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Item Embargo Activation of the p53 pathway in combination with photon irradiation for the treatment of neurological tumour cells(2026-05-19) Maluleka, Musa; Nemangwele, Fhulufhelo; Fisher, Randall; Engelbrecht-Roberts, MoniqueMedulloblastoma (MB) and glioblastoma (GB) are highly aggressive brain tumours for which treatment outcomes remain poor, particularly due to intrinsic and acquired resistance to radiotherapy. Molecular determinants, especially TP53 status, play a critical role in regulating tumour cell proliferation, cell-cycle control, and DNA damage response following irradiation. This study investigated the biological effects of the MDM2 inhibitor AMG232 in combination with photon irradiation in TP53- wild-type and TP53-mutant MB and GB cell lines, with the aim of assessing whether AMG232 enhances radiosensitivity in a TP53-dependent manner. Cell proliferation, cell-cycle distribution, and DNA damage were assessed using growth assays, flow cytometry, and H2AX foci analysis, respectively. The findings showed that TP53 status strongly influenced cellular responses to treatment. TP53-wild-type cell lines demonstrated clearer growth control following AMG232 treatment, consistent with activation of functional p53 signalling. In contrast, TP53- mutant cell lines showed slower growth, inconsistent cell-cycle regulation, and weaker responses to AMG232, indicating limited recovery of p53 function. Cell-cycle analysis revealed that AMG232 induced a stronger and more sustained G0/G1 arrest in TP53-wild-type cells, supporting activation of the canonical p53–p21 axis. TP53-mutant cells displayed only partial or transient G0/G1 accumulation, suggesting the involvement of p53-independent stress responses rather than effective checkpoint enforcement. H2AX foci analysis confirmed a dose-dependent induction of DNA DSBs following photon irradiation across all cell lines. AMG232 treatment was associated with increased persistence of H2AX foci, particularly in MB cell lines, indicating impaired or delayed DNA repair. Residual foci at later time points reflected the predominance of error-prone non-homologous end joining, especially in G0/G1-arrested cells. In GB cell lines, DNA repair efficiency remained limited irrespective of treatment, highlighting intrinsic radioresistance. This study demonstrates that AMG232 enhances radiosensitivity primarily by prolonging DNA damage signalling and reducing DNA repair capacity, with effects that are most pronounced in TP53-wild-type cell lines. These findings highlight the importance of TP53 status in determining the therapeutic efficacy of MDM2 inhibition combined with photon irradiation and support the potential for molecularly guided treatment strategies in aggressive brain tumours.Item Embargo Automation of the γ-ray spectrometry setup of the Environmental Radioactive Laboratory at NRF-iThemba LABS(2026-05-19) Maluleke, Vuako; Nemangwele, Fhulufhelo; Nkadimeng, Edward K.; Ndabeni, Ntombizikhona B.Environmental γ-ray spectrometry plays a critical role in radioactivity monitoring, radiation protection, and nuclear safety assessments. Conventional spectrometric analysis relies heavily on manual peak identification and expert interpretation, which can be time-consuming and subjective, particularly when dealing with complex environmental samples and varying measurement geometries. This thesis presents the development of an automated and physics-inspired machine learning framework for γ-ray spectrometry, aimed at improving the accuracy, efficiency, and robustness of radionuclide identification at the Environmental Radioactivity Laboratory (ERL) of NRF-iThemba LABS. The primary objective of this research was to integrate domain-specific knowledge from nuclear spectroscopy with advanced machine learning techniques to enable reliable automated analysis of γ-ray spectra. To achieve this, γ-ray spectral data were acquired from five selected radionuclides under controlled experimental conditions, including different counting geometries and known activity concentrations. The resulting dataset captured both the statistical and physical characteristics of detector responses, providing a solid foundation for model training and evaluation. Data preprocessing, feature handling, and visualization were carried out using Python and ROOT, ensuring consistency and reproducibility throughout the analysis pipeline. Two physics-inspired deep learning models, namely Convolutional Neural Networks (CNNs) and Kolmogorov-Arnold Networks (KANs), were developed and optimized for γ-ray spectral classification. These architectures were specifically designed to extract meaningful spectral features by exploiting the physical structure of γ-ray interactions, including peak shapes, Compton continua, and energy-dependent detector responses. By embedding physical intuition into the learning process, the models demonstrated strong generalization capabilities when exposed to previously unseen spectra. The performance of the proposed deep learning models was systematically compared with traditional machine learning algorithms, including k-Nearest Neighbours, Artificial Neural Networks, Support Vector Machines, Random Forests, Decision Trees, and AdaBoost. Evaluation metrics such as accuracy, recall, and area under the receiver operating characteristic curve revealed that the CNN and KAN models consistently outperformed conventional approaches across all radionuclides and geometries. Traditional algorithms exhibited limitations in handling spectral complexity and variability, underscoring the advantage of deep learning methods for high-dimensional nuclear spectroscopy data. To facilitate practical deployment, a Gradio-based interactive dashboard was developed, enabling real-time γ-ray spectrometry analysis. The dashboard allows users to upload spectra and receive immediate radionuclide identification results, along with visual feedback on spectral features and model confidence. This interface enhances accessibility and operational efficiency, bridging the gap between advanced machine learning models and routine laboratory workflows. Overall, this research demonstrates that physics-inspired deep learning provides a powerful and reliable approach to automated γ-ray spectrometry. The proposed framework represents a significant advancement in environmental radioactivity analysis and establishes a foundation for future extensions involving additional radionuclides, higher-resolution detectors, and adaptive learning strategies for real-world monitoring applications.Item Embargo Machine Learning Applications in Blockchain for Renewable Energy Systems(2026-05-19) Nemakonde, Pfano; Nemangwele, F.; Ratshitanga, M.The transition towards