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Item Feasibility of using metakaolinite for the treatment of coal-mining acid mine drainage: Insights into the interaction behaviour and partitioning of inorganic contaminants(2025) Mothetha, M; Msagati, T; Masindi, Vhahangwele; Kebede, KIn this novel study, the efficacy of metakaolinite for the treatment of acid mine drainage (AMD) was evaluated. The optimized parameters included the feedstock dosage and contact time. Experimental results were further explored using inductively coupled plasma–mass spectrometry (ICP–MS), ICP–OES (inductively coupled plasma–optical emission spectroscopy), Fourier transform infrared spectroscopy (FTIR), high-resolutionfocused ion beam/scanning electron microscopy (HR–FIB/SEM), energy-dispersive x-ray spectroscopy (EDS), x-ray fluorescence (XRF) and x-ray diffraction (XRD). Optimum conditions were observed to be 45 min of mixing time, ≥10 g·L−1 of feedstock dosage, i.e., metakaolinite, and ambient temperature and pH. The metal content (Fe, Mn, Cr, Cu, Ni, Pb, Al, and Zn) embedded in AMD matrices were partially removed whilst the level of sulphate was significantly reduced. Chemical species removal efficacies were observed to occur in the following sequence; Cr ≥ Zn ≥ Cu ≥ Pb ≥ Mn ≥ Ni ≥ sulphate ≥ Mg ≥ Fe, with the following removal percentages: 100, 91.7, 74.6, 65, 38.8, 37.5, 32.3, 13.8, and 8.3%, respectively. Thus metakaolinite proved to be partially effective in the treatment of AMD emanating from coal-mining processes. Furthermore, to enhance the performance of this technology, a polishing technique needs to be coupled or integrated to further remove residual inorganic contaminants, as well as other forms of modification such as the addition of alkaline agents to synthesize the nanocomposite and increase its alkalinizing capabilities.Item Assessment of the water quality and microbial regrowth in drinking water treatment plants and the distribution network(2025) Nduli, S; Tekere, M; Masindi, Vhahangwele; Foteinis, SRecurring contamination of drinking water and microbial regrowth in distribution networks remains an issue of prime concern to water provision authorities. This is common in the developing world, where aging and under-developed infrastructure along with degraded freshwater resources exacerbate the problem. Here, the year-round measurements, on a weekly basis, of the quality of drinking water from a typical water treatment and distribution system in the South African setting are reported. Results confirmed that the drinking water treatment plants under study rely on heavily degraded freshwater, mainly affected by microbial contamination which could suggest the release of untreated or poorly treated wastewater in receiving water bodies, a common problem in low- and medium-income countries (LMICs). In most cases, freshwater was effectively treated (e.g., 100% removal for E. coli and over 99%, 92%, and 83% removal for total coliforms, turbidity, and colour, respectively) to meet the drinking water quality standards for South Africa and the world health organisation (WHO) guidelines. Yet, in some monthly measurements, certain contaminants such as ammonia were above the prescribed limits, suggesting the need to operationally improve water treatment and/or curbing the release of untreated or poorly treated wastewater in the catchment. Alarmingly, microbial regrowth was identified within the distribution networks, and this was significantly correlated (p < 0.01) with the distance (from 0 to 101 km) that the water travels within each distribution network and nodes. Also, large seasonal variations in the water quality were observed, with water quality being poorer during winter, likely tracing back to environmental factors in combination with parts of the distribution system being laid proximal to the surface or above ground. Overall, a clear correlation between the chlorine concentration and microbial failure was observed. This could be attributed to high chlorine demand, which devoids the system of residual chlorine, thus, to a larger extent, creating an environment that is conducive to microbial regrowth. Therefore, it can be concluded that high chlorine demand is the main contributor towards microbial regrowth within the water distribution networks, and, as such, comprehensive chlorine demand and decay studies are needed to identify whether chlorine booster stations are required, particularly at the distal ends of the network. This will inform the sustainable top-up of chlorine residual in the distributed water, hence effectively suppressing microbial regrowth. Albeit, high chlorine levels are not a panacea, since these can lead to the formation of toxic and carcinogenic disinfection by-products such as trihalomethanes (THMs). Therefore, first and foremost, focus should be placed on safeguarding the quality of freshwater resources.Item Spatial age-stratified epidemiological model with applications to South African COVID-19 pandemic(2025) Manjoo-Docrat, R; Abdelatif, N; Holloway, Jennifer P; Dudeni-Tlhone, Nontembeko; Dresselhaus, C; Mbayise, E; Janse van Rensburg, C; Fabris-Rotelli, I; Debba, Pravesh; Makhanya, SObjectives: