Scholarly Articles
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Publication Embargo A Comprehensive Investigation of Microplastic Contamination and Polymer Toxicity in Farmed Shrimps; L. vannamei and P. monodon(Springer Nature, 2025-02-20) Jayaweera, Y. U; Hennayaka, H. M.A.I; Herath, H.M.L.P.B; Kumara, G. M.P; Mahagamage, M.G.Y.L; Rodrigo, U.D; Manatunga, D. CMicroplastic (MP) pollution poses a significant threat to marine ecosystems, seafood safety, and human health. This study investigates the accumulation of microplastics in two commercially important shrimp species, Litopenaeus vannamei (L. vannamei) and Penaeus monodon (P. monodon), sourced from cluster farming sites in Puttalam, Sri Lanka. Shrimp exoskeletons and edible soft tissues underwent rigorous microplastic analysis, including density separation, alkali digestion, stereo microscopy, and Raman spectroscopy. The results revealed high microplastic contamination, with L. vannamei containing an average of 4.99 ± 1.81 MP particles/g and P. monodon containing 1.87 ± 0.55 MP particles/g. Microplastic sizes varied, with L. vannamei predominantly contaminated with 100–250 µm particles and P. monodon with 500 µm—1000 µm particles. Fiber morphotypes were prevalent in L. vannamei, while blue-colored microplastics were dominant in P. monodon. These comprised polystyrene (PS), nylon 6,6, and polyethylene (PE) which were identified by Raman spectroscopy. Additionally, the study investigated the acute toxicity effects of microplastic polymer combinations using a zebrafish embryo model (FET236 assay). Zebrafish embryos exposed to polyethylene-nylon 6,6 combinations exhibited significant adverse effects on hatching, survival, and heart function at lower concentrations, while polyethylene terephthalate-polystyrene combinations showed no considerable effects. These findings underscore the urgent need for monitoring and managing microplastic contamination in shrimp farming areas. Future research should focus on elucidating the ecological impacts and human health risks associated with microplastic exposure.Publication Open Access A cross-category analysis of high impact low occurrence (HILO) disasters(Elsevier Ltd, 2026-03-19) Samaraweera, U; Kulatunga, U; Dias, PThis paper explores six High Impact Low Occurrence (HILO) disasters, generating insights from five different categories associated with them, namely causes (geophysical, technological, biological, sociological), phases (preparedness, response, recovery), dimensions (socio-economics, governance, equity), sectors (health, education, infrastructure, economy) and national contexts with differing levels of economic development. The process involved the generation of a questionnaire, based on a literature review; and the subsequent analysis and discussion of the questionnaire responses made by six experts nominated by six academies of science in Asia. The findings highlight the limitations of probabilistic, frequency-based risk models for HILO disasters and instead emphasise the importance of scenario-based (worst-case) analyses; mechanisms that preserve inter‐generational knowledge, institutional continuity and community‐based early‐response networks; strengthening community resilience while ensuring equity; and making appropriate investments for increasing preparedness, if not through structural interventions, at least through sustained awareness programs and periodic drills. Theoretical contributions include arguments that institutional capacity, governance quality, and social resilience are more decisive determinants of HILO event outcomes than probabilistic risk analyses; and that effective preparedness depends more on anticipatory planning, scenario-based training and institutionalised memory rather than experiential learning; thus advancing HILO theory beyond event-centred and frequency-driven interpretations towards a more governance- and resilience-oriented understanding.Publication Open Access A physics-informed machine learning for detecting suspicious satellite maneuvers (orbital manipulation)(Elsevier B.V., 2026) Karunathilake K.K.H; Abeywardena, K.Y; Vecchini, SSatellite systems have become prime targets for cyberthreats given their critical role in global infrastructure and general lack of security. Among these, orbital manipulation, a form of satellite hijacking, is a particularly severe threat that can disrupt essential operations and impact national security. To address these concerns, this research proposes an Artificial Intelligence (AI)-based anomaly detection system that utilizes Machine Learning (ML) models to analyze telemetry data for possible orbital manipulations with a multi-gate physics architecture grounded in orbital mechanics, to verify that detected anomalies are kinematically inconsistent and