Tshwane University of Technology

The adoption of lithium-ion batteries (LIBs) in electric vehicle (EV) propulsion has highlighted their exceptional properties, including light weight, high-energy storage capability, long life cycle, high performance, and eco-friendliness. LIBs store energy through electrochemical reactions, and recent research has focused on SiO 2 as a potential anode electrode material due to its higher theoretical specific capacity. However, challenges such as poor electrical conductivity and high volume expansion have hindered its real-world applications. Current research is focused on developing SiO 2 -based composite anodes to overcome these challenges. This study aims to explore innovative strategies for improving the electrochemical performance of SiO 2 -based materials and understanding their lithium storage mechanism and degradation behavior. Additionally, the study delves into the effect of prelithiation on the discharge efficiency and long-term cycling performance of SiO 2 -based composite anodes. Finally, it highlights current bottlenecks and presents ways to address them for further improvement.

Multi‐principal element alloys (MPEAs) have emerged as a transformative class of materials with exceptional mechanical, thermal, and chemical properties, making them promising candidates for energy applications. This mini‐review explores the role of MPEAs in energy systems, focusing on their potential for high‐temperature energy applications, hydrogen storage, fuel cells, thermoelectric energy conversion, and advanced battery technologies. The unique combination of configurational entropy, phase stability, and corrosion resistance in MPEAs offers significant advantages over conventional materials in extreme environments. Despite their promising properties, challenges such as compositional optimization, processing scalability, and cost remain key areas for future research. This short communication provides insights into the advancements, opportunities, and future directions for the utilization of MPEAs in next‐generation energy systems.

During the synthesis of dyes, desalination of high-salinity dye-containing waste liquor is a critical premise for high-quality, clean dye production. Conventional membrane processes, such as electrodialysis, nanofiltration and ultrafiltration, are inevitably subjected to serious membrane fouling, deteriorating the dye/salt fractionation efficacy. Integrating the technical merits of electrodialysis and pressure-driven membrane separation, we devise an electro-driven filtration process using a tight ultrafiltration membrane as alternative to conventional anion exchange membrane for rapid anion transfer, in view of dye desalination and purification. By employing a sub-4 nanometer tight ultrafiltration membrane as anion conducting membrane, the electro-driven filtration process achieves 98.15% desalination efficiency and 99.66% dye recovery for one-step fractionation of reactive dye and NaCl salt, markedly outperforming the system using commercial anion exchange membranes. Notably, the electro-driven filtration system displays a consistently high and stable fractionation performance for dyes and salts with unprecedentedly low membrane fouling through an eight-cycle continuous operation. Our results demonstrate that the electro-driven filtration process using nanoporous membranes as high-performance anion conducting membranes shows a critical potential in fractionation of organic dyes and inorganic salts, unlocking the proof of concept of nanoporous membranes in electro-driven application.

The integration of smartboards in South African classrooms, particularly in under-resourced township schools, has been championed as an innovative solution to improve teaching and learning outcomes. This study explores the application of smartboards in Grade 12 classrooms at a secondary school in Soshanguve, Gauteng Province. Despite significant investments in technological infrastructure, challenges persist in effectively utilising smartboards due to factors such as inadequate teacher training, unreliable infrastructure, and limited professional development. Guided by the Technological Pedagogical and Content Knowledge (TPACK) framework and employing an exploratory case study design within an interpretivist paradigm, this research collected qualitative data from semi-structured interviews with teachers. Findings reveal that while teachers possess basic technological competencies and recognise the pedagogical potential of smartboards, their practical application is constrained by infrastructural limitations and insufficient support mechanisms. Recommendations include targeted professional development, reliable technological maintenance, and the development of context-sensitive guidelines to maximise the educational benefits of smartboard integration in township schools.