This study aims to address the heterogeneity among different age groups in terms of their susceptibility to and transmissibility of infectious diseases. It also seeks to understand how spatial disparities affect disease spread and local population responses to emerging and re- emerging infectious diseases (EIDs and REIDs). Design/Methods: We developed a spatial age-stratified SEIR model using COVID-19 hospitalisation data from South Africa, focusing on the first two waves of the pandemic. This model incorporates contact matrices and demographic data to capture age-dependent and spatial variations in disease dynamics. Results: The spatial age-stratified model produced more biologically plausible and accurate predictions compared to non-stratified models investigated. It highlighted significant differences in COVID-19 risk and transmission across different age groups and regions, offering insights into targeted intervention strategies. Conclusions: The proposed model demonstrates the importance of considering both age and spatial heterogeneity in mathematical models for infectious disease prediction. It provides a valuable tool for governments and public health officials, particularly in resource-limited settings, to develop more effective and targeted interventions. This model can be adapted for other EIDs and REIDs with similar dynamics, enhancing preparedness and response strategies.Item Biomass-derived carbon-based nanomaterials: Current research, trends, and challenges(2025) Lesch, R; Visser, ED; Seroka, Ntalane S; Khotseng, LThe review investigates the use of biomass-derived carbon as precursors for nanomaterials, acknowledging their sustainability and eco-friendliness. It examines various types of biomasses, such as agricultural residues and food byproducts, focussing on their transformation via environmentally friendly methods such as pyrolysis and hydrothermal carbonisation. Innovations in creating porous carbon nanostructures and heteroatom surface functionalisation are identi ed, enhancing catalytic performance. e study also explores the integration of biomassderived carbon with nanomaterials for energy storage, catalysis, and other applications, noting the economic and environmental bene ts. Despite these advantages, challenges persist in optimising synthesis methods and scaling production. e study also highlights existing research gaps, forms a basis for future studies, and underscores the role of biomass-derived nanomaterials in promoting a circular economy and sustainability.Item Pathways to pluralism in Strategic Environmental Assessment (SEA): The Multi-Author Team and integrated governance model(2025-03) Schreiner, Gregory O; Snyman-van der Walt, Luanita; Adams, Abulele; Abed, Rohaida; Lochner, Paul A; De Wet, BenitaPluralism – the integration of different perspectives and values across diverse stakeholders – has long been considered foundational to Strategic Environmental Assessment (SEA) good practice. Yet guidance on how practitioners should elicit and then manage pluralism remains scarce. This professional practice paper explores the topical concept of pluralism within the context of the South African Energy SEA programme, conducted between 2013 and 2019. Authored by the project leaders of the programme, the paper provides a practice-inspired, plain-language account of how pluralism was attempted via the Multi-Author Team (MAT)/ integrated governance model. The paper describes the structure and mechanics of the model, highlighting several virtues and vices that practitioners might be mindful of when considering similar elaborate coproduction approaches. In summary, virtues include higher levels of pluralism and trust; enhanced systems for managing bias; the development of more complete conceptual frameworks; a broader psychological ownership of outputs; plus, the potential to reduce financial costs on large SEAs allowing for bigger, more inclusive writing groups. Vices, or points of caution, include substantially higher human resource costs, time costs and process complexity; confusion, tension and asymmetrical power dynamics within writing groups; and, if not monitored by experienced leadership, the risk of stealth issue advocacy.Item Recent advances in Fe-based metal-organic frameworks: Structural features, synthetic strategies and applications(2025-04) Mosupi, Keaoleboga; Masukume, Mike; Weng, G; Musyoka, NM; Langmi, HWMetal organic frameworks (MOFs) are very exciting porous materials owing to their unique properties such as high surface areas, high pore volume, tunable functionalities and great thermal stabilities. The properties of MOFs can be diversely constructed by precise control of synthesis conditions. Amongst the thousands of MOFs that have been discovered to date, Fe-MOFs make up a percentage of these MOFs. Fe-MOFs are increasingly gaining great interest due to their unique properties and chemical versatility. However, comprehensive reviews on their emerging architectural features and designs as well as strategies for tailoring their applications. Therefore, in this review, we present a panoptic summary of the recent developments of