decentralized renewable energy systems offers a critical solution to the "Energy Trilemma," yet its practical implementation in emerging economies such as South Africa is obstructed by grid instability, inaccurate demand planning, and the lack of secure local market mechanisms. This thesis addresses the "Deployment Feasibility Gap" in Peer-to-Peer (P2P) energy trading by establishing a synergistic framework that integrates advanced Machine Learning (ML) forecasting with Distributed Ledger Technology (DLT). The research first investigates the limits of predictive accuracy for community microgrids. A novel hybrid deep learning model, Bidirectional Long-Short-Term-Memory with Gated Recurrent Unit (BiLSTM-GRU), is developed for regional solar irradiance forecasting, while a rigorous comparative analysis of ensemble methods such as eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest is conducted for residual demand. To optimize these models, the study contrasts bio-inspired Swarm Intelligence, Honey Badger Algorithm (HBA), Particle Swarm Optimization (PSO) with probabilistic Gaussian Process Bayesian Optimization, and Heteroscedastic Evolutionary Bayesian Optimization (GP-BO, HEBO). Results demonstrate that the HBA-optimized XGBoost model, when coupled with robust feature scaling, achieves superior predictive fidelity, significantly reducing Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) in volatile grid conditions. To operationalize these forecasts, the study proposes "GreenGrids," a P2P trading architecture built on the Hedera Hashgraph network. This Proof-of-Concept validates the technical and economic viability of using high-throughput, low-latency DLT for micro-energy transactions, overcoming the scalability limitations of traditional blockchains. Synthesizing these technical findings with a critique of the South African regulatory landscape, the study culminates in the Deployment Feasibility Framework (DFF). This four-pillared framework offers a comprehensive blueprint for implementing sustainable, community-level energy markets, bridging the gap between theoretical computational models and real-world socioeconomic applications.Item Embargo Investigation of Pd-Ti and Ni-Ti Multilayer Thin Films for Enhanced Hydrogen Storage Capacity(2026-05-19) Nemukula, Enos; Nemangwele, Fhulufhelo; Mtshali, Christopher B.The development of compact and secure storage solutions for hydrogen in solid-state materials presents significant challenges and demands. High-capacity storage technologies that operate effectively at low pressures and exhibit favourable kinetics in absorption and desorption are essential for hydrogen storage solutions. The slow kinetics of hydrogen adsorption and desorption present limitations in metal hydride storage. This investigation utilised advanced materials, specifically palladium-coated and nickel-coated metals, to explore potential enhancements in hydrogen adsorption and desorption. Palladium and nickel act as adsorption catalysts, thereby enhancing the kinetics of hydrogen diffusion into metal interstitial sites. This study examined the multilayers of Pd-Ti and Ni-Ti, which were synthesised and evaluated for hydrogen storage and the kinetics of absorption and desorption. The Pd/Ti/Pd/Ti and Ni/Ti/Ni/Ti multilayers were fabricated using an e-beam evaporator and annealed at different temperatures. Rutherford backscattering spectroscopy confirmed the formation of multilayers, consisting of pure palladium and nickel layers. The titanium layers in both systems exhibited a significant amount of oxygen contamination (up to 63 at.% in the Pd-based system and 61 at.% in the Ni-based system), which was picked from the deposition chamber as residual gases. Hydrogen profiling performed at iThemba LABS revealed a strong temperature dependence of the hydrogen absorption in the multilayers. For both the Pd-Ti and the Ni-Ti based systems, the hydrogen absorption peaked at 200 ◦C. For the Pd-Ti system, the average hydrogen concentration was 3.72 at.% and a total concentration of 51.34 at.%, while Ni-Ti multilayers showed a maximum absorption of 2.58 at.% and a total hydrogen uptake of 46.42 at.%. The hydrogen absorption declined at elevated temperatures, which was likely due to hydrogen embrittlement and structural degradation. X-ray diffraction confirmed the formation of titanium hydrides and tracked the phase transformation with temperature. Atomic force microscopy revealed changes in the surface roughness and morphology. The surface roughness showed the structural response to the hydrogenation temperature. The root mean square roughness for both samples showed a correlation with the total hydrogen content absorbed.Item Embargo Multi-scale electrochemical-thermal modelling of a sodium-ion battery cell using the SPMe with a lumped thermal model(2026-05-19) Patel, SanaThe escalating global demand for energy has raised questions regarding the sustainability, availability, and viability of energy sources. Consequently, renewable energy resources have gained significant attention owing to their environmental safety and abundant availability. However, owing to seasonal fluctuations in renewable sources, it is necessary to integrate efficient energy storage systems that can store energy during peak production hours and supply it when needed. For several decades, lithium-ion batteries have served as the primary energy storage technology for portable electronic devices and electric vehicles (EVs). However, their economic cost and geographical concentration have intensified interest in sodium-ion batteries (SiBs). Among various SiB chemistries, the Na3V2(PO4)2F3 (NVPF) cathode paired with a hard carbon (HC) anode is a promising electrode pair owing to its structural stability and high operating voltage, making it suitable for EV applications. This study investigated the electrochemical-thermal behavior of an NVPF/HC sodium-ion cell using a multiscale modelling framework based on the Single Particle Model with electrolyte (SPMe) coupled with a lumped thermal model and implemented in PyBaMM. The simulations were conducted across C-rates, ranging from C/15 to 3C and temperatures from -20 °C to 55 °C, to analyze voltage profiles, capacity retention, thermal response, and volumetric energy-power trade-offs. The model predicted a maximum discharge capacity of ~2.75 mAh at the lowest rate of C/15 across most operating temperatures. Capacity loss increased with increasing C-rate, delivering ~ 2.11 mAh at 1C, ~1.374 mAh at 2C, and ~0.94 mAh at 3C. At 55 °C ,the cell retained ~76 % of its capacity at 1C, ~51 % at 2C, and only ~42 % at 3C. The thermal effects were minimal at low C-rates but became significant at high rates, resulting in an increased overpotential. The