The University of Nigeria, commonly referred to as UNN, is a federal university located in Nsukka, Enugu State, Eastern part of Nigeria. Founded by Nnamdi Azikiwe in 1955 and formally opened on 7 October 1960, the University of Nigeria has three campuses in Enugu State– Nsukka, Enugu, and Ituku-Ozalla – and the Aba campus in Abia State.The University of Nigeria is the first full-fledged indigenous and first autonomous university in Nigeria, modelled upon the American educational system. It was the first land-grant university in Africa and one of the five most reputed universities in Nigeria. The university has 15 Faculties and 102 academic departments. The university offers 108 undergraduate programs and 211 postgraduate programmes.The university celebrated its 50th anniversary in October 2010, and would have celebrated its 60th anniversary save for the COVID-19 pandemic.
High-entropy alloys (HEAs) are multi-principal-element material systems in which five or more elements are combined in approximately equimolar proportions. They have attracted sustained scientific attention because they can unite mechanical, thermal, and chemical properties that surpass those of conventional single-principal-element alloys. Heat treatment is a principal lever for tuning HEA microstructures: annealing, homogenisation, ageing, and solution treatment each alter phase fractions, precipitate morphologies, grain size distributions, and dislocation substructures in ways that ultimately determine suitability for demanding aerospace, energy-generation, or biomedical service conditions. The experimental exploration of this enormous compositional and processing space is, however, prohibitively expensive and time-consuming when pursued through conventional trial-and-error metallurgy alone. The emerging closed-loop paradigm, in which ML models predict microstructural outcomes, active learning selects the next experiment, and automated characterisation closes the feedback loop, represents the critical frontier of HEA heat-treatment science, enabling systematic navigation of composition-processing-microstructure space at throughputs unachievable by purely experimental campaigns. Machine learning (ML) methods including random forests, gradient-boosted trees, artificial neural networks, support vector machines, Gaussian process regressors, graph neural networks, convolutional neural networks, long short-term memory networks, and physics-informed deep learning architectures, offer a fundamentally different paradigm: they extract quantitative structure-property-processing relationships directly from accumulated datasets, then generalise those relationships to compositions and heat treatment schedules that have never been physically tested. This review synthesises the state of the art in ML-assisted prediction of microstructural evolution during the heat treatment of HEAs. We trace the foundational metallurgical concepts governing HEA phase selection and microstructural development, survey the construction and curation of datasets on which ML models are trained, evaluate principal algorithms and their relative merits and shortcomings, and discuss the integration of ML with CALPHAD thermodynamic frameworks and physics-informed constraints. We then identify critical open challenges such as data scarcity, transferability, interpretability, and the treatment of kinetic pathways and propose research directions expected to define the next decade of work in this field. The scope of this review is deliberately bounded: it covers ML methods applied to heat-treatment-induced microstructural evolution, including phase fraction, precipitate morphology, grain size, and sigma-phase formation in HEAs and directly related MPEA systems; coverage of room-temperature mechanical property prediction, corrosion, and coating deposition is included only where it directly informs microstructural evolution modelling. Although Section 10 gives particular attention to surface engineering and high-entropy coating applications, the methods reviewed are equally applicable to bulk structural alloys for aerospace and nuclear service, biomedical implant alloys, hydrogen-storage hydrides, thermoelectric devices, and energy-storage electrodes; this breadth is addressed in Section 10.4. The review encompasses 106 verifiable primary references distributed across 12 sections and 15 original figures.
This work sought to establish whether starting particle size (SPS) is an important parameter on the properties of hydroxyapatite (HAP) extracted from cattle bone (CB) waste material by heat treatment, for biomedical applications, such as temperature and holding time. CB was processed to obtain micron-sized particles of 50, 150, 250 and 350 & micro;m. The powders were heated to a constant temperature and holding time of 900 degrees C and 3 h, respectively, to enable their transformation into HAP. Evolved HAP was characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and Fourier transform infrared spectroscopy (FTIR). Results indicated that physico-chemical properties were very sensitive to SPS of CB. XRD results revealed that crystallite size and degree of crystallinity of evolved HAP increased with greater SPS. Based on EDX results, concentration of essential elements of Ca, P, O and C in HAP also varied according to SPS increasing from 36.0 to 50.5 wt. % for Ca, decreasing from 13.7 to 9.9 wt. % for P, 36.2 to 29.6 wt. % for O and 12.6 to 9.3 wt. % for C. FTIR spectra indicated that concentration of functional groups is sensitive to SPS, with amounts of OH- and PO43- increasing, while CO32- decreased with an increase in SPS. Thus, SPS is an important parameter in evolution of HAP from CB waste via calcination process.