Fe-MOFs, which includes synthetic strategies, activation methods, functionalization, overview of selected applications, current challenges impeding their commercialization, and suggested remedial actions. A holistic view of the interconnectedness of Fe-MOFs structural features, synthetic strategies and applications provides greater insights that highlight challenges hindering their wide-scale industrial applications. Moreover, newer approaches such as utilization of machine learning technique that are providing an opportunity for out-of-sight insights for material design and prediction of material properties are briefly highlighted. Remedial actions for challenges of transitioning Febased MOFs towards commercialization and industrial applications are also explored, and suggestions for these aspects are presented.Item Power generation time series for solar energy generation: Modelling with ATlite in South Africa(2025) Botha, Gerda N; Coleman, Toshka; Wessels, Gert JC; Kleebauer, M; Karamanski, StefanThe global energy landscape is experiencing growing challenges, with energy crises in regions such as South Africa underscoring the drive to accelerate the shift toward renewable energy solutions. This paper presents an approach for improving solar energy planning, specifically focusing on leveraging the capabilities of the ATlite software in conjunction with custom data. Using mathematical models, ATlite (which was initially developed by the Renewable Energy Group at the Frankfurt Institute for Advances Studies) is a Python software package that converts historical weather data into power generation potentials and time series for renewable energy technologies such as solar photovoltaic (PV) panels and wind turbines. The software efficiently combines atmospheric and terrain data from large regions using user-defined weights based on land use or energy yield. In this study, European Centre for Medium-Range Weather Forecasts reanalysis data (ERA5) data was modified using Kriging to enhance the resolution of each data field. This refined data was applied in ATlite, instead of utilizing the standard built-in data download and processing tools, to generate solar capacity factor maps and solar generation time series. This was utilized to identify specific PV technologies as well as optimal sites for solar power. Thereafter, a simulated power generation time series was compared with measured solar generation data, resulting in a root mean square error (RMSE) of 19.6 kW for a 250 kWp installation. This approach’s flexibility and versatility in the inclusion of custom data, led to the conclusion that it could be a suitable option for renewable energy planning and decision making in South Africa and globally, providing value to solar installers and planners.Item Enhanced phase-based plasmonic biosensing with quantum states of light(2025-03) Mpofu, Kelvin T; Mthunzi-Kufa, PatienceOur research aims to create a theoretical framework that may be used to improve phase-based surface plasmon resonance (SPR) biosensor precision beyond the shot noise limit by utilizing quantum states including squeezed states and NOON states. For this work, a two-mode phase sensing setup model is created. One of the two arms of the two-mode model of this model has an integrated SPR system based on the Krestchmann arrangement. An experiment involving phase-based plasmonic biosensing is modeled using the two-mode setup. The state preparation, the biosensing component, and the measurement comprise the three main components of the model. The measurement varies depending on the particular input state. Quantum noise reduction for quantum states results from the combination of the sub-Poissonian statistical structure of a single mode and the non-classical correlation of the photon number between the two modes (entanglement). We show that when combined with the high sensitivity of the SPR sensor, the use of two-mode quantum states of light considerably increases the estimation precision of the refractive index of an analyte. Here, we use distilled water diluted BSA solutions at different concentrations that correlate to different refractive indices to reproduce a static phase-based SPR biosensor. Both lossless and lossy circumstances were taken into consideration for the model. The NOON state and the product coherent squeezed vacuum (PCSV) state are the two main quantum states taken into consideration in this work. The PCSV state exhibited the better limit of detection (LOD) among the states that we measured, suggesting that it is a promising candidate for the development of quantum biosensing systems. The effect of losses will require more investigation, but this work enables us to identify a precise course of action for enhancing the performance of the phase-based surface plasmon resonance sensor even further. Phase-based quantum SPR sensors have yet to be thoroughly examined, but intensity-based quantum SPR sensors have previously been extensively researched in the biochemical and medical sensing domains.Item Response of the global ITCZ to ENSO and how the ITCZ determined from maximum precipitation compares with the surface tropical wind