volumetric Ragone plot showed energy and power densities at both low and high rates, and an optimal balance was observed at moderate discharge rates (i.e., C/2 to 1C). The electrode potential profiles indicated that the NVPF cathode dominated the initial discharge voltage, whereas at high currents, the potential rise of the HC anode limited the performance of the sodium-ion cell in EVs. This study provides guidance for battery engineers to understand the influence of the C-rate and temperature on sodium-ion batteries.Item Embargo Predicting Gas Sensor Materials Using Machine Learning(2026-05-19) Shandukani, Kharavho; Maluta, N. E.; Dima, R. S.; Ranwaha, T. S.Gas sensor devices are employed globally by industries and individuals for the purpose of air quality monitoring and the detection of harmful gases. These sensors are important in the identification and tracking of gases that pose significant threats to human life. The mechanism of these devices is based on the electrode material within the device, which includes its interaction with other gases. Despite the wide range of gas sensors available in the market today, they still face challenges such as sensitivity, selectivity, low-temperature operation, and cost-effectiveness. Addressing these limitations necessitates the exploration of novel materials, a process that is often experimentally time-intensive and costly. To mitigate these challenges, this study proposes an innovative approach to accelerate the discovery of materials for gas sensing applications, which constitutes the focus of this research. This study presents a synergy of density functional theory (DFT) and machine learning (ML) to accelerate the discovery of perovskite oxide-based gas-sensing materials. DFT was employed to calculate the adsorption energy, adsorption distance, and adsorption angle. The study focused on LaCoO3(110) surfaces doped with 20 different transition metals. The data generated from DFT calculations were used to train ML models for classification, support vector classifier (SVC), and for regression (random forest regressor (RFR), gradient boosting regressor (GBR), support vector regressor (SVR), k-nearest neighbors (KNN), decision tree regressor (DTR), ridge regressor (RR), and Lasso regressor (Lasso)). The SVC achieved approximately 90% classification accuracy in distinguishing high and low adsorption materials, while GBR and SVR both yielded the highest regression performance ( 𝑅2 ≈0.97), closely reproducing DFT calculation results for the adsorption energy as the target. Explainable AI framework named SHapley Additive exPlanations (SHAP) analysis identified dopant electronegativity, atomic radius, and electron density as dominant factors influencing adsorption strength. The integration of DFT and ML substantially reduces the computational and experimental screening time, accelerating the identification of promising gas-sensing material candidates.Item Embargo Commensal searches for radio transients variables in the data from Mhongoose Large Survey Project(2025-09-05) Tshilengo, Vhuthu Miranda; Woudt, P. A.; Maluta, N. E.The MeerKAT radio telescope in South Africa, a precursor to the mid-frequency component of the Square Kilometre Array (SKA1-Mid), offers exceptional sensitivity and a wide field of view, making it ideal for exploring time-domain radio astronomy. This dissertation presents the use of MeerKAT images from three different fields of the MHONGOOSE Large Survey Project (LSP) to identify radio transient and variable sources. Each of these three fields consists of 10 different epochs spanning time scales of order one month to over 1 year. The MHONGOOSE survey operates at a central observing frequency of 1.28 GHz. I made use of South African Radio Astronomy Observatory (SARAO) Science Data Processing (SDP) images of the MHONGOOSE observations, with source detection and variability analysis conducted via the Transient Pipeline (TraP) software on the Inter-University Institute for Data Intensive Astronomy (IDIA) cloud. The light curves generated by TraP enabled the calculation of variability parameters: the reduced weighted chi-square (χ2) of a fit that assumes constant brightness represented by the symbol (ην) and the coefficient of variation (Vν). Analysis of ten epochs across the fields of NGC 1566, NGC 5068, and NGC 1371 led to the identification of 31 variable sources. All variable sources identified in this study are associated with known objects listed in existing multi-wavelength catalogues and are classified as active galactic nuclei (AGNs). One radio transient was found in the NGC 1566 field “SRC76717”, which is associated with a nearby flaring M dwarf located at a distance of 48 pc (Doyle et al., 2019).Item Embargo Comparative Assessment of Grid-Tied and Isolated PV/Hybrid Energy System for Grid-Connected Facilities: Case of Masia Development Centre and Vuwani Science Centre Energy Systems(2025-09-05) Muronga, Shandukani; Tinawro, D.; Mulaudzi, T. S.The study underscores the significance of a sustainable energy system for national progress, particularly highlighting the increasing adoption of solar photovoltaic (PV) in South Africa. Solar PV integration into the current power generation systems offers financial and ecological benefits, but determining the optimal configuration, especially in areas with an existing utility grid, is challenging. A walk-through energy audit was conducted at the Masia Community Development Centre and the Vuwani Science Centre to establish accurate energy demand profiles. Using the Hybrid Optimisation Model for Multiple Energy Resources (HOMER), the study analysed load characteristics for two sites: the Masia Community Development Centre and the Vuwani Science Centre. Results show that grid-connected (GC) systems outperform off-grid (OG) systems in technical and economic terms, as evidenced by their lower net present cost (NPC) and levelized cost of energy (LCOE) values, alongside higher power output. However, GC systems’ reliance on grid electricity, often derived from non-renewable sources, increases greenhouse gas emissions. Among the evaluated configurations, the Photovoltaic-Grid-Converter (PV/Grid/Conv) architecture emerged as the most cost-effective, delivering low NPC, LCOE, and operating costs of R1,852,811.00, R1.63, and R20,878.56, respectively, for Masia, and R2,969,068.00, R1.07, and R83,039.74 for Vuwani. When scaled to Masia’s total facility demand, values reached R15,768,780.00 NPC, R1.90 LCOE, and R365,062.00 operating costs. Despite its efficiency, the PV/Grid/Conv system struggles with nighttime supply. It is vulnerable to load shedding, suggesting the advantage of incorporating PV/Battery/Grid/Converter (PV/Batt/Grid/Conv) systems for enhanced reliability in critical