are therefore genuine integrity failures. This research demonstrates that temporal-based models like LSTM are essential for this domain, achieving high recall rates which are then validated by the physics component. While the framework includes multiple physical constraints, this study specifically validates the energy-based Vis-Viva gate, with the Tsiolkovsky and Angular Momentum gates established as architectural designs for future verification. This study concludes that successful AI deployment in orbital cybersecurity requires a comprehensive approach that integrates domain-specific context and physics-informed validation beyond traditional performance metricsPublication Embargo Accessibility and usability of virtual learning platforms: Lived experiences of visually impaired undergraduates in Sri Lanka(Elsevier Ltd, 2026-03-12) Rajapakshe, W; Wickramaarachchi, C; Alwis, M.K. S.S; Amarasinghe, A.A. M.L; Jayasekara, P.N; Jayasekara, P.TThis study explores the accessibility and usability of virtual learning platforms of visually impaired undergraduate students in Sri Lanka, focusing on their lived experiences, use of assistive technologies, and institutional support mechanisms. As online learning becomes increasingly prevalent, accessibility and inclusive challenges persist, particularly in developing countries with limited infrastructure and institutional support. Despite the availability of assistive technologies, visually impaired learners frequently encounter barriers, including poorly designed platforms, limited usability of screen readers, and inadequate institutional guidance. Addressing a critical research gap, this study investigates how visually impaired undergraduates experience and navigates virtual learning environments to identify accessibility barriers, enabling practices, and context-specific strategies for inclusive digital learning. Using a qualitative approach, semi-structured interviews were conducted with fifteen visually impaired university students across Sri Lanka. Thematic analysis revealed five core themes: barriers and challenges to effective virtual learning, preferred virtual platforms, accessibility features and tools, facilitators of learning success, and strategies to optimise the learning environment. These findings illuminate how systemic inequalities, infrastructural limitations, and institutional neglect collectively constrain the digital learning experience for visually impaired students, while also highlighting enabling practices that foster access and inclusion. The study's originality lies in foregrounding student-led insights in a developing country context and integrating practical, context-specific recommendations for platform developers, educators, and policymakers. By centering the voices of visually impaired learners, this research contributes unique and actionable knowledge to the field of inclusive digital education.Publication Open Access Achieving zero hunger: A global policy lens on food security drivers and income group disparities(Elsevier B.V., 2026-03) Pulle, N; Sampath, P; Perera, S; Wijayaweera, D; Jayathilaka, RMany countries struggle to meet their daily dietary requirements despite numerous attempts to address the existing demand. Consequently, this study collectively analyses the impact of urbanisation, renewable energy, greenhouse gas emissions, population growth, gross domestic product per capita and agricultural land on food production relying on Sen's Entitlement Theory, thus providing insights to resolve the long-standing issue of food insecurity, and support the achievement of the Sustainable Development Goals. The study utilises a stepwise panel ordered Probit model on 146 countries, for the years 1993 to 2023. It further categorises the food production index into three categories of food security as; low, moderate and high, thereby enabling discussion of the likelihood of a country falling into one of the aforementioned food security categories over the years. Urbanisation, agricultural land, and the dummy variables introduced to represent the income groups have been identified to have a significant and favourable relationship with the food production index. In contrast, the greenhouse gas emissions and renewable energy variables have a significantly inverse impact on the food production index. This makes a unique contribution to the existing body of literature, especially by comparing odds over the years, across different food secure categories, countries, and their specific income levels. This study enables policymakers to gain a comprehensive historical perspective on each case. This study further promotes the Sustainable Development Goals, highlighting areas where these goals have been negatively impacted. Additionally, the study discusses optimised investment