convergence(2025-07) Ramotubei, Teke S; Landman, Willem A; Mateyisi, Mohau J; Nangombe, Shingirai S; Beraki, AFShifts in the position of the intertropical convergence zone (ITCZ) may lead to amplification of climate extremes such as droughts and flooding. Its spatio-temporal variations respond to well-established oscillation processes like the El Niño Southern Oscillation (ENSO). This research establishes the global and regional response of the ITCZ position to ENSO. It also explores the alignment between the ITCZ as determined from two methods: the surface tropical wind convergence, and maximum precipitation. The ERA5 reanalysis data, 1990–2020, are used in this study. Each longitude is scanned for latitude of maximum precipitation, during each El Niño/La Niña/Neutral year, within the 20°N/S latitude range to identify the ITCZ position. An overlay of surface tropical wind convergence and the ITCZ position is employed for comparison of the two methods. The study concludes that the position established by the maximum precipitation aligns with the surface tropical wind convergence over the global oceanic areas. On seasonal average, the La Niña ITCZ position is consistently southward of its El Niño position over Africa and Central Pacific Ocean. Furthermore, the extreme cases of El Niño/La Niña leads to further north/south shifting of the ITCZ position from its normal El Niño/La Niña positions. The continental and Atlantic Ocean ITCZ is more persistent and shows a minimal fluctuation, in comparison to Oceanic ITCZ, during the El Niño/La Niña. Cross-wavelet analysis was explored as an African case study and it shows common high-power features between the Oceanic Niño Index (ONI) and ITCZ signals over a four-year periodicity, mirroring the ENSO periodicity albeit with slowly varying time lag across the years. The cross-correlation of the two signals is strongest in Austral summer (DJF), corresponding to the peak of ENSO. This study contributes to the understanding of the overall description of the global and regional (with Australia and South America as new additions) ITCZ along with its response to the ENSO phases using the latest ERA reanalysis data. The global/regional spatio-temporal ITCZ shifts open an opportunity for improved interpretation of seasonal forecasts of hydroclimatic events, especially under climate change conditions that reflect a possibility of an increase in the frequency of ENSO events in the future.Item Interpretable Heart Disease Detection Model for IoT-Enabled WBAN Systems(2025-02) Olatinwo, DD; Abu-Mahfouz, Adnan MI; Hancke, GP; Myburgh, HCHeart disease is a leading global health concern, contributing to significant mortality rates. It encompasses a range of conditions affecting the heart, leading to complications such as blocked blood vessels, myocardial infarction, chest pain, and stroke. This study presents an interpretable heart disease detection model specifically designed for Internet of Things (IoT)-enabled wireless body area networks (WBANs). Our approach employs a highway bidirectional gated recurrent unit (BiGRU) network to accurately detect heart disease patients. To enhance the model performance, we address critical data preprocessing challenges, such as outliers in data, class imbalance, and feature selection. We employ a robust scaler data transformation method to mitigate the impact of outliers. The synthetic minority oversampling technique (SMOTE) is applied to address the imbalance in the dataset. We utilize the SelectKBest algorithm with the ANOVA F-test scoring function to select the mostrelevant features to improve the modelefficiency. Thedatasetispartitioned into training, validation, and testing sets to ensure model generalization. Hyperparameter optimization is performed using a random search method to determine the optimal model architecture. Furthermore, a highway network mechanism is incorporated to enhance information f low, leading to improved training efficiency and detection accuracy. To ensure clinical relevance and acceptability, we employ the SHapley Additive exPlanations (SHAP) technique to provide insights into the model’s decision-making process. Evaluation of unseen test data demonstrates that our proposed model outperforms existing approaches by 1%–9% in terms of detection accuracy.Item Green Synthesis and Application of Biochar Derived from Alien Vegetation Wood for Proton Exchange Membrane Fuel Cells(2025-04) Sobekwa, AG; Mojapelo, NA; Visser, ED; Seroka, Ntalane S; Khotseng, LInvasive alien vegetation brought about by various human activities has grown to be a significant threat to the ecosystem and its diversity; therefore, control strategies to combat this threat are being explored. This review aims to investigate the prospect of using biochar specifically from alien vegetation as a support material for the proton exchange membrane (PEM) fuel cell electrocatalyst, highlighting the need to move to green energy and invest in Eco conservation. The use of biochar derived from alien vegetation as carbon support for the platinum (Pt) electrocatalyst for PEM fuel cells is an interesting field that is slowly gaining momentum. Biochar has the potential