applications. The off-grid (OG) systems remain vital for remote locations where grid extension is impractical, enabling sustainable electrification, reduced fossil fuel dependence, and greater energy independence. This analysis offers valuable guidance for energy planners and system designers, balancing economic performance, environmental impact, and reliability in PV system deployment.Item Embargo Techno-Economic Analysis of Microgrids with Distributed Energy Resources in Rural Limpopo Province, South Africa(2025-09-05) Netshilonwe, Pfesesani Shammah; Nemangwele, Fhulufhelo; Ratshitanga, Mukovhe; Maluta, EricThe United Nations' sustainable energy development portfolio indicates that around 1.3 billion people globally still lack access to grid-based electricity, underscoring the urgent need for sustainable energy solutions. In Sub-Saharan Africa, about 13% of the population faces limited electricity access due to challenging terrains, inadequate energy policies, and insufficient investment. High costs of extending the electrical grid further complicate the issue. Regions with potential for renewable energy resources, such as solar and wind, offer opportunities to improve energy access. In South Africa's Limpopo province, while the electrification rate is 96%, some rural areas remain without electricity due to poor grid infrastructure and unreliable supply caused by load shedding and load reduction. Even where electricity is available, rising energy costs pose a significant burden on economically disadvantaged communities. This deficit of energy supply in rural areas needs attention through microgrid optimisation. This research aims to techno-economically analyse the feasibility of optimising microgrids in rural Limpopo province, focusing on adopting a system with the least net present cost and levelized cost of energy. Three objectives are the main drive to achieve the aim of this research. The first objective is to provide a review of available and potential renewable energy resources in Limpopo province, focusing on their operational status. Currently, solar PV, biomass, and biogas are available, while geothermal, hydropower, and wind are potential resources. The second objective is to analyse the technical and economic aspects of microgrid optimisation to assess its implementation feasibility without hydrogen production. The third objective evaluates the same elements to determine the feasibility of microgrid implementation with hydrogen production. The Herman-Beta method was employed for peak load estimation, while Homer Pro analysed maximum daily consumption, developed load profiles, and simulated microgrid configurations. The analysis comprised two parts: one focused on microgrids without hydrogen production and the other with it. The first part evaluated PV/Grid and PV/BES/Grid configurations to identify the optimal microgrid solution for each region. For the hydrogen production configurations, three types of PV modules (250 W, 375 W, and 500 W) with a 48V, 14.4 kWh lithium battery were tested, including PV/H2/Grid and PV/BES/H2/Grid setups. Microgrid optimisation results without hydrogen production show that the PV/Grid configuration is the most cost-effective option across all areas. For Ga-Masekwa, the LCOE is 2.356 R/kWh with an NPC of R 5.4 M. For Ka-Dzingidzingi, the LCOE is 1.292 R/kWh and NPC R 76 M; for Duthuni, 1.216 R/kWh and R 138.7 M; and for Mookgophong NU, 1.197 R/kWh and R 250.3 M. The findings on microgrids with hydrogen production show that the PV/H2/Grid configuration is the most cost-effective, offering the lowest NPC and LCOE, and a high return on investment. However, producing green hydrogen requires significant energy, increasing the overall system cost. Conducting a techno-economic analysis of microgrids with distributed energy resources is essential for assessing their feasibility, sustainability, and cost-effectiveness. This study aids in cost-benefit evaluations, system optimisation, financial risk assessments, and the development of resilient alternative energy systems.Item Embargo First principle study of Nax[TiyZnzMnw]O2 as a cathode material for sodium-ion batteries(2025-09-05) Ranwaha, Tshifhiwa Steven; Maluta, N. E.; Maphanga, R. R.The fast-growing energy generation from renewable sources such as solar, wind, and waves is calling for reliable energy storage technologies with high energy density, high power, and low cost, because the energy harvested from these renewable energy sources is intermittent. Currently, the leading technology in energy storage is the lithium-ion battery (LIB), While lithium possesses numerous electrochemical advantages that make it a critical component in modern energy storage technologies, its continued viability is increasingly challenged by the rapid depletion of accessible lithium reserves and its uneven geographical distribution, which pose significant constraints on sustainable and equitable resource utilization. The development of electric vehicles and plug-in hybrid electric vehicles has raised everybody’s expectations as well as requirements for the materials employed. That is why there is an urgency to find alternative technologies which would replace LIBs. In search of alternative technology, sodium-ion batteries are a promising solution for large-scale electrochemical energy storage, owing to their low cost, materials abundance, good reversibility, and decent energy density. For sodium-ion batteries to achieve comparable performance to current lithium-ion batteries, significant improvements are still required in cathode, anode, and electrolyte materials. In this study, first- principles method based on the density functional theory was used to investigate the structural, electronic, mechanical and thermodynamics properties of Na intercalated electrode material NaxMnO2 electrode materials doped with Titanium (Ti) and Zinc (Zn) using random substitution doping method. The investigation was based on the effect of Na atom de-intercalation on the 2 X 1 X 1 NaxMn0.5Ti0.5O2 and 2 X 1 X 1 NaxMn 0.5Zn 0.5O2 supercells. The effects of dopants Ti and Zn on the NaXMnO2 stretch the lattice v parameters, resulting in volume expansion, this is because the atomic radii of the dopants are not the same as those of the host Mn. The electronic properties of the two doped systems show that the band gap is reduced by the effect of the dopants. The calculated elastic constants for the NaxMn0.5Ti0.5O2 and NaxMn0.5Zn0.5O2 bulk structures, as well as the NaxMn0.5Ti0.5O2 and NaxMn0.5Zn0.5O2 supercells, indicate mechanical stability for this compound as they meet the monoclinic structure mechanical stability criterion. In further investigation, the voltage window for the Ti-doped system was found to be between 3.410 