allocations, agricultural research and development, agricultural technology, climate resilient farming, and sustainable urbanisation planning as solutions for extreme cases.Publication Open Access Achieving zero hunger: A global policy lens on food security drivers and income group disparities(Elsevier B.V., 2026-01-19) Pulle, N; Sampath, P; Perera, S; Wijayaweera, D; Jayathilaka, RMany countries struggle to meet their daily dietary requirements despite numerous attempts to address the existing demand. Consequently, this study collectively analyses the impact of urbanisation, renewable energy, greenhouse gas emissions, population growth, gross domestic product per capita and agricultural land on food production relying on Sen’s Entitlement Theory, thus providing insights to resolve the long-standing issue of food insecurity, and support the achievement of the Sustainable Development Goals. The study utilises a stepwise panel ordered Probit model on 146 countries, for the years 1993 to 2023. It further categorises the food production index into three categories of food security as; low, moderate and high, thereby enabling discussion of the likelihood of a country falling into one of the aforementioned food security categories over the years. Urbanisation, agricultural land, and the dummy variables introduced to represent the income groups have been identified to have a significant and favourable relationship with the food production index. In contrast, the greenhouse gas emissions and renewable energy variables have a significantly inverse impact on the food production index. This makes a unique contribution to the existing body of literature, especially by comparing odds over the years, across different food secure categories, countries, and their specific income levels. This study enables policymakers to gain a comprehensive historical perspective on each case. This study further promotes the Sustainable Development Goals, highlighting areas where these goals have been negatively impacted. Additionally, the study discusses optimised investment allocations, agricultural research and development, agricultural technology, climate resilient farming, and sustainable urbanisation planning as solutions for extreme casesPublication Embargo Activity enhanced TiO2 nanomaterials for photodegradation of dyes-A review(Elsevier, 2021-12-01) Thambiliyagodage, C. JWastewater generation due to anthropogenic activities has become a tremendous problem that the world is struggling to solve. Dyes release to normal water reservoirs badly impacts the environment causing severe issues. Removal of dyes from wastewater streams is important. The advanced oxidation process is advantageous as the dye molecules are degraded into harmless species. TiO2 is the most promising semiconductor that has been researched. However, the use of it in the visible range is restricted due to its high band gap (3.0 eV). TiO2 has been modified in order to enhance visible light sensitivity. This review mainly focused on the effects of doping TiO2 with metals and non-metals and coupling with metal and non-metal oxides to improve its efficiency in photodegrading dyes. TiO2 doped with Fe, Cu and Ag as the main metal species, N, S, and C as the main non-metals are summarized. Further, the effect of doping with multi non-metals and co-doping of metals and non-metals are also discussed. Moreover, coupling TiO2 with metal oxides and graphene oxide for enhanced photocatalytic activity is also summarized in this review.Publication Embargo Analysis of the ‘Toll Free Agricultural Advisory Service’ Data as Decision Support Tool for the Department of Agriculture(IEEE, 2022-07-18) Rajapaksha, N; Dias, NThe Department of Agriculture’s Toll-Free Agricultural Advisory Service was formed with the 1920 short code and is connected to all land and mobile telephone service providers in Sri Lanka. This short code allowed farmers and other stakeholders to contact technical officers which Agriculture Instructors immediately. All the information was gathered into the 1920 call center database. Farmers all over the island bring their agricultural problems to the 1920 Agricultural Advisory Service. Nevertheless, it can be seen that they do not do any analysis of these problems. This big data if properly examined has the potential to assist the country on a massive scale in the future. This study for carrying out to explore the possibility of introducing decision support for the 1920 reporting system to generate enhanced analytics and to make it easier to make informed decisions by the top management of the Department of Agriculture, more efficiently and effectively than the reporting method previously.Publication Open Access Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills(Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, R.S; Wijesinghe, R.E; Herath