Abstract The accelerated growth of population and the rise in the number of vehicle owners are expected to create a sharp demand in the global market for brake pads, which will exert an acute demand to substitute asbestos-containing materials. Asbestos, which was previously employed in friction composites, is dangerous and does not withstand hot climates; hence, substitutes with safer ones are required. This paper explores the utilization of doum palm shell (DPS) as an agro-waste that contains silica as a reinforcement in asbestos-free brake pads manufactured using phenolic (PR) and epoxy (EP) binders. DPS is biodegradable, economical, and locally available, and it helps in valorizing waste and recovering resources that are sustainable. Graphite, glass fibre, aluminium oxide, and alkali-treated DPS were used to create brake pads. FTIR analysis showed better bonding, and TGA showed better thermal stability in EP and more char yield with better flame resistance in PR. Compressive strengths of EP samples of 47–77 MPa and impact energies of 4.27–7.09 J/m² were obtained in mechanical tests, compared to 70.12–80.20 MPa and 8.75 J/m² in PR samples. Hardness peaked at 95.6 Hv in S1PR. The tribological analysis showed that PR composites had lower wear levels and increased friction coefficients, while EP had high thermal resistance and lower water absorption (0.098%). The best performance was recorded in S2PR with 20 g DPS and 15 g glass fibre, which had a balance between strength and friction. Phenolic binders outperformed epoxy in tribological criteria, while epoxy had greater heat stability. The results reveal that DPS is a potential reinforcement in the manufacturing process of sustainable brake pads. Future research ought to address fade and recovery behaviour, thermo-mechanical coupling, and long-term durability. Graphical Abstract
This study assesses linkages between global uncertainty, human resource development, and economic complexity in 38 African countries between 2010 and 2023. The Driscoll and Kraay fixed-effect standard errors are employed. Global uncertainty unconditionally deters economic complexity. Human resource development positively moderates the impact of global uncertainty on Africa's economic complexity. The empirical results further show that foreign direct investment, infrastructural development, and improved economic performance remain vital for enhancing the region's economic complexity. The corresponding policy implications and recommendations are discussed in alignment with the African Union's Agenda 2063 and Sustainable Development Goals.
This study investigates financial development and economic growth and moderated for the role of national security, environmental sustainability and green bonds in the emerging economies using annual time series data which covered the period of 2000 to 2022, panel differenced and system generalized method of moment (GMM) as the baseline model as well as fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) as the models for robustness checks. Financial development was stratified into financial development-financial institution access index and financial institution depths index and financial markets-measured with financial markets access index and financial markets depths index. Also, national security were measured with military expenditure, global peace index and global terrorism index, environmental sustainability was measured with indicators such as ecological footprint, biodiversity index and renewable energy share, while the green bonds were measured with environmental protection expenditure and carbon footprint of bank loans as we controlled for climate change and foreign remittances. Findings from the GMM results revealed that while financial development and green bonds shows positive and significant effects on the economic growth in the emerging economies, the national security had negative effects, while the environmental sustainability had significant negative and positive effects on the economic growth of the emerging economies and similar findings were made from the results of the robustness checks (FMOLS) and (DOLS). Also, from the results of the interactive effects financial development with national security, environmental sustainability green bonds, we found that the variables had significant effects on the economic growth. In addition, findings from the marginal effects (ME) and threshold effects (TH) results revealed that while financial development improves economic growth, additional effects of national security, environmental sustainability and green bonds may have adverse effects on the economic growth in the emerging economies at a certain threshold. Based on these findings, this study recommends that financial development, national security and environmental sustainability should be improved by the authorities to forestall economic growth in the emerging economies.