to be used as a carbon support due to its high specific surface, area, and intrinsic property needed for an electrocatalyst support. The current widely used electrocatalyst, which is Pt supported on carbon black, has shown to suffer from corrosion which weakens the bond between the support and the Pt nanoparticles, leading to instability and resistance; therefore, alternative supports are needed also to decrease the Pt loading as it is expensive. The focus of this review is on the benefits and prospects of these cheap green resources in increasing efforts to conserve the environment.Item Tailoring the properties of 2D nanomaterial-polymer composites for electromagnetic interference shielding and energy storage by 3D printing—A review(2025-04) Gebrekrstos, A; Muzata, TS; Elias, A; Ray, Suprakas S3D-printed 2D nanomaterials-based polymer composites, with their exceptional electrical conductivity and structural functionalities, have become leading-edge engineering materials for electromagnetic interference (EMI) shielding, sensors, and energy storage applications. This review begins with a brief introduction to various types of 2D nanomaterials and their fabrication techniques, specifically different types of 3D printing. The subsequent sections highlight key factors such as rheological properties, surface tension, additives, and binders that influence the printability of 2D nanomaterials-based polymer composites. The advancements in 2D nanomaterials-based polymers, including MXene, graphene, and graphene derivatives, are then presented. The interaction, dispersion, and/oMr network formation of 2D nanomaterials in the polymer matrix is a crucial factor in determining the electrical performance of the composites. This review also discusses surface modification strategies for 2D nanomaterials to enhance their sensing, EMI shielding, and energy storage capabilities. Finally, the impact of various 3D-printed polymer composite geometries, such as rectangular, cylinder, and circular, on shielding performance is thoroughly examined, engaging the reader in the exploration of these materials.Item Measurement of phase transition, density and viscosity of supercritical carbon dioxide-Fischer-Tropsch wax mixtures2025(2025-06) Swanepoel, Andri; Labuschagne, Philip W; Schwarz, CEMelting temperature, phase behaviour and densities of binary mixtures of CO 2 and three Fischer-Tropsch waxes with varying molecular weights were experimentally determined. The melting temperatures of the lower molecular weight waxes increased with CO 2 pressure, and pressure induced crystallisation of the lowest molecular weight wax occurred above 20 MPa. CO 2 solubility in the waxes decreased with increasing wax molecular weight. Trends in mixture densities with changes in temperature and pressure mimicked that of pure CO viscosity of the lowest molecular weight wax decreased with increased CO 2 2 . The concentration, and decreased with increases in temperature and pressure, with the impact of pressure minimised above the temperature inversion point. Solubility data were correlated with a modified Chrastil and the Mendez-Santiago & Teja models. The Chrastil model accurately predicted solubility of CO 2 in all three waxes to within 1 % of the measured values.Item Effect of Cowpea Lignocellulosic Fibers as a Low-Value Reinforcing Filler on the Properties of Poly(butylene succinate-co-adipate) Bio-Composite Foams(2025-03) Masanabo, MA; Keränen, JT; Ray, Suprakas S; Emmambux, MNHerein, fully bio-based and biodegradable bio-composite foams are produced from poly(butylene succinate-co-adipate) (PBSA), reinforced with low-value , and azodicarbonamide as a chemical blowing agent. These are produced by melt extrusion followed by compression molding. Fiber addition increases the melt viscosity and melt strength, this restricts uncontrolled bubble growth during foaming to decrease the bubble size. The bio-composite foam containing 15% fibers has the largest decrease in bubble size from 209 μm in the unfilled PBSA foam to 95 μm in the foam containing 15% fibers. Fiber addition significantly increases the bubble density, from ≈1.05 × 109 cells cm−3 in the unfilled PBSA foam to 5.13 × 109 cells cm−3 in bio-composite foam containing 15% fibers, due to heterogeneous bubble nucleation induced by the fibers. The stiffness of the bio-composite foams increases with fiber addition, with the bio-composite foam containing 15% showing the largest increase relative to the unfilled PBSA foam as revealed by dynamic mechanical analysis. In conclusion, the f ibers not only induce heterogeneous bubble nucleation to increase bubble density and decrease bubble size during the foaming of PBSA, but also act as reinforcement to increase the stiffness of the bio-composite foams. These bio-composite foams have potential applications in packaging and agriculture.Item Sustained gamification in medication adherence: Strategies and conceptual framework(2025-03) Adetunji, RO; Botha, Adele; Herselman, Martha EThis paper examines the current body of literature on medication adherence using sustained gaming strategies and approaches. The objective is to investigate the concepts