H.M.L.P.B.; Nalin de S.K.M; de Silva, R.M; Rajapaksha, S.M; Wijenayake, U; Manatunga, D.CThe existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.Publication Open Access Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills(Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, R.S; Wijesinghe, R.E; Herath H.M.L.P.B; Nalin de S.K.M; de Silva, R.M; Rajapaksha, S.M; Wijenayake, UThe existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.Publication Open Access Assessing the influence of diverse skills on employability outcomes for IT undergraduates(Public Library of Science, 2026-04-10) Senadheera, D; Wisenthige, KRapid technological advancements have reshaped the global job market, emphasizing the importance of specialized competencies such as user interface (UI) and user experience (UX) design, alongside technical and interpersonal skills.. This study examines how UI/UX skills (UIUX), soft skills (SS), and technical skills (TS) influence the employability (EP) of IT undergraduates in Sri Lanka, addressing a notable gap in existing literature that often examines these competencies in isolation and predominantly within Western contexts. The current study offers a detailed examination of employability determinants in Sri Lanka’s IT sector by incorporating gender as a moderating factor and investigating the mediating roles of self-efficacy (SE) and proficiency levels (LP). The collection of data involved 345 IT undergraduates participating in structured surveys, which were subsequently analyzed using partial least squares structural equation modelling (PLS-SEM). The results demonstrate that gender significantly affects the relationship between soft skills and technical skills with employability, underscoring differences in the assessment of these competencies among different genders. Moreover, the degree of proficiency influences the connection between technical skills and employability, yet it does not play a significant mediating role in the relationship between soft skills and UI/UX employability. Self-efficacy has proven to be a significant mediator across various skill categories UI/UX, soft, and technical highlighting its essential function in converting competencies into career success. This work seeks to add to existing knowledge by tackling the main significant gap of examining the combined effect of UI/UX, soft, and technical skills on employability. This study contributes to the theoretical understanding of employability by presenting an integrated model that elucidates the complex interactions among skills, mediators, and gender within the Sri Lankan IT sector. The results provide actionable insights for educators, policymakers, and industry leaders, advocating for curriculum alignment with industry needs and the promotion of self-efficacy through mentorship and experiential learning.Publication Open Access Automated design of reinforced concrete dapped-end connections using hybrid deep learning and generative AI augmentation(Elsevier Ltd, 2026-04-15) Dharmawansha, S; Herath, S; Fernando P.L.N; Meddage D.P.P.; Rajapakse, CDapped-end connections, also known as half-joints or Gerber beams, are widely used yet structurally vulnerable elements in precast concrete structures due to high stress concentrations near the re-entrant corner. Therefore, a comprehensive assessment of the load-bearing capacity of dapped-end connections is important to ensure structural integrity and mitigate the risk of failure. Although prior studies have explored their behaviour through analytical and experimental methods, the application of data-driven approaches remains limited due to the availability of limited experimental data, which constrains the predictive accuracy and generalisation of Machine Learning (ML) models. This study presents a novel approach that integrates numerical simulation with Conditional Tabular Generative Adversarial Network (CTGAN)-based data augmentation to enhance prediction accuracy and model generalisation. A numerical database containing 720 results was developed, which was expanded with 680 augmented data using CTGAN. The combined dataset of 1400 instances was used to train Artificial Neural Network (ANN), Genetic Algorithm-ANN (GA-ANN), and Particle Swarm Optimisation-ANN (PSO-ANN) models. The hybrid models outperformed the standalone ANN, with GA-ANN achieving the highest accuracy (testing R2 = 0.961). The trained models were separately validated using 64 unseen experimental datasets, which shows the improved generalisation of the models through augmentation. Shapley Additive Explanations analysis reveals that the GA-ANN model predictions aligned with the principles underlying the compatibility of deformations of dapped-ends. Further, a