Lady Doak College

Background This work explores the design of a ternary heterojunction photocatalyst, g-C3N4/TiO2/ZrO2, synthesized with varying proportions of graphitic carbon nitride (TZ1-TZ4). The motivation was to improve visible-light absorption and photocatalytic efficiency for wastewater treatment, particularly targeting the degradation of organic dyes under sunlight. By integrating TiO2, ZrO2, and g-C3N4 through interface engineering, the study aimed to create a durable and environmentally friendly photocatalyst. Methods The composite was fabricated using a chemical wet method, producing nanoparticles uniformly anchored on g-C3N4 nanosheets. Structural, morphological, and optical properties were characterized using XRD, XPS, FESEM, HRTEM, and UV-Vis spectroscopy. XRD confirmed anatase TiO2, monoclinic ZrO2, and graphitic g-C3N4 phases, with an average particle size of similar to 27 nm. Bandgap analysis revealed a decrease from 3.2 eV (TZ1) to 2.9 eV (TZ4). Photocatalytic activity was evaluated by degrading crystal violet dye under sunlight for 180 min, while radical-scavenging experiments identified hydroxyl radicals and photogenerated holes as the main reactive species. Significant findings The ternary heterojunction exhibited enhanced photocatalytic performance, with TZ4 achieving the highest efficiency of 79% due to increased g-C3N4 content and improved visible-light absorption. Radical-scavenging studies confirmed hydroxyl radicals and holes as the dominant contributors to dye degradation. After three cycles, the catalyst retained most of its activity, demonstrating good stability. Overall, the TiO2-ZrO2/g-C3N4 heterojunction proved to be an efficient, durable, and eco-friendly photocatalyst suitable for wastewater treatment applications.

In this study, a few-layered molybdenum disulfide (MoS2) and tungsten disulfide (WS2) nanosheets (NSs) were synthesized through a facile liquid-phase exfoliation route using N, N-dimethylformamide (DMF) as an exfoliating solvent. This work introduces a solvent-driven stabilization, where electrostatic interactions between DMF carbonyl groups and Mo/W atoms as revealed by C = O red-shift (23/9 cm⁻1) in FTIR studies, prevented agglomeration, ensuring a long-term colloidal stability (> 6-months) of the NSs, a key advancement over conventional exfoliation methods. Exfoliated NSs show characteristic excitonic blue shifts and trap-state photoluminescence (PL) in optical spectra, evidencing few-layer structures with improved charge separation. Structural and morphological characterizations substantiated the formation of few-layer NSs with reduced crystallinity and thickness. Wide band gaps (2.25/1.85 eV), PL trap states, and negative zeta potentials enable > 98

A novel solution-mixing method was proposed to synthesize Co3O4/graphene nanocomposites (Co3O4@Gr) using a green tea leaf (Camellia sinensis) extract as the reducing agent. XRD analysis shows that the as-prepared Co3O4@Gr exhibits a cubic spinel crystal structure. From morphological analysis, the obtained Co3O4 NS forms spherical clusters that are uniformly distributed on the graphene surface. FT-IR and Raman analyses confirmed the strong molecular and vibrational interactions between the Co3O4 NS and Gr. The suppressed PL intensity peak of the Co3O4@Gr NCs indicated significant inhibition in the recombination of charge carriers between the hybrid orbitals within the composites. As a result, the catalytic efficiency of Co3O4@Gr NCs increased to 80% compared to pristine Co3O4, which exhibited only 45% efficiency against methylene blue (MB) dye. Moreover, the as-prepared NCs exhibited a detection limit of 0.01-224 μM, demonstrating a superior low-DPA detection with high sensitivity. The Co3O4@Gr/GCE exhibits admirable selectivity for various pesticides, fungicides, and metal ions, with outstanding reproducibility and stability. From electrochemical investigations, the highest specific capacitance values of the as-synthesized Co3O4@Gr were 349 F/g at a scan rate of 5 mV/s and 158 F/g at a current density of 1 A/g.

Inclusive costume design involves complex interactions between anthropometric diversity, garment structure, and creative intent, making fit optimization a challenging and largely expert-driven process. While recent Artificial Intelligence (AI) approaches offer automation, they often lack interpretability and fail to support designer agency. To address this gap, this paper presents HIL-DTFit (Human-in-theLoop Decision Tree-based Fit Optimization), an Augmented Intelligence framework that integrates interpretable Machine Learning (ML) with continuous designer involvement to support inclusive fit decision-making in costume design. The proposed framework models fit suitability using Decision Tree Regression (DTR) trained on structured anthropometric and garment features derived from traditional costume construction practices. A human-in-the-loop mechanism enables designers to review, adjust, and iteratively refine model predictions, allowing expert judgment to be embedded directly into the learning process. This collaborative design ensures transparency, adaptability, and ethical alignment, particularly when addressing non-standard body types and performance-specific requirements. The effectiveness of HIL-DTFit is evaluated using standard regression metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination $(\mathrm{R}^{2}$), to assess prediction accuracy, robustness, and explanatory power. Experimental analysis demonstrates that incorporating human feedback enhances model reliability while preserving computational simplicity. The study highlights the suitability of augmented intelligence approaches for human-centered fashion and costume design, emphasizing collaboration over automation for inclusive and practical fit optimization.

Earthworms, particularly Eisenia fetida, are key bioindicator organisms for testing the toxicity of chemicals in soils. Chlorpyrifos, a widely used contact organophosphate insecticide, is applied against numerous insect pests of crops and vegetables. This study focused on the effects of Chlorpyrifos toxicity on E. fetida behaviour, survival and composting efficiency by determining the LC50 (Lethal Concentration 50) value, and evaluating the residual effect of Chlorpyrifos in cauliflower waste and on E. fetida using HPLC. The methodology involved a standard 14-day acute toxicity tests and a 30-day composting efficiency test, in accordance with the OECD guidelines. The lethal concentration determined using Probit analysis yielded a critical LC50 value of 0.434 mg/kg. Surviving earthworms consistently displayed weight loss and changes in body coloration and their activity became slow as days passed by. In the composting efficiency test using pre-digested cauliflower waste, 100% mortality occurred rapidly, within two to three minutes of introduction. Earthworms exhibited severe shock, agitated and restless movement, coiling, curling, and bulging/swelling of the clitellum and body. High-Performance Liquid Chromatography (HPLC) analysis confirmed the presence of residual Chlorpyrifos in the cauliflower waste samples and the tissue samples of the earthworm. The highest Chlorpyrifos concentration was detected in fresh cauliflower waste (5,837 mg/L at 218 nm). Dead earthworm tissues exposed to high Chlorpyrifos mixed soil (0.5 mg/kg) accumulated high levels, up to 2,443 mg/L at 218 nm and 2,711 mg/L at 250 nm.