Maize ( Zea mays L.) is a staple food crop that smallholder farmers mostly cultivate under rain-fed conditions in Southern Africa. Despite significant contributions to food production by smallholder farmers, they face climate change-related challenges such as drought, resulting in crop water stress and significant yield losses. This is exacerbated by the lack of financial resources, mechanical skills, and sound climate change adaptation strategies, increasing the yield gaps. This could potentially be addressed through technological advancements such as precision farming systems. Remote-sensing systems are sufficient and well equipped to address crop production’s complex and technical assessments, such as crop water stress, inexpensively and efficiently. This study sought to systematically review the literature on the progress, emerging gaps, and opportunities in applying remote sensing technologies in quantifying maize water stress. Adhering to the PRISMA guide, 100 peer-reviewed articles were examined from Web of Science, Scopus, Google Scholar, and ScienceDirect. Results significantly increasing research efforts have been exerted from 2002 to the present, with the majority of research articles (37%) being conducted in the United States and the least (12%) in the African continent. Specifically, 17 different Earth observation sensors were used to map maize water stress. Landsat is the most widely utilized sensor, particularly the red and near-infrared regions of the electromagnetic spectrum, along with their derivatives. These Landsat spectral derivatives are used mostly in conjunction with the surface energy model in retrieved literature. However, there is a dearth of literature on remote sensing maize crop water stress in smallholder croplands. This is mainly because these agricultural systems are extremely small (<1 ha) and heterogeneous to be detected by moderate spatial resolution sensors that are freely available. Furthermore, validation mechanisms, data, and fine spatial resolution suitable for these croplands are scanty, if not expensive. Providentially, UAV-based remote sensing technologies, which are relatively cheaper, with ultra-high spatial resolutions, and user-defined acquisition times have emerged as suitable alternatives. In this regard, more research efforts are required to assess the prospects of these technologies, especially in smallholder farms in southern Africa associated with limited resources.

Agriculture accounts for over 70% of freshwater withdrawals globally, yet water and food insecurity remain prevalent. The challenge of water insecurity is compounded by climate change, rapid urbanisation, and the need to increase the agricultural area to produce more crops to meet the growing needs of an increasing population. Improving crop water productivity at the farm level is key to resource security and climate change adaptation and resilience as it enhances the production of more food with less water. This summary chapter provides a brief overview of each chapter in this book, highlighting the pathways to improved crop-water productivity in both rainfed and irrigated cropping systems. Specifically, the major highlight is that water productivity can be enhanced by practicing well-adapted climate-smart crop types that have the potential to reduce unproductive water losses and, at the same time, maintain healthy and suitably adapted crops that optimise water, nutrient, and agronomic management.

Polyfluoroalkyl substances (PFAS) are persistent, bioaccumulative, and toxic compounds that pose significant environmental and health risks. Although PFAS contamination has been extensively studied in high-income countries, there is limited data on its occurrence and impact in low and middle-income countries, particularly in Africa. This study addresses the lack of comprehensive data on PFAS in wastewater treatment plants (WWTP), surface water, and sediments in Pretoria (South Africa), to inform pollution control strategies and health risk assessments. The Apies River in Pretoria, which receives treated effluent from nearby WWTPs, was selected for investigation due to its importance to local communities and potential exposure to PFAS. Samples were collected during the dry season weekly on days 1, 7, 14, and 21, to investigate temporal variations in PFAS concentrations in treated effluent, sediment, and surface water, and analysed for legacy and emerging PFAS. Sampling during the dry season provides an opportunity to detect and quantify PFAS more effectively as they are less likely to be diluted by rainfall. PFAS were recovered from the samples using solid-phase extraction followed by liquid chromatography-tandem mass spectrometry. The results showed that the ΣPFAS concentrations were consistently higher in treated effluent samples over 21 days. Long-chain PFAS concentrations were significantly different (p < 0.05) between upstream and downstream locations. Perfluorocarboxylic acids were consistently found at higher concentrations in treated effluent, surface water, and sediment samples, with surface water (downstream) showing the highest concentration (62.98%). Sediment samples upstream (31.44%) and downstream (29.24%) showed a higher percentage of perfluorosulfonic acids, indicating stronger sediment adsorption. The findings of this study will drive future research and policy development focused on protecting water resources in South Africa’s waterways.

Herewith we present the first detection of Amblyomma lepidum (Dönitz, 1909) on cattle in Zimbabwe. Zimbabwe’s smallholder farmers rely heavily on their livestock, mainly cattle and goats. Even though great importance is placed on cattle farming, no systematic surveillance of tick and tick-borne diseases is currently being conducted in the country. Forming part of the Amblyomma genus, A. lepidum is a brightly ornate tick with great vector potential, which could have harmful effects on cattle and cattle farming in Zimbabwe. This report documents the presence of both male and female A. lepidum ticks that were morphologically identified, and with the use of two ribosomal genes, were molecularly characterised as A. lepidum. Hypotheses can be made as to how this tick was introduced into Zimbabwe; however, its presence highlights the need for a systematic surveillance programme to track not only new introductions, such as the aforementioned, but the current distribution of this tick population in Zimbabwe. Supplementary Information The online version contains supplementary material available at 10.1007/s10493-025-01017-7.

