particles/L in Sousa and 0.7 ± 1.3 particles/L in Alhaniyah. Mean sediment abundances were 254 ± 57 and 212 ± 23 particles/kg dry weight, respectively. These findings indicate microplastic contamination at both study sites, with spatial variation in abundance. Microplastic abundance in fish gastrointestinal tracts varied among species, with the highest mean values observed in Epinephelus marginatus (3.0 ± 1.6 particles/fish), followed by Alepes djedaba (2.7 ± 1.2 particles/fish) and Pagrus pagrus (2.3 ± 0.6 particles/fish). This variability revealed the species-specific exposure associated with the feeding behavior and habitat use. The microplastic particles observed were in sizes from 1.0 to 5.0 mm, with larger particles more prevalent in the sediments and smaller particles in fish. Nine different microplastic colors were seen, with blue being the most common, followed by black, green, white, transparent, yellow, glossy, red, and brown. Rayon, alkyd resin, polyamide, and poly(acrylonitrile) were identified at both study sites, whereas poly (ethylene terephthalate) (PET) was detected only at Alhaniyah. The overall similarity in polymer composition suggests that the two locations share common sources of microplastic contamination, while the occurrence of PET exclusively at Alhaniyah may indicate additional local inputs. However, further investigations are required to identify and confirm the specific sources of these polymers. This study provides the first baseline assessment of microplastic contamination in the coastal environments of Alhaniyah and Sousa, northeastern Libya. It offers a valuable reference for future monitoring and mitigation strategies.

1. Cancer is one of the foremost causes of mortality globally, requiring an advanced search for new and effective therapeutic agents [1–9]. The imidazole-based ionic-liquid-related salts (ILRSs) ha...

The evaluation of analytical methods has evolved beyond traditional validation parameters to encompass environmental sustainability, analytical reliability, and practical applicability. While numerous assessment tools exist for individual dimensions, integrated multicolor frameworks often impose rigid parameter selection, assign equal weights to all dimensions regardless of analytical context, or lack the granular diagnostic feedback necessary for method improvement. To address these limitations, we present the Triple-Color Assessment (TriCA) tool, a web-based platform that integrates three validated metrics into a unified scoring system with a hierarchical weighting scheme. TriCA combines the Analytical Green Star Area (AGSA) for greenness assessment, the Click Analytical Chemistry Index (CACI) for blueness evaluation, and the Analytical Method Reliability Index (AMRI) for redness scoring. While these metrics are provided as defaults, users may select alternative assessment tools within each dimension according to their specific preferences or requirements. TriCA features a user-customizable weighting system with recommended defaults (red = 3, blue = 2, green = 1), reflecting the logical priority that a method must first be valid, then practical, and finally green. Users can adjust these weights for specific contexts, while the underlying metric scoring protocols (AGSA, CACI, and AMRI) remain fixed to ensure reproducibility. Beyond composite scoring, TriCA generates three detailed pictograms, 12 segments for AGSA, eight segments for CACI, and 10 segments for AMRI, each color-coded to show performance at the individual criterion level. This diagnostic capability enables analysts to identify exactly which green chemistry principles, practicality criteria, or validation parameters are deficient, transforming assessment from an opaque scoring exercise into actionable guidance for method improvement. Seven case studies encompassing diverse analytical techniques demonstrate TriCA’s discriminative power and diagnostic utility. The tool is freely accessible at bit.ly/TriCA, offering the analytical community a transparent, reproducible, and diagnostically rich framework for method evaluation, development, and optimization.

In stereochemistry and drug design, the absolute configuration (AC) is one of the most important aspects. This is the exact three-dimensional arrangement of atoms around a stereogenic center in a m...