novel ML-assisted design model was developed, which predicts multiple solutions for a given design problem, assisting in the optimisation of connection design.Publication Open Access Ball milling–A green and sustainable technique for the preparation of titanium based materials from ilmenite(Elsevier, 2022-01-01) Thambiliyagodage, C. J; Wijesekera, RIlmenite is a naturally available mineral that is highly applicable in the synthesis of pure TiO2. Titania mainly presents in four polymorphs as rutile, anatase, brookite and TiO2–B. Titania could be mined from minerals such as ilmenite, leucoxene and rutile among which ilmenite is the main source. Ball milling is a mechanical activation method used before subjecting ilmenite to chemical treatment methods to produce titanium based materials. Effect of milling time, milling intensity, milling atmosphere, the introduction of reducing agents on the particle size, surface area, annealing temperature, and the crystal structure of the products are reviewed. The effect of ball milling on acid digestion of ilmenite in hydrochloric acid and sulfuric acid is discussed. Further, the effect of mechanical activation on hydrothermal treatment of ilmenite is explained in detail.Publication Open Access Beyond compensation: effect of employee benefits on job motivation, performance, and turnover intention(Cogent OA, 2026) Peemanee, J; Weerarathna, R; Issarapaibool, A; Boonlua, S; Rathnayake, NThis study investigates the influence of employee benefits on motivation, performance, and turnover intention within contemporary workplaces that increasingly emphasize employee well-being. Addressing a key gap in the literature, it employs Structural Equation Modeling (SEM) and analyzes data from 387 Generation Y and Generation Z employees in Small and Medium Enterprises (SMEs) in Thailand. The analysis examines how diverse benefit types influence employees’ motivation, performance, and decisions to remain with their organizations. The findings reveal a direct and positive link between employee benefits, enhanced motivation, and improved performance, which together significantly reduce turnover intention. Specifically, attraction and retention strategies, organizational support mechanisms, and a growth-oriented, well-being-focused environment emerged as critical factors in motivating employees and elevating their performance. Overall, the study demonstrates that strategically designed employee benefit packages—aligned with employee needs and workplace realities—foster engagement, productivity, and loyalty. This study contributes valuable insights for organizational leaders seeking to refine benefit systems and extends the academic understanding of the strategic importance of non-monetary benefits in promoting employee satisfaction and retention.Publication Open Access Bi-directional long short-term memory based ensemble deep learning framework for non-linear steam turbine power forecasting: a biomass fuelled case study(Elsevier Ltd, 2026-04-10) Perera, H; Jayasekara, S; Wijesinghe, R.E; Silva, B. N; Cha, HIn palm oil manufacturing, steam turbines powered by biomass fuel are central to energy generation. However, fluctuating load demands and temporal variations lead to inefficiencies, while limited and variable supply of biomass waste constrains boiler feed flexibility. Current index-based boiler feeding methods overlook actual load demands and waste availability, resulting in significant energy wastage. This study presents a novel ensemble deep learning model combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) with Attention Layers, trained on an eight-year operational dataset with structured preprocessing and feature selection, to forecast steam turbine power generation. The model captures complex non-linear temporal patterns more effectively than conventional and standalone ML models, achieving a Root Mean Square Error (RMSE) of 0.0684, Mean Absolute Error (MAE) of 0.0414, and an R-squared (R2) value of 0.9832, which outperformed eight benchmark models by approximately 25% in prediction accuracy. Additionally, the framework incorporates operational parameters such as kVA, total energy, and Fresh Fruit Bunch (FFB) production to dynamically optimise biomass feed rates, balancing energy output with resource availability. This approach minimises energy wastage, reduces grid reliance, and promotes both sustainability and profitability.Publication Embargo Bridging tradition and innovation: exploring vegetable harvest loss reduction strategies in Sri Lanka(Emerald Publishing, 2026-01-15) Jayasuriya, N; Yapa, C.G; Rathnayake, T.A; Dilhara, A; Rathnayake, I.D; Mathangadeera, RPurpose – This study aims to address a significant gap in the literature regarding vegetable harvest loss reduction methods, exploring