V and 4.132 V. We found the voltage window for the Zn-doped system to lie between 2.221 V and 4.337 V. The calculated formation energies are negative, indicating that the material is thermodynamically stable and potentially amenable to synthesis under standard conditions. This inherent stability, coupled with favorable electrochemical characteristics such as appropriate voltage profiles, sufficient capacity, and adequate ionic conductivity, positions the material as a promising candidate for cathode applications in sodium-ion batteries. Furthermore, the cluster expansion formalism was used to investigate the NaxMnTiO2 and NaxMnZnO2 phase stabilities. The method determines stable multi-component crystal structures and ranks metastable structures by the enthalpy of formation while maintaining the predictive power and accuracy of first-principles density functional methods. The findings predict that all nickel-doped LMO structures on the ground state line are most likely stable. Relevant structures are NaMnO2, NaTiO2, NaTiMn2O2 and NaTi2MnO2 for NaxMnTiO2 CE-predicted structures and NaMnO2, NaZnO2, Na3Mn2ZnO6, Na6MnZn5O12, Na6Mn2Zn4O12, Na2MnZnO4 and Na5Mn4ZnO10 for NaxMnZnO2 CE-predicted structures. They were selected based on how well they weighed the cross-validation score (CVs) of 1.7 meV for NaxMnTiO2 CE-predicted structures and 1.9 meV NaxMnZnO2 CE-predicted structures, which is a statistical way of describing how good the cluster expansion is at predicting the energy of each stable structure. Although the structures have different symmetries and space groups, they were further investigated by calculating the structural, electronic, mechanical, and thermodynamical properties. The results show that all CE-predicted structures have a wide diffusion compared to the parent structure (NaMnO2). The reduction of band gap was also observed which give evidence that the structures are becoming metals and have an improved conductivity. The results showed that all the predicted structures met the stability requirements for monoclinic structures and were stable in terms of thermodynamics. For Ti-doped systems, the ductility was only observed on NaTiMn2O2 CE-predicted structure NaMnO2 doped with Zn found to be ductile which implies that these materials can bend without deformation, resulting in fewer cracks during battery operation. This study enhances the fundamental understanding of dopant-induced effects on NaMnO₂-based cathode materials, providing a prospective option to improve Na⁺ mobility, electrical conductivity, and structural stability. It presents a comprehensive analysis of the beneficial effects of Ti and Zn doping in the enhancement of sodium-ion battery performance. It provides the theoretical framework that underpins the development of advanced, cost-effective, economical, and thermally stable cathode materials which are crucial large-scale energy storage applications.Item Open Access Multiscale modeling of sodium-iron battery materials(2025-05-16) Dima, Ratshilumela Steve; Maphanga, R. R.; Maluta, E. N.In recent years, there has been a growing interest in alternative energy storage technologies as a result of the diminishing reserves of fossil fuels. The development of these technologies requires a careful evaluation of factors such as energy storage and conversion, implementation costs, and environmental impact. Rechargeable batteries are expected to become crucial energy storage devices and promote a more sustainable energy ecosystem. Battery technology has the potential to become cost competitive, especially for portable applications, and exhibits exceptional efficiency, exceeding 90% in electrical efficiency. Sodium ion batteries are considered to be cost-effective and economically feasible alternatives. This work used multiscale computer modelling techniques to understand, control, and improve the intrinsic properties of NaxMnPO4, an electrode material that undergoes Na intercalation and de-intercalation processes. This work aims to promote a more sustainable energy ecosystem. Firstly, we examine the structural and electrochemical performance of NaxMnPO4 using the first-principle density functional theory method. Comparison of the exchange correlation functionals PBE, PBEsol, and PBE+U was conducted, and the results showed that the PBE+U replicated the structural parameters and the energy band gap values well and was used to further analyse the electrochemical performance of the de-intercalated systems. The effect of Na atom de-intercalation on the structural, electronic, mechanical, and thermodynamic properties of both maricite and olivine polymorphs of NaMnPO4 has been investigated by first-principle calculations. The calculated values for the formation energy were found to be negative for all NaMnPO4 systems, hence the solid solution is predicted for states of de-intercalation. The analysis of the electronic density of states indicated that, during the Na removal stages, the material exhibited a rise in its metallic properties between the first and third stages. On the contrary, in the fourth stage, the material displayed semiconductor behaviour, characterised by a band gap of 0.194 eV. A voltage range of 3.997 to 3.848 V was observed, and the computed formation energy values of the de-intercalated systems were determined to be negative, indicating the anticipated presence of a solid in the material. Secondly, the ab initio molecular dynamics method was used to simulate the dynamic properties of NaxMnPO4 materials at different temperatures. The results showed an increasing mean-square displacement gradient as the number of de-intercalated Na atoms increased. The Na-ion diffusion coefficients for olivine and maricite NaMnPO4 were calculated at 100 K and 300 K. Both polymorphs had low diffusion rates at 100 K but increased at 300 K, suggesting faster ion movement. These findings are crucial for understanding the behavior of NaxMnPO4 materials and their potential applications, as diffusion rates can affect processes such as charge / discharge rates in batteries and ion transport in solid-state electrolytes. Controlling temperature and understanding its influence on diffusion coefficients can optimize the performance of NaxMnPO4 materials. Lastly, the cluster expansion (CE) method was introduced as a multiscale pipelining method, establishing a connection between first-principles calculation and large-scale atomistic simulations, as well as Monte Carlo simulation. CE was used to examine the phase stabilities of Na concentrations in relation to vacancies. The stability of the predicted structures on the isotopically optimized volume binary diagram was assessed by calculating their mechanical, electronic, and dynamic properties. Structures that underwent