Herein, silica‐supported Au–Ru catalysts with 5% loading for each metal were prepared by microwave‐assisted loading (MW) and deposition (DP) methods. Au–Ru nanoparticles are obtained on MW‐5Au5Ru while short Au–Ru nanochains are obtained on DP‐5Au5Ru. The performance of the catalysts is tested through the oxidation of 2,3,5‐trimethylhydroquinone (TMHQ) and 4‐methoxy‐1‐naphthol (MNL) with H2O2, in which 2,3,5‐trimethyl‐1,4‐benzoquinone (TMBQ) and 4,4′‐dimethoxy‐2,2′‐binaphthalenylidene‐1,1′‐dione (BNP) are produced as main products, respectively. Catalytic data obtained for the oxidation of TMHQ demonstrate that the structures of the catalysts, type of solvent, and reaction temperatures used have a significant influence on the activities and selectivities of the catalysts. When MeOH and MeNO2 are used at room temperature (RT) in the oxidation of TMHQ on MW‐5Au5Ru catalyst, 58.2% and 100% conversions of TMHQ are achieved, respectively. Both MW and DP‐synthesized catalysts are highly active in the oxidation of TMHQ. Similar to TMHQ, the catalytic outcomes on the oxidative coupling of MNL highly depend on the temperature and structure of the catalyst. For example, 34% and 96% conversions of MNL are achieved at RT and 60 °C, respectively, over MW‐5Au5Ru catalyst in MeOH. However, MNL conversion of 82% is achieved on DP‐5Au5Ru catalyst in MeOH at RT.

This review paper provides a comprehensive overview of the heat treatment processes applied to laser additive manufactured medium-entropy alloys (MEAs), focusing on their impact on microstructural evolution and material properties. MEAs, characterized by their high compositional complexity and exceptional mechanical performance, have garnered significant attention in materials science due to their potential for advanced engineering applications. The paper begins by outlining the fundamental concepts of MEAs, including their composition, properties, and applications. It then explores various laser additive manufacturing (LAM) techniques, such as selective laser melting (SLM) and laser metal deposition (LMD), and their associated challenges, including issues related to microstructure, porosity, and residual stresses. A detailed examination of heat treatment processes follows, covering methods such as solutionizing, aging, annealing, and quenching, and their effects on MEAs. The review highlights how these heat treatment techniques influence microstructural changes, such as phase transformations and grain refinement, and their subsequent impact on mechanical properties, including hardness, tensile strength, and ductility. Additionally, the paper addresses the effects of heat treatment on thermal stability and corrosion resistance, emphasizing the importance of optimizing these processes for enhanced performance in various industrial applications. Recent advancements in LAM and heat treatment technologies are discussed, alongside emerging trends and future research directions. Key areas for further investigation include the development of novel heat treatment techniques, the need for standardized testing and evaluation methods, and the exploration of sustainable practices. The review concludes with recommendations for future research efforts aimed at addressing current knowledge gaps and advancing the field of MEAs in both academic and industrial contexts. This review provides valuable insights for researchers, engineers, and industry professionals seeking to optimize the performance of MEAs through effective heat treatment strategies and advanced manufacturing techniques.

The world population continues to increase with a large proportion of this increase happening in Africa just as predicated by Food and Agriculture Organization of the United Nation. To meet the demand of feeding this increasing population, innovative farming practices are being developed to increase the yield of the major stable food supply. This research developed a computer vision and control algorithm on a quadcopter for farm surveillance, crop protection from the invasion of pest, early disease detection as well as crop yield estimation. A process of developing a model capable of detecting and classifying these pests in the environment was presented in this study. The process involves selecting, preprocess and transforming the sensed data from a vision sensor to train classification algorithm and to detect and track object of interest in the environment. This study addresses a notable research gap in computer vision, control systems, big data analytics, and robotics, specifically within the context of smart farming. It primarily utilizes conventional learning algorithms, including Artificial Neural Networks and Support Vector Machines, typically found in widely-used commercial software.