both traditional and modern perspectives in Sri Lanka, which is largely driven by an agricultural economy. This study explores the diverse strategies employed and how they are going to be integrated by Sri Lankan vegetable farmers, highlighting both traditional and modern pre- and post-harvest practices aimed at improving productivity, sustainability and resilience in agricultural systems. Design/methodology/approach – The study was conducted across key agricultural districts in Sri Lanka, with data collected through semi-structured interviews with vegetable farmers using the snowball sampling method. Thematic analysis was employed to identify patterns and themes in the data. Findings – The findings emphasize the importance of traditional methods, including cultural practices such as cultivating at auspicious times, established pest control and irrigation techniques. These are complemented by advanced agricultural innovations, modern harvest protection methods and improved packing and transportation techniques. This integrated approach showcases farmers' adaptability in reducing vegetable losses despite the challenges they face. Originality/value – Post- and pre-harvest loss reduction in Asian countries can be considered an understudied area. Furthermore, the focus on traditional methods is rare in the field. Therefore, this study provides a clear understanding of traditional and modern methods that are suitable for farmers in developing countries.Publication Embargo Catalytic graphitization in nanocast carbon monoliths by iron, cobalt and nickel nanoparticles(Pergamon, 2018-08-01) Thambiliyagodage, C. J; Ulrich, S; Araujo, P. T; Bakker, M. GHierarchically porous carbon monoliths containing metal (Fe, Co, Ni) nanoparticles were synthesized in a one-pot synthesis through a nanocasting technique using silica (SiO2) as the template. The macropore structure of SiO2 has been replicated in nanocast carbon and N2 adsorption analysis shows that the monoliths have high surface area, high mesopore volume, and that micropores are also present. The temperature effect on catalytic graphitization was studied by using x-ray diffraction (XRD), transmission electron microscope (TEM) and Raman spectroscopy. It is observed that iron was capable of producing turbostratic carbon at 500 °C, while turbostratic carbon was produced at temperatures of 700 °C when cobalt and nickel are present. Iron, cobalt, and nickel were found to be good graphitization catalysts with the order of catalytic activity being iron > cobalt > nickel. Raman spectroscopy experiments provide insight into the degree of ordering of the carbon of each sample and are in agreement with the results from the other techniques: with increasing pyrolysis temperature, with and without the presence of metals, ordering of amorphous carbon is confirmed. Detailed analysis of the Raman spectroscopic data showed clear differences between the metal catalyzed and non-catalyzed samples enabling the contributions from the two different mechanisms to be clearly distinguished.Publication Open Access Circular Valorization of Post-Industrial Textile Waste in Thermal-Insulating Cementitious Ceiling Sheets(Multidisciplinary Digital Publishing Institute (MDPI), 2026-02-27) Fernando, K. V; Dodangodage, C.A; Seneviratne, V.M; Jayasinghe, S.M; Dharmaratne, D.D; Gamage, G.N; Halwatura, R. H; Gunasekera U.S.W; Halwatura, R.UThe construction sector faces increasing pressure to reduce the embodied energy of building materials while valorizing industrial waste streams. This study evaluates the direct incorporation of post-industrial textile waste (100% cotton and cotton–polyester blends) in its native form to develop high-performance cementitious ceiling sheets. Composites were fabricated under a controlled hydraulic compaction pressure of 2.0 MPa, optimized to achieve matrix densification while preserving the integrity of the fibrous network. Viscoelastic recovery of the compressed fibers induced a hierarchical double-porosity architecture characterized by macro-voids and hollow fiber lumens. This microstructural evolution reduced thermal conductivity to 0.091 W/m·K, approximately 50% lower than commercial cement–fiber benchmarks—without compromising mechanical compliance. Scanning Electron Microscopy (SEM) revealed a mechanistic decoupling between water absorption and dimensional stability. Although the CP15 formulation (15 wt.