isotropic volume optimisation yielded a cross-validation score of 1.1 meV. This score suggests that the cluster expansion is of good quality, as it falls below the threshold of 5 meV per active position. Based on the analysis of the electronic structure, it is observed that both parent structures (MnPO4 and NaMnPO4) exhibit semiconducting behaviour, while the remaining structures (Na1MnPO4, Na0.825MnPO4, Na0.75MnPO4, Na0.625MnPO4, and Na0.25MnPO4) have semi-metallic characteristics. The mechanical stability of NaMnPO4 was shown by the estimated elastic constants, since the stability conditions were met for all intercalated systems, except for the parent structure MnPO4. Based on the Pugh criterion pertaining to the properties of ductility and brittleness, the structures of Na1MnPO4, Na0.825MnPO4, Na0.75MnPO4, Na0.625MnPO4, and Na0.25MnPO4 exhibit ductile characteristics, while the structures of Na0.5MnPO4 and MnPO4 display brittleness. In addition, MD simulations were performed, revealing that the mean square displacement slope is influenced by the concentration of sodium ions, whereas the diffusion coefficients of sodium ions are influenced by the temperature. These findings suggest that the addition of sodium ions improves the ductility of Na1-xMnPO4 structures. The higher concentration of sodium ions leads to increased ductility, as evidenced by the ductile characteristics observed in Na1MnPO4 and Na0.825MnPO4. However, as the concentration of sodium ions decreases, the structures become more brittle, as seen in Na0.5MnPO4 and MnPO4. Furthermore, the MD simulations indicate that the movement of sodium ions within the structures is influenced by both the concentration of sodium ions and the temperature, highlighting the complex relationship between the composition and mechanical properties in these materials.Item Open Access Understanding the properties of the interface between graphene and transition metal oxide thin films using first principle approaches(2025-05-16) Phuthu, Lutendo; Maluta, Eric; Maphanga, RapelaRecently, carbonaceous nanomaterials such as carbon nanotubes and two-dimensional graphene have attracted the attention of the scienti c community in probes to improve energy conversion and storage technologies. The graphene sheet is preferred due to its large speci c area, exible structure, high transparency, excellent mobility of charge carriers and is expected to be able to slow the charge recombination. Graphene/transition metal oxides nanocomposite study has become much of a wide interest recently with metal oxides like TiO2, ZnO, SnO2, etc. These metal oxides are used as thin lms in photovoltaic technology to harness energy. The nal composite embodies both the transport properties of the former and the semiconducting properties of the latter species. This work describes an analysis of the electronic and optical properties of the nal composite studied using the Density Functional Theory (DFT) in application to dye-sensitized solar cells (DSSCs). The study aims to slow charge recombination in DSSCs and improve the e ciency of the cell. The geometry optimizations for the electronic and optical properties were performed by the rst principle calculations based on density functional theory. Various supercells of graphene were modelled and, optimized and their properties were calculated. The results show that different graphene supercells have di erent electronic and optical properties. When graphene is incorporated into a brookite TiO2, the composite results show a reduced energy band gap compared to that of a brookite TiO2 without a graphene on it. The optical properties showed graphene/TiO2 increases absorption in the infrared region.Item Open Access The adsorption of bidens pilosa dye molecules onto TiO2 nanoparicle surfaces for optimization of light harvesting efficiency in dye sensitized solor cell: an experimental and theory study(2024-09-06) Randela, Ronel Ronella; Maluta, N. E.; Mathomu, L. M.; Maphanga, R. R.The availability and high demand for electrical energy is a key global concern, as a result, Dye sensitized solar cells (DSSCs) have attracted a lot of attention in recent years due to their ease of preparation, low toxicity, and environmental friendliness. The current study describes the green synthesis of TiO2 nanoparticles as well as their characterization using ultraviolet-visible, Fourier transformed infrared spectroscopy and X-ray diffraction. Furthermore, the study used Density Functional Theory to describe the optical characteristics of produced nanoparticles. The UV-Vis results showed that the dye extracted using solvents such as water, methanol, and ethanol had a common absorbance at 665 nm among the solvents used ethanol had the highest absorption. The molecules responsible for a broader range of absorbance are known to be pheophytin and porphyrin, which are found in chlorophyll extracted from the B. pilosa plant. FTIR analysis of the prepared TiO2 revealed the absorbed functional groups of the synthesized B. pilosa extracts and confirmed the formation of TiO2 NPs with a vibrational band at 497 cm 1. The TiO2 NPs were heterogeneous in shape under TEM and SEM but spherical under SEM, indicating the formation of paste during agglomeration. XRD analysis confirmed that the polymorph formed is anatase with the highest peak of (101) surface, which was used to computationally adsorb the dye molecule. Pheophytin and porphyrin characteristics were optimized using DFT. For both experimentally and computationally, the UV-vis absorbance was found to be between 420 nm and 665 nm with a higher light harvesting efficiency. pheophytin and porphyrin exhibited energy gaps of 2.1 eV and 2.8 eV respectively. This study demonstrates that the dye molecule synthesized from B. pilosa is an efficient sensitizer for DSSCs. The adsorption results substantiate the spontaneous electron injection and subsequent efficient regeneration of oxidized dye molecules and the strong binding ability of porphyrin dye molecules to the TiO2 surface. The results of this study will be useful for the development of highly efficient organic dyes for DSSCs.Item Open Access Studies on structural, electronic and optical properties of SnO2 doped with nitrogen, chloeine, antimony and indium(2024-09-06) Nekhwevha, Nditsheni; Maluta, N. E.; Maphanga, R. R.SnO2 has recently attracted a great deal of interest due to its many technological applications, including in solar cells as it possesses advantageous optical and electrical characteristics, outstanding chemical stability, and thermal stability. However, the photocatalytic activity and charge carrier mobility are constrained by the large band gap. A cost-effective and efficient method for reducing the SnO2 band gap and increasing