Background of the study The influence of macroeconomic indicators makes it important to study the relationship between macroeconomic indicators and stock market return. On further analysis it can be observed that different sectors respond differently to change in the macroeconomic indicator that is important for investors, researchers and policy makers. Methods The autoregressive distributed lag (ARDL) model is applied to study influence of macroeconomic indicators on sectoral return of NSE from April 2012 to August 2024. Results Findings of the study show that macroeconomic indicators influence sectoral return in the short run as well as long run and the influence is differential. The analysis of long run relationship shows that Foreign Institutional Investment (FII) significantly affects all the sectoral indices except IT. Index of industrial production (IIP) have significant relationship with Auto, IT, Media, Metal and Pharma. Money supply (MS) significantly affects Bank, FMCG and IT in the long run. Wholesale Price Index (WPI) has significant relationship with Auto, FMCG and Media in the long run. Economic Policy Uncertainty Index (EPU) affects Auto, FMCG and Pharma in the long run. Crude oil price (COP) has significant effect only on Media in the long run. Exchange rate (ER) does not have significant effect on any of the sectoral index. Conclusion In the long run FII, IIP, EPU, MS and WIP are major determinants of stock market return. In the short run FII, ER and COP are major determinants of stock market return.

Cloud computing eliminates the need for expensive hardware and software expenditures by revolutionising access to computing resources through internet-based utility services. However, Data Integrity (DI) in this paradigm faces a variety of challenging issues, including complexity, security, privacy, control limits, fallibility of human beings, and financial limitations. The shortcomings of current DI solutions in terms of guaranteeing data verification, preventing replay attacks, and controlling computational overhead have led to an increasing need for access to cloud infrastructures by third-party verifiers. The suggested Cryptographic Accumulator Provable Data Possession with Merkle Hash Tree (CAPDP-MHT) scheme demonstrates significantly improved performance over Provable Data Possession (PDP) and Rivest Shamir Adleman (RSA) algorithms in various domains, as demonstrated by thorough simulation and MATLAB-based evaluation. In particular, CAPDP-MHT outperforms PDP and RSA with an average data verification success rate of 25%, compared to their respective rates of 10% and 5%. Moreover, it identifies replay attacks in about 30 seconds, compared to 45 and 70 seconds for PDP and RSA, respectively. Furthermore, the computational overhead of CAPDP-MHT is about 27 seconds, while that of PDP and RSA is 45 and 60 seconds, respectively. Therefore, as compared to PDP and RSA-based systems, CAPDP-MHT not only exhibits exceptional computing efficiency but also outperforms in reliability.

This research investigates the drug delivery efficacy for 6‐fluoro‐3‐hydroxy‐2‐pyrazinecarboxamide (favipiravir) in PEGylated bionanocomposites using a predictive modeling approach. The study focuses on understanding the interaction mechanisms between favipiravir (FAV) and polyethylene glycol (PEG)/graphene oxide (GO) (GO/PEG) nanosheets across various environmental conditions. To evaluate drug delivery efficacy, the following key parameters were calculated: adsorption energies ranging from −202.61 to −3.46 kcal/mol indicating the strength of binding between the drug and nanocarrier; net charge transfer values between −0.222 and 0.373 electrons, reflecting the degree of charge migration; release times spanning a wide range from 3.4 × 10⁻¹⁴ to 2.38 × 10¹³² ms, which impacts the drug release kinetics; and thermodynamic parameters such as changes in Gibbs free energy (ΔG) between 183.34 and 16.95 kcal/mol, and changes in enthalpy (ΔH) between −203.64 and 0.55 kcal/mol, providing insights into the favorability and spontaneity of the drug‐nanocarrier interactions. The results show that incorporating PEG onto GO nanosheets enhances adsorption energies and binding affinities for FAV. Environmental factors and PEGylation influence the charge transfer and noncovalent interactions. PEGylation leads to faster FAV release kinetics. Favorable thermodynamics are observed, especially in aqueous environments. Electronic properties, quantum descriptors, and theoretical spectra provide further insights into molecular interactions.