of sustained gaming, gaming strategies, and gaming approaches in relation to medication adherence. The goal is to identify the elements that contribute to a conceptual framework for understanding medication adherence behavior in patients, as part of a broader study. A comprehensive literature review was performed on four scholarly databases: MEDLINE, BMC, Global health, and Embase. The study examined the strategies and methodologies employed by the Pokémon Go game as a location-based game (LBG), and its impact on patients' adherence to medication. The study has identified several components, namely physical activities, social connections, exploration, enhanced emotional expression, and Marlien Herselman Council of Scientific and Industrial Research, Pretoria South Africa Technology for Development (ICT4D) viewpoints should encompass various aspects, including economic, ecological, educational in nature, political, cultural, and technological dimensions [10-12]. The concept of sustainability has gained significant attention in the field of Information Systems [8, 13] while the concept of long-term gaming strategies and approaches is seldom addressed and has yet to be thoroughly examined in scholarly literature. Furthermore, there is a lack of theoretical research that specifically examines the use of strategies and approaches for sustained gaming in relation to medication adherence. This article aims to identify the strategies, approaches, and gaming experiences that promote sustained gaming in order to develop a conceptual framework for medication adherence in patients focusing on Pokémon Go as a game. This study adds to the growing body of knowledge in the field of digital health, gaming and medication adherence. individual/self-treatment. The study conducted a qualitative examination on Pokemon Go players to understand their experiences and what kept them playing the games for a longer period. Therefore, the findings from the study and data analysis helped in the development of conceptual framework for promoting medication adherence using sustained gaming strategies and approaches is hereby presented. The results of this study have the potential to contribute to a shared comprehension among practitioners, professionals, and academics in the field of digital health and serious games. Additionally, these findings can serve as a foundation for future research on the long-term viability of digital health through gaming.Item Assessment of the inland wetland ecosystem types in South Africa: threats and protection(2025-04) Van Deventer, Heidi; Nel, JLEcosystem threat status (ETS) and ecosystem protection levels (EPLs) are headline indicators that can assess freshwater ecosystems at a country-wide scale. A spatial layer of freshwater, inland wetland ecosystem types of South Africa was combined with a range of spatial data sets to model their ecological condition. The ETS and EPL of each ecosystem type were determined using the area of that type in good ecological condition relative to a biodiversity target, which represented 20% of the total area of that ecosystem type. Thresholds were applied to distinguish four ETS categories ranging from Least Concern to Critically Endangered, and four EPL categories ranging from Not Protected to Well Protected. A total of 79% of the 135 of South African inland wetland ecosystem types were found to be threatened, of which 83 (62% of the number of types) are Critically Endangered, 12 (9%) are Endangered, 12 (9%) are Vulnerable and 28 (21%) of Least Concern. Of the 135 inland wetland types, 61% were Not Protected, with 6% being Well Protected, 3% Moderately Protected, and 30% Poorly Protected. Protected and Ramsar sites hosted only 7% of the total area of inland wetlands, which means that the Aichi Biodiversity Target 11 for 2020 (17%) was not met.Item Characterization of ZrC-V-Ti-ZrC multilayer hydrogen storage thin films prepared by e-beam evaporator(2025-05) Rampai, MM; Mtshali, CB; Nemukula, E; Seroka, Ntalane S; Khotseng, LIn this study, a physical deposition method was used to prepare a ZrC–V–Ti–ZrC multi-layered stack film that was deposited on Ti and borosilicate glass substrates. The hydrogenation was achieved by thermal annealing of samples at temperatures of 200, 300, 400, and 550 °C in a pure hydrogen environment with a flow rate of 100 sccm for 30 min. RBS revealed that the multilayers are thermally stable, showing no sign of intermixing of layers up to 600 °C. It revealed the presence of oxygen in all the layers with a significant amount. ERDA revealed that a significant amount of H was near the surface and dropped towards the bulk of the samples, which is the middle layers (V and Ti layers) location. The probing towards the inner last layer (buried ZrC layer) of the multilayer stack showed an increase in the H amount detected. H amount decreased as the oxygen amount was increased in the layers indicating the negative impact of oxygen in the system, such that the total H amount in the samples with the TiO (1:1) and VO (1:1) was 99.122 at.% at 200 °C while that of Ti2O3 (2:3) and V2O3 (2:3) was 60.016 at.