the potential for photocatalytic applications is doping with different elements. Examining how mono-doping and co-doping impact the electronic, structural, electrical, and optical characteristics of the SnO2 supercell structure, the current theoretical study used Density Functional Theory (DFT) calculations of different metal and nonmetals (N, Cl, In, and Sb) and (N-Cl and In-Sb) as dopants and co-dopants, respectively. The results show that due to the band gap narrowing and the existence of impurity levels in the band gap, all mono-doped and co-doped SnO2 exhibit some small redshift. The results of the trials and the calculated optical characteristics, such as the dielectric function, reflectivity, absorption coefficient, and energy-loss spectrum, are in good agreement. According to the predicted absorption coefficient, doped SnO2 has a noticeable band of absorption. Doped SnO2 exhibits superior absorption in the visible area of the electromagnetic spectrum than undoped, In-doped, Sb-doped, and In-Sb co-doped SnO2.Item Open Access Investigation of covariability between energy fluxes and CO2 exchange over a semi-arid savanna (Kruger National Park) by Eddie Covariance Technique(2024-09-06) Takalani, Lufuno; Mulaudzi, T. S.; Maluta, N. E.; Mateyisi, M.; Thenga, H.South Africa faces climate change, natural disasters, and rising temperatures due to increased levels of carbon dioxide in the atmosphere, primarily caused by deforestation, burning fossil fuels, and releasing carbon dioxide into the atmosphere without additional carbon sinks. The gap in understanding lies in studying the connection between energy flows and Net Ecosystem Exchange (NEE) at the semi-arid savanna of Kruger National Park to gain a more detailed and accurate understanding of these processes, especially in semi-arid savannas that are susceptible to changes in environmental factors. By studying energy fluxes and NEE at Kruger National Park using the eddy covariance technique, the dissertation seeks to deepen our understanding of the mechanisms driving carbon exchange in semi-arid savannas and provide insights into the impact of environmental factors on ecosystem processes. The eddy covariance technique is a powerful tool that directly measures energy and carbon dioxide exchange between the land surface and the atmosphere. The study shows that the correlation between NEE and latent heat flux (LE) and net radiation (Rn) is generally the strongest, while ground heat flux (G) and sensible heat flux (H) have little impact on NEE. The dataset provides insight into the biometeorological and flow dynamics of the Skukuza ecosystem and how it responds to climate change. The study emphasizes the importance of considering seasonality, climatic variability, and precipitation when studying the surface energy balance and its components. The findings have implications for understanding the complex interactions between ecosystem processes and environmental factors.Item Open Access Comparison and evaluation of empirical and machine learning models in estimating global solar radiation in Limpopo province(2023-10-05) Murida, Thalukanyo Witney; Mulaudzi, T. S.; Maluta, N. E.; Mphephu, N.This study investigated the performance of machine learning techniques as compared to the empirical models to forecast the global solar radiation in Limpopo regions. The machine learning techniques used in this study are Support Vector Machines, Random Forest, and Artificial Neural Network, and the empirical models used are the Clemence and Hargreaves- Samani models. To assess the efficiences of the machine learning models against the empirical models, the researchers calculated and compared the models performance evaluation using statistical equations such as Coefficient of determination, Mean Square Error, Mean Absolute Error, and Root Mean Square Error. Calibaration was done to improve performance of the empirical models. The present study found that machine learning techniques perform better than the empirical models when estimating the global solar radiation in the selected Limpopo regions.Item Open Access Forecasting Minute Averaged Solar Irradiance Using Machine Learning for Solar Collector Applications(2023-05-19) Nemalili, Ronewa Collen; Jhamba, I.; Kirui, J. K.; Sigauke, C.Challenges in utilising fossil fuels for generating energy call for the use of renewable energy. This study focuses on modelling and forecasting solar energy and optimum tilt angle of solar energy acceptance using historical time series data collected from one of the South African radiometric stations, USAid Venda station in Limpopo province. In the study we carried out a comparative analysis of Random Forest and Bayesian linear regression in short-term forecasting of global horizontal irradiance (GHI). To compare the predictive accuracy of the models, k-Nearest Neighbors (KNN) and Long short-term memory (LSTM) are used as benchmark models. The top two models with the best performances were then used in hourly forecasting of optimum tilt angles for harvesting solar energy. The performance measures such as MAE, MSE, and RMSE were used and the results showed RF to have better performance in forecasting GHI than other models, followed by the LSTM and the third best model was the KNN whereas the BLR was the least performing model. RF and LSTM were then used in modelling and forecasting the tilt angles of optimal solar energy acceptance and as thus, the LSTM outperformed the RF by a small margin.Item Open Access First-principles study of Hematite (α-Fe2O3) surface structures doped with Copper (Cu), Titanium (Ti), nickel (Ni) and manganese (Mn)(2023-05-19) Mabaso, Clarence Vusi; Maluta, N. E.; Maphanga, R. R.Hematite has attracted research interest for many years due to its application in water splitting. Despite its attractive characters such as a reasonable optical band gap, the semiconductor is still faced with great uncertainty for the accomplishment of hematite based photoelectrochemical cells for water splitting. Doping with transition metals has shown to be a practical solution to overcome some of the limitations faced with hematite by modifying the energy band to improve its photo-electrochemical (PEC) activity. This study explored two surface structures of pure and transition metals (Ti, Cu, Ni and Mn) doped- α-Fe2O3 oriented in the directions (001) and (101). Calculations via the first principle using the density functional theory (DFT) were adopted, the results show that the doping of transition metals in α-Fe2O3 has an effect in modifying both the valence and conduction band edges. Specifically, doping Ti introduces more electrons in the conduction band and fills the unoccupied 3d states, which could improve the rate of charge transportation and likely