ENERGY

Protein folding is an important process that allows a long chain of amino acids (called a polypeptide) to form a specific three-dimensional shape needed for it to work properly. This project studies how proteins fold using the energy landscape model, which explains folding as a gradual movement toward the most stable and low-energy shape. By reviewing studies from 2020 to 2025 and comparing examples of amyloid-β, prion, and αsynuclein proteins, the research shows how changes in the folding process can cause proteins to fold incorrectly. When this happens, they may form clumps, leading to diseases such as Alzheimer’s and Parkinson’s. The findings show that protein folding is not random it is guided by chemical interactions, helper molecules called chaperones, and how easily different shapes can form. Misfolding occurs when proteins get stuck in the wrong shape, creating harmful structures. Overall, this study helps explain why protein folding is so important to human health. It also shows how understanding the energy landscape model can help scientists find better ways to prevent or treat diseases caused by misfolded proteins.

The global transition towards sustainable energy and the decarbonization of power and transport sectors have positioned Lithium-ion Battery-based Energy Storage Systems (Li￾ion ESS) as a critical technological enabler. Despite their dominance, characterized by high energy density and efficiency, these systems face persistent challenges related to performance trade-offs, safety, economic viability, and sustainability, with distinct implications for developing regions like Sub-Saharan Africa. This study undertakes a comprehensive review of Energy Storage Systems for Lithium-ion Batteries to evaluate recent advancements, identify key challenges, and map future research directions. Employing a systematic qualitative review methodology, this research analyzes and synthesizes findings from peer-reviewed literature, technical reports, and conference proceedings published between 2020 and 2025, sourced from databases including IEEE Xplore, ScienceDirect, and SpringerLink. The analysis is structured around a comparative framework that evaluates dominant lithium-ion chemistries—specifically Lithium Nickel Manganese Cobalt Oxide (NMC), Lithium Iron Phosphate (LFP), and Lithium Nickel Cobalt Aluminum Oxide (NCA)—across key parameters: energy density, safety, cycle life, cost, and application suitability. The review reveals a critical market bifurcation. NMC and NCA chemistries remain preferred for applications demanding high energy density, such as premium electric vehicles. In contrast, LFP has emerged as the superior choice for stationary storage and an increasing segment of the EV market, owing to its exceptional safety, long cycle life, cost-effectiveness, and cobalt-free supply chain. The study further highlights that system￾level components, particularly the Thermal Management System (TMS) and Battery Management System (BMS), are paramount in determining overall ESS performance and lifetime. From a regional perspective, the adoption of Li-ion ESS in Sub-Saharan Africa, and Nigeria specifically, is hampered by infrastructural deficits, reliance on imports, high costs, and an underdeveloped recycling framework. The study concludes that the future of Li-ion ESS lies in application-specific vii optimization, the integration of digital technologies like AI for predictive management, and the development of robust circular economy models. It recommends intensified research into solid-state batteries, the standardization of second-life battery protocols, and the formulation of policies that support local capacity building and sustainable deployment in developing economies to harness the full potential of lithium-ion energy storage for a clean energy future.

This study aimed to design and implement a low-cost microcontroller-based system for monitoring the energy consumption of individual workshop machines, addressing the limitations of conventional centralized metering systems that fail to provide machine- specific data. The literature review examined previous work on energy monitoring technologies, including commercial, open-source, and academic systems, highlighting the growing role of the Internet of Things (IoT) in enabling real-time data acquisition and remote monitoring. It emphasized the need for affordable, scalable, and educationally adaptable solutions for developing regions, where technical expertise and financial resources are limited. The research adopted an experimental design methodology involving hardware and software integration. The system was built using Arduino Nano and ESP32 microcontrollers, ZMPT101B voltage and SCT-013 current sensors, an LCD display, and a ThingSpeak IoT cloud interface. Mathematical modeling was applied to compute voltage, current, power, energy, and cost, while SolidWorks was used for casing design. Calibration and testing were conducted under varying load conditions to assess accuracy, response time, and data stability. Data were logged both locally on an SD card and remotely on the cloud for redundancy and analysis. Results indicated that the system achieved high accuracy within ±1% for voltage and ±5% for current, with an overall efficiency of 95% and IoT data transfer uptime of 98%. The developed prototype successfully provided real-time monitoring, stable performance, and reliable data transmission. The study concluded that the Arduino-based energy monitoring system is a cost-effective, scalable, and efficient solution suitable for educational, domestic,v and small-scale industrial applications. It recommended future enhancements in predictive analytics, multi-machine scalability, and integration with renewable energy management platforms.

The surface free energy of nanoparticles is important as it gives us vital information about the reactivity and stability of nanoparticles. Starting from a previously reported equation, a theoretical model that involves a specific term for calculating the cohesive energy of nanoparticles, is established in a view to describe the surface free energy of metallic nanoparticles ( using different shapes of particle; sphere, cube and disk). The results show that the behaviour of surface free energy for spherical nanoparticles is the most realistic shape compared to disk and cubic shaped nanoparticles. As the surface free energy differs from shape to shape we also see that its value falls as the number of atoms (nanoparticle size) decreases. The results are in close agreement with the results of Fathi and Ayyad (2014).