This study evaluated physicochemical changes in edible oils during repeated frying and storage using a hybrid analytical framework integrating experimental analysis with artificial intelligence (AI)-based predictive modelling. Eleven edible oils were analysed for peroxide value, iodine value, acid value, free fatty acids, viscosity, refractive index, moisture content, and specific gravity using standard methods. Repeated frying significantly increased peroxide value, acid value, free fatty acids, and viscosity, indicating oxidative and hydrolytic degradation, while iodine value decreased due to thermal oxidation of unsaturated fatty acids. Storage conditions affected oil stability; oils in transparent plastic bottles under light showed greater oxidative deterioration than those in dark glass containers. To predict oil degradation, random forest regression was benchmarked against multiple linear regression, support vector regression, and gradient boosting. Random forest achieved the best predictive performance (coefficient of determination R2 = 0.92, root mean square error RMSE = 0.52), with peroxide value combined with frying cycle number (34 %), acid value with free fatty acids (26 %), and viscosity with specific gravity (18 %) as primary predictor groups. These findings demonstrate the practical utility of AI-assisted predictive monitoring for rapid edible oil quality assessment.

The industrial progress in oil and gas, petrochemical, pharmaceutical, and food processing sectors has resulted in the discharge of oily wastewater into the environment.[1–3] The direct discharge o...

This article describes electroanalytical methods developed over the last 25 years for kaempferol (an important molecule) detection, with a main emphasis on voltammetric techniques like cyclic voltammetry, differential pulse voltammetry, and square wave voltammetry. Extensive research describes how electrode surface alterations (using metal nanoparticles, carbon nanomaterials, metal–organic frameworks, and conductive polymers) enhance electron transfer kinetics and analytical performance. These nanostructured devices enabled low‐level detection limits, i.e., nano‐ to picomolar concentrations, even in complex biological and environmental matrices, foods, and pharmaceuticals. Despite these improvements, challenges like electrode fouling, matrix interference, overlapping oxidation signals from structurally similar flavonoids, and low portability continue to be barriers to real‐world sample applications. Furthermore, this review highlights emergent solutions, comprising miniaturized and paper‐based devices, molecularly imprinted sensors, and smartphone‐integrated platforms. Special attention is given to the rising role of artificial intelligence and machine learning in electrochemical sensing, where chemometric and deep‐learning methods advance the signal processing, solve overlapping peaks, decrease human‐induced errors, and permit multi‐analyte analyses. Briefly, this review proves that the combination of nanotechnology and artificial intelligence is capable of transforming the electrochemical kaempferol determination from laboratory methods to portable, robust, and intelligent sensing systems for clinical diagnosis, food safety, and pharmaceutical analyses. © 2026 Society of Chemical Industry (SCI).

The study investigates the capillary electrophoretic enantioseparation of agrochemicals (dichloroprop and mecoprop) using modified cyclodextrins 2‐hydroxypropyl‐β‐cyclodextrin (CD‐1), heptakis (2,3‐di‐O‐methyl)‐β‐cyclodextrin (CD‐2), heptakis (2,3‐di‐O‐methyl‐6‐sulfo)‐β‐cyclodextrin (CD‐3), and heptakis (2,3,6‐tri‐O‐methyl)‐β‐cyclodextrin (CD‐4) in 2‐(N‐morpholino)ethanesulfonic acid (MES) buffer. Migration times varied depending on the selector type and inclusion complex type and stability, reflecting differences in enantiomer–selector interaction strengths. The reversal of enantiomer migration order observed among the cyclodextrins suggested that even small structural differences in the chiral selectors influenced changes in intermolecular interactions and complex stability. This reversal order is useful for understanding the mechanisms. The separations were due to inclusion complexes with neutral cyclodextrins, while electrostatic interactions were responsible for negatively charged cyclodextrins. Docking and ROESY‐NMR studies were used to provide supportive structural insight into the nature of chiral recognition mechanisms. The docking provided general structural information about the potential binding modes of these systems. However, native β‐cyclodextrin was used for the ROESY‐NMR studies because it produced very clear spectra, allowing the comparison of the basic interaction patterns among the various β‐cyclodextrin derivatives. Overall, the results demonstrated that capillary electrophoresis (CE) is a sensitive technique for investigating intermolecular interactions and gaining insight into enantioselective processes.