enhance the electrical conductivity of α-Fe2O3. Doping with Mn, Ni, and Cu has effectively improved the absorption coefficient for α-Fe2O3 (001) and (101) surfaces, in the visible light region. The overall analysis of the results shows an opportunity for a successful photo-electrochemical water splitting application.Item Open Access Development of a mathematical model for predicting bio-slurry temperature and subsequent gas production rate for underground brick-built biogas digester using ambient air temperature forecast(2022-11-10) Nekhubvi, Vhutshilo 1st Mountaineer; Tinarwo, David; von Blottnitz, HarroBackground: Heat energy is essential for the anaerobic digestion of organic materials such as household, human or agricultural waste. Many developing countries have witnessed efforts to implement anaerobic digestion technology for biogas production as a strategy to enhance energy supply and poverty eradication in rural communities. Underground, brick, and mortar built fixed dome type digesters are the most deployed small-scale biogas technology in sub-Saharan Africa (SSA) countries such as Rwanda, Ethiopia, Tanzania, Kenya, Uganda, Burkino Faso, Cameroon, Benin, Senegal, and South Africa despite their relatively high initial costs. They have a long lifespan and no moving or rusting parts involved. The basic design is compact, saves space, is well insulated, and does not need additional heating, hence suitable for developing countries. The technology is labour-intensive that involves digging the pit and constructing the structure from underground, thus creating local employment. Unlike prefabricated biogas digesters, underground, brick, and mortar-built fixed dome type digesters are more robust than the latter, with minimal gas pipes corrosion experienced. However, little literature on this type of digesters' actual field operation and performance within the SSA context is available. The end-user must know what needs to be done and what the system's outcome is supposed to be. Besides determining parameters like total solids, volatile solids, carbon-nitrogen ratio, hydrolysis rate, organic loading rate, and hydraulic retention time, the temperature inside the digester becomes one of the metrics to evaluate the anaerobic digestion process. The digestion temperature critically affects the biogas yield, considering all other conditions unchanged. Knowing the operational temperature, one can estimate the maximum specific growth rate of the microorganisms and the biogas production rate. Prediction models for the internal operating temperature of these digesters under local conditions typical of Limpopo province of South Africa, where most of these digesters have been installed, are still lacking. To ordinary users in rural areas, the prediction of the possible 'duration of use,' for example, the duration of continuous cooking, is essential. However, regardless of fulfilling all other operational requirements to predict daily gas production, internal digester temperature remains the missing link to having a complete set for a quick and easy gas yield estimation. Aim of the study: The overall objective was to develop a locally applicable model for predicting the bio-slurry operating temperature of underground brick-built domestic size biogas digesters. The work established a correlation of ambient air temperature with the slurry temperature inside the digester using a heat transfer mechanism through the media between the fermenting slurry and the ambient air. Methodology: A thermodynamic study of a small-scale fixed-dome Deenbandhu biogas digester model was performed by monitoring the digester's temperature and surroundings. The K-type chromium-nickel temperature sensors with a sensitivity of 41 μV/°C and a response time of 0.8 s in liquids were positioned at the centre of the digester to measure the slurry temperature. Another temperature sensor was placed 2.0 m above the ground to measure ambient air temperature. The sensors were connected to the data logger and programmed to record temperature readings every second, automatically averaged hourly and daily. The soil surface heat flux was computed using Fourier's law of heat conduction to strengthen the model. Results: The average daily bio-slurry temperature of the digesters ranged between psychrophilic and mesophilic ranges. The results show a strong correlation between bio-slurry and ambient air temperature. A strong correlation was obtained between the measured and predicted temperature of the fermenting slurry inside the digester with a ()Pr|t|>value less than 2e-16 ***, showing that the model is most significant. A Q-Q plot was also used to measure the importance of each observation to the regression. Conclusion: The developed models can accurately estimate the bio-slurry temperature inside the digester using local ambient air temperature data. The set equation adds value as input to the research of small-scale household biogas digesters. Furthermore, the biogas production rate was calculated using data on predicted slurry temperature. It was found that the biogas production rate is satisfactory, given the condition of the study area. The biogas production rate varies from as low as 0.18 m3m-3d-1 during the cold month to 0.48 m3m-3d-1 during the warmest month. Temperatures above 20 ℃ were more conducive to a high biogas production rate.Item Open Access Determination of global solar radiation using temperature-based model for different climate conditions for Limpopo Province of South Africa(2022-07-15) Mathebe, Sampie Mphagala; Maluta, N. E.; Mulaudzi, T. S.The research mainly focused on the determination of global solar radiation using temperature-based model by Hargreaves and Samani for the Northern regions of Limpopo Province of South Africa. The daily maximum and minimum temperature data measured at the following six (6) stations were used: Ammondale, Mutale, Nwanedi, Roedtan, Sekgosese and Xikundu for the period 2008 – 2010. The values of empirical coefficient Kr for the Inland stations of South Africa were computed and used as an input to the model. The observed and calculated global solar radiation data were compared on the basis of the statistical error tests that is mean bias error (MBE), the mean percentage error (MPE) and the root mean square error (RMSE). Based on the statistical results the model was found suitable to estimate monthly average daily global solar radiation for the regions listed above and elsewhere with similar climatic conditions and areas where the radiation data is missing or unavailable. Hence, the study will also help to advance the state of knowledge of global solar radiation to the point where it has applications in the estimation of monthly average daily global solar radiation across.