Leopard seals (Hydrurga leptonyx) occur mainly south of the Antarctic Polar Front, but immatures, in particular, seasonally move beyond this range during the austral winter and spring, typically under increased sea ice conditions. Extralimital occurrences of leopard seals can be observed at several sub-Antarctic islands where they haul out to rest. We present new records of leopard seal sightings at Marion Island, southern Indian Ocean, from 2006 to 2024 and discuss fluctuations in their seasonal and annual abundance (drawing on data collected since 1980) and body condition based on regular surveys. The eastern beaches at Marion Island were surveyed every 7–10 days while the western beaches were visited monthly. Observed leopard seals were photographed and given a body condition score based on the visibility of bony protrusions. From 2006 to 2024, we identified 35 presumed unique immature leopard seals between July and November, with a peak in September, all being immatures. Individuals to which we could assign body condition scores were either in good or excellent condition. This contrasted with the prevailing hypothesis that leopard seal body condition deteriorates with decreasing latitude. However, we could not determine whether this was because of an actual shift in body condition or because we used a different scoring system from other studies. We recommend adopting a standardised scoring system for visually estimating pinniped body condition and a global repository to monitor leopard seal haul-outs. As an apex predator, leopard seals may be important indicators in Antarctic and sub-Antarctic ecosystems, and monitoring changes in their distribution and body condition may indicate environmental and biological changes in these remote regions.

This review summarizes key features of the genus Duguetia over the past 54 years (1970 to May 2024), focusing on its occurrence, distribution, isolation, and bioactivities. A thorough literature review was performed using databases such as Google Scholar, PubMed, ScienceDirect, SciFinder, and Web of Science. The search utilised the keyword “Duguetia” in combination with relevant terms like “distribution,” “traditional use,” “phytochemicals,” “chemical compounds,” “pharmacology,” and “bioactivity.” The findings indicate that the genus Duguetia, closely related to Keetia and Psydrax, belongs to the Annonaceae family and consists of trees and shrubs. It comprises 95 species, primarily distributed in South and Central America, with four species located in West Africa. Traditionally, plants of the genus Duguetia have been used in medicine to manage kidney colic, stomachaches, rheumatism, coughs, toothaches, fever, muscle pain, digestive issues, and respiratory problems. Chemical investigations have identified 146 secondary metabolites, with alkaloids representing approximately 76% of these compounds, serving as a chemotaxonomic marker for the genus. Other compound classes, including flavonoids, benzoic compounds, sesquiterpenoids, steroids, bisnorlignans, cinnamates, and triterpenoids, account for the remaining 24%. Pharmacological studies of chemical constituents and extracts from Duguetia species have revealed a wide range of bioactivities. These include cytotoxicity, antimalarial, antioxidant, anti-inflammatory, urease inhibition, antifungal, antinociceptive, antibacterial, antitumor, anticancer potential, and insecticidal properties. The structural diversity of alkaloids and the variety of bioactivities position the genus Duguetia as a promising resource for drug discovery and medicinal applications.

Digital out-of-home media continues to attract consumers due to its dynamic and interactive characteristics. Despite the growing interest in the uses and gratifications of contemporary digital media, scholarly inquiry into digital out-of-home media remains limited. This study investigated how digital out-of-home media’s uses and gratifications correlated with user experience and the mediating role of interaction. Employing a quantitative approach, data were collected from 450 shoppers via a structured questionnaire and analysed using Hayes PROCESS macro to test for mediation. Results show that convenient information and entertainment gratification factors significantly influence user experience, with interaction playing a pivotal role. Moreover, interaction statistically mediates the relationship between motivational factors and user experience. However, no statistically significant correlation between process gratifications and affordances was found. These findings highlight the importance of understanding user engagement in digital out-of-home media contexts and offer insights for marketers and researchers in the digital media landscape.

Essential oils are well studied for their antimicrobial effects; however, blends extending to formulations are rarely scientifically explored. In this study, we aimed to quantify and optimise the synergy of an essential oil blend by means of computational interpretation in order to create a nanoemulsion formulation ideal for use against respiratory tract pathogens. The nanoemulsion blend consisted of essential oils from Hyssopus officinalis var. angustifolius in combination with Salvia rosmarinus var. angustifolius. The prediction tool SynergyFinder (Version 2.0) was implemented to determine optimal synergy blends. According to the synergy maps derived, an optimal blend of these two essential oils is composed of 49.57% of H. officinalis and 50.43% of S. rosmarinus. This optimised blend was then formulated into a nanoemulsion, using the two-component, self-emulsification technique. The essential oil nanoemulsion showed strong in vitro antimicrobial activities against pathogens of the respiratory tract including Streptococcus pneumoniae (ATCC 49619), Haemophilus influenzae (ATCC 19418), Klebsiella pneumoniae (ATCC 13883) and Moraxella catarrhalis (ATCC 23246), with an average six-fold improvement in antimicrobial effect when compared to the neat essential oils. The blended H. officinalis and S. rosmarinus essential oil nanoemulsion therefore holds potential to be developed as a natural antimicrobial agent for the management of respiratory tract infections.