Uva Wellassa University

Ocean acidification (OA) and nutrient enrichment can separately or together threaten coral reefs by reducing calcification efficiency and increasing physiological stress, ultimately weakening reef resilience. Therefore, the study evaluates the prevailing OA level over the Sri Lankan coral reef areas using the aragonite saturation state (ΩAr) and assesses the nitrate (NO3−), and phosphate (PO43−) concentrations over the coral sites. The study was conducted on coral reefs on the eastern coast (EC), southern coast (SC), northern coast (NC), and west coast (WC) of Sri Lanka from April to June 2024. A total of 63 seawater samples were collected around each coastal site for analysis. The ΩAr were supersaturated (ΩAr > 1) and ranged from 2.98 ± 0.04 to 4.92 ± 0.12. Throughout the study period, the study sites had ΩAr values exceeding 2.92 ± 0.16, indicating that the nation's corals were resilient to deterioration, and the comparative analysis demonstrates that these sites were not vulnerable to OA. However, the NC exhibited significantly (P < 0.05) the lowest ΩAr values (3.2 ± 0.64), positioning the regions near the lower bound of optimal calcification conditions. While ΩAr values indicate low OA stress during sampling, elevated NO3− concentrations (2 – 5 μmol L−1) in SC (2.19 ± 1.28 µmol L−1) and WC (3.52 ± 1.48 µmol L−1) may exacerbate coral bleaching during thermal stress events, representing a co-stressor rather than OA effect. Coral bleaching HotSpot (HS) identification emphasizes how spatially distributed HS are from January to June. The OA risk assessment confirmed that climate change will bring high risk to the coral calcification, reproduction, and damage to the breeding ground, which impact on the ecology and economy of Sri Lanka.

Realizing the full potential of incorporating mine tailings as supplementary cementitious materials (SCMs) to replace ordinary Portland cement (OPC) requires carefully balancing the benefits-such as cost reduction and emissions mitigation-while ensuring the mixtures achieve the required strength. Given the demonstrated effectiveness of combining machine learning (ML) with optimization algorithms in similar multi-objective optimization (MOO) problems, for the first time, this study employed a novel tabular prior data fitted network (TabPFN) model to forecast the uniaxial compressive strength (UCS) of those mix designs. The TabPFN model outperformed traditional boosting ML models, achieving an R2 of 0.973 and a low prediction error of 2.115 MPa. Notably, its pre-trained architecture reduced computational time by 1045 s. Building on this, a MOO case study was developed using the TabPFN model to predict UCS as the first objective, alongside separate equations used as objective functions to calculate cost and total emissions. This MOO problem was tackled using the non-dominated sorting genetic algorithm-II (NSGA-II). The optimized mixture designs achieved better balances between strength, cost, and emissions than those obtained through experimental methods, validating the use of this ML-based method for mixture design. Finally, a software tool-GreenMix AI-was developed to provide integrated access to the entire framework, translating advanced research into practical application. In essence, this research supports the reuse of mine tailings as SCMs and provides a practical pathway to developing more economical and sustainable cementitious mixtures.

This study presents a novel strategy to enhance the efficiency of dye-sensitized solar cells (DSSCs) by integrating a p-type La2O3 layer between the TiO2 photoanode and the electrolyte. The p-La2O3 layer suppresses charge recombination by inhibiting electron transfer from TiO2 to the electrolyte, improving charge separation and photovoltaic performance. Structural and compositional analyses (XRD, XRF, and SEM) confirm the presence of a uniform polycrystalline La2O3 layer on the 5-layered nanostructured TiO2 electrodes. Mott-Schottky analysis confirms that TiO2 is n-type and La2O3 is p-type, highlighting their complementary roles in efficient charge transfer. For the first time, DFT + U calculations were employed to investigate the band structure of La2O3. The electron diffusion length was found to be enhanced upon the integration of p-La2O3, as revealed by electrochemical impedance analysis. The optimized device exhibits a power conversion efficiency of 8.50 %, with a short-circuit current density of 17.70 mA cm-2 and an open-circuit voltage of 0.73 V, representing a 34.07 % enhancement compared to the reference six-layer photoanode DSSC (6.34 %). The results demonstrate the effectiveness of a p-type La2O3 layer in boosting device performance by reducing charge recombination. To our knowledge, this is the first report demonstrating the effectiveness of La2O3 as a blocking layer in quasi-solid-state DSSCs, presenting one of the highest efficiencies.

Supercapacitors (SCs) hold significant promise as a key component in energy storage systems due to their unique combination of high-power density, rapid charge/discharge rates and long cycle life. Unlike traditional batteries, SCs can efficiently handle frequent charge/discharge cycles without significant degradation, making them ideal for applications requiring rapid energy delivery. Graphene oxide (GO) and reduced graphene oxide (RGO) are gaining significant attention as electrode materials for SCs due to their unique properties. The solvents, HNO3 acid and DMF not only facilitate the dispersion of graphene-based materials but also impact the morphology, surface chemistry, and structural properties of the resulting thin films. The present research explores the suitability of GO and RGO-based composite electrodes prepared using 0.1 M HNO3 acid and DMF for SCs. The reported work mainly focused on comparing the performance of RGO-based SCs with that of GO-based (control) using HNO3 and DMF as solvents. The maximum specific capacitance obtained was 146 F g- 1 at 2 mV s- 1 for RGO - DMF-based SC at room temperature. The maximum gravimetric energy density of 21.58 Wh kg- 1 and gravimetric power density of 81.55 kW kg- 1 were exhibited for GO - DMF and RGO - DMF-based SCs, respectively. Electrochemical impedance analysis was used to characterize the device. Investigation of solvent influence on electrode preparation is unique and can provide insights into optimizing the electrode preparation process. The study uncovers that DMF is a better solvent for preparing GO and RGO-based electrodes. In addition, the study reveals that RGO-based SC shows higher performance compared to that of GO-based one.

Tea blending is a delicate craft that demands extensive experience and a deep understanding of ingredient combinations. Initially aimed at ensuring consistent tea qualities to meet consumer preferences, blending has since evolved to add value, enhance profitability, and diversify tea offerings. The subjective nature of blending, relying only on the tea blender’s decisions, poses challenges in maintaining consistency, reducing operational costs, and expediting the selection of optimal blends. As tea blending is a multi-faceted optimisation procedure, manual handling struggles to address multiple factors simultaneously. Traditional methods for identifying blend compositions involve costly chemical analyses. Therefore, modern technologies, like artificial intelligence, optimisation algorithms, and advanced analytics, have been integrated to boost accuracy, closely match target tea properties, accelerate performance, and uphold uniformity. Currently, a comprehensive review of tea blending is unavailable. This review explores the blending process, its challenges, and the technological advancements in optimisation and composition identification. It also introduces a new classification for blending methods, delves into research gaps, and suggests ways forward for enhancing the blending process within the tea industry.