% cotton–polyester) exhibited high moisture uptake (~21%), thickness swelling remained limited to 1.35%. This dimensional stability is attributed to the hydrophobic polyester framework, which bridges microcracks and constrains hygroscopic expansion within the cellulosic phase. The optimized CP15 composite achieved a Modulus of Rupture (MOR) of 8.75 MPa, exceeding ISO 8336 Category C, Class 2 requirements. Despite increased thickness, the areal density (10.84 kg/m2) remains compatible with standard gypsum-grade suspension systems, eliminating the need for structural modification. These findings establish a scalable, direct-valorization strategy for circular construction materials delivering enhanced thermal insulation and robust performance under tropical climatic conditions.Publication Open Access Coconut Shell Waste-Derived Porous Carbon-Supported Sn Catalysts for Efficient Electrochemical CO2Reduction to Formic Acid and Deuterated Formic Acid(American Chemical Society, 2025-11-05) Qin, C; Masakorala, G; Mohideen, M; Samarasekara, T; Zhang, L; Zhu, W; Zhou, Y; Thambiliyagodage, CIndustrial-level electrochemical CO2 reduction reaction (CO2RR) to form HCOO– and DCOO– requires robust Sn catalysts with high performance. In this study, the hydrothermal method was employed to load varying amounts of Sn precursors onto waste biomass-derived porous carbon to investigate the structure–activity relationship between Sn loading forms and HCOO– selectivity. Through comprehensive ex/in situ characterizations, we discovered that with 5% Sn precursor addition, highly dispersed SnO2 nanoparticles formed on the carbon support, enabling the catalyst to exhibit exceptional HCOO– activity (Faradaic efficiency exceeding 90%) across a broad potential window. In situ attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and in situ Raman spectroscopy revealed that the highly dispersed SnO2 nanoparticles enhance the stability of the *OCHO intermediate. Furthermore, when H2O was replaced with D2O, the generation of DCOO– was observed, and good selectivity was maintained. This study provides a facile strategy for waste biomass conversion and the design of Sn-based catalysts for DCOO– production.Publication Open Access Correlation between molecular diversity and biochemical traits of edible aerial parts of Basella alba L. from different geographical locations of Sri Lanka(BioMed Central Ltd, 2025-12-29) Dahanayaka, L. W.; Mapa, M. M. S. T; Kadigamuwa, C.C; Udayanga, DBackground: Basella alba L. a widely consumed green leafy vegetable, exhibits considerable nutritional and therapeutic potential attributed to its bioactive constituents. Prior investigations revealed significant variation in phytochemical and antioxidant activity across agro-climatic zones in Sri Lanka, suggesting potential genetic influence. This study is designed to explore underlying genetic variation using RAPD markers to investigate the correlations and contributions of genotype on previously reported bioactivity variation. Results: From a screening of 15 RAPD primers, four primers (OPA 9, OPA 10, OPA 16, and OPB 10) produced, polymorphic, consistent and clearly scorable banding profiles (under optimized PCR conditions) in B. alba L. collected from 15 Sri Lankan locations. These primers collectively yielded 36 bands, 35 of which were polymorphic, resulting in a high polymorphism rate of 97.2%, confirming the informativeness of the selected primers for genetic diversity analysis. Genetic similarity was assessed using Jaccard’s coefficient in NTSYSpc.v2·10e, revealing values ranging from 0.44 to 0.97, with the highest similarity from the samples from Ratnapura and Kandy and the lowest similarity in Ratnapura and Kalutara. A dendrogram constructed via UPGMA grouped the samples into two major clusters and five sub-clusters, demonstrating substantial genetic differentiation influenced by geographic origin. Cluster I included Ratnapura and Kandy, while the remaining samples formed Cluster II and its subgroups, each representing different ecological zones. When compared to the phytochemical and antioxidant clustering data of the previous study, partial correspondence was observed. A Mantel test comparing genetic diversity and biochemical/antioxidant potential revealed a weak negative correlation which was not significantly different. Some of the locations within similar genetic cluster shared similar biochemical traits, while others diverged significantly, indicating that environmental conditions also influence bioactive compound synthesis. Notably, Cluster I (Ratnapura and Kandy) showed both genetic similarity and lower antioxidant traits. Samples from Ella and Polonnaruwa showed similar bioactive traits even though they were grouped into different genetic clusters. Conclusion: These findings suggest that both genetic makeup and environmental adaptation contribute to observed biochemical diversity in B. alba L. with a clear geographical correlation. This study highlights the value of integrating molecular and biochemical analyses to develop regionally adapted B. alba L. cultivars with enhanced nutritional traits, supporting sustainable agriculture in Sri Lanka and beyond.