% at 300 °C indicating a significant change. The optimum temperature for the highest H amount observed was found to be between 200 °C and 300 °C. The as-deposited sample only showed the surface H, which is normally due to the atmosphere's hydrocarbons. The Raman spectroscopy results indicated that there was a significant decrease in the intensity of the D and G peaks due to annealing in a hydrogen environment. This suggests that the extent of hydrogen absorption, which occurs predominantly in the temperature range of 200–300 °C, is inversely related to the intensity of the D and G peaks. There was more formation of the sp3 at temperatures between 200 °C and 400 °C in the samples as seen by the decrease in the sp2/sp3 ratio from 0.13 to 0.003. XRD revealed the presence of diffraction phases, i.e., ZrC (111), ZrC (400), V2O5 (001), Ti (100), Ti (101), and Ti (103) in addition to the TiH2 and the broadening of peaks for the system annealed at 200 °C and 300 °C due the high H amount, which is consistent with ERDA results. These results indicate the suitability of this system in hydrogen storage applications, provided it is optimized by eliminating oxygen contamination.Item Analytical analysis of the beam propagation factor of elegant Hermite-Gaussian and elegant Laguerre-Gaussian beams with astigmatism(2023-09) Mabena, Chemist MThe impact of astigmatism on the beam propagation factor (M2) of elegant Hermite–Gaussian and elegant Laguerre–Gaussian beams is examined. We derive closed-form expressions for M2 when the optical beams are aberrated with astigmatism. The analysis shows that the beam radius is crucial to the degree of impact astigmatism has on M2. To this extent, we derive the beam radius that separates the region where the M2 is negligibly affected and the region where it becomes severely affected. For the elegant Laguerre-Gaussian beams, we establish a parameter that determines a set of beams that are impacted equally by astigmatism. The analytical results are validated with numerical simulations.Item Mapping the Orientation and Distribution of Defects for the Natural Casting of 2,4,6-Trinitrotoluene (TNT) in 10kg Anti-tank Landmine Mold(2025-03) Thungatha, Lamla; Nyembe, N; Qhamakwane, Tshepo A; Mahlase, Andrew C; Ngcebesha, Pholisa2,4,6-Trinitrotoluene (TNT) is an explosive that is well known for its stable nature, performance, and reliability. It is used in the military and mining industries as it can be cast into various shapes due to its ease of processing at its melting temperature of 80 to 82°C. It can be processed safely within melting temperature without the risk of thermal and impact-related initiation. Despite these properties, casting defect-free charges of uniform density is challenging. Hence, there is a need for targeted quality control measures and process optimisation to minimise density variations and defect formation in manufacturing. In this work the defects formation is mapped for a 10 kg anti-tank landmine, this is done by melting and casting TNT into a 10 kg anti-tank landmine fibre glass mould without any controlled cooling method. The melting and cooling temperature profiles of the TNT casing process were manually monitored using an infrared camera and the process was simulated using COMSOL Multiphysics. The resulting cast was characterised by Vidisco foXRayzor Digital X-Ray and Irdium-192 (192lr) radioactive source. The findings from this study depicted a dense structure at the mould’s margins compared to the booster centre. The less dense area also showed a high proportion of defects which were attributed to shrinkage during cooling.Item Prediction of the appropriate temperature and pressure for polymer dissolution using machine learning models(2025-02) Dadashi, D; Kaedi, M; Dadashi, P; Ray, Suprakas SThe widespread use of polymer solutions in the chemical industry poses a significant challenge in determining optimal dissolution conditions. Traditionally, researchers have relied on experimental methods to estimate the processing parameters needed to dissolve polymers, often requiring numerous iterations of testing different temperatures and pressures. This approach is both costly and time-consuming. In this study, for the first time, we present a machine learning-based approach to predict the minimum temperature and pressure required for polymer dissolution, correlating molecular weight and chemical structure of both the polymer and solvent and its weight percent. Using a dataset compiled from existing literature, which includes key factors influencing polymer dissolution, we also extracted chemical bond information from the molecular structures of polymer-solvent systems. Six different machine learning algorithms, including linear regression, k-nearest neighbors, regression trees, random forests, multilayer perceptron neural networks, and support vector regression, were employed to develop predictive models. Among these, the Random Forest model achieved the highest accuracy, with R2 values of 0.931 and 0.942 for temperature and pressure predictions, respectively. This novel approach eliminates the need for repetitive experimental testing, offering a more efficient pathway to determining dissolution conditions.