FACULTY OF PHYSICAL SCIENCES

Chemical industries increasingly rely on catalytic processes, and more than 90% of large scale chemical transformations depend on catalysts. Some of the commonly used homogeneous catalysts in Fridel Crafts alkylation reaction are; BF3, H2SO4, HF, AlCl3. Though, readily available at low cost, they have several limitations and these include toxicity, difficulty in separation and recovery and disposal problems. These limitations have intensified the demand for sustainable, efficient, and environmentally benign heterogeneous catalysts. This study focuses on the development of metal-doped coconut-shell biochars as robust heterogeneous catalysts for the Friedel Crafts benzylation of toluene. Biochars derived from the coconut shell produced at different pyrolysis temperatures (350, 400, 450, 500, 600 and 700 °C) were characterized in terms of their physicochemical and textural properties, surface oxygen functional groups and surface morphology using standard methods. The coconut shell biochar (CSB) sample with the optimal textural properties (CSB450) was then co-pyrolysed with Fe³⁺, Zn²⁺, and Fe³⁺/Zn²⁺ ions to produce metal doped biochars (Fe³⁺ - doped, Zn²⁺- doped, and Fe³⁺/Zn²⁺-doped biochar). Central Composite Design (CCD) of the Response Surface Methodology (RSM) was employed to optimize the process variables (metal loading, pyrolysis temperature and pyrolysis time) for the co-pyrolysis reaction. Similarly, RSM was used to optimized the process variables (mole ratio of toluene: benzyl chloride (T:BC), reaction temperature and reaction time) on benzyl chloride conversion to benzylated toluenes with the metal doped biochars. RSM-derived optimal conditions resulted in enhanced specific surface areas of 2098.04 m2 .g-1 for the Fe³⁺-doped biochar, 1721.40 m2 .g-1 for the Zn²⁺-doped biochar, and 2124.5 m2 .g-1 for the Fe³⁺/Zn²⁺-doped biochar. Compared with the pristine biochar (CSB450), the metal doped biochars (MBCs) showed improved physicochemical properties and textural properties. In addition, surface elemental analysis confirmed the successful incorporation of the Fe (10.09%), Zn (5.17%) and Fe/Zn (9.44/6.51%) on the MBCs. The results of the conversion process showed that the reaction temperature, mole ratio, and reaction time significantly affected benzyl chloride conversion with the metal doped biochar. Three novel models were developed for benzyl chloride conversion process. From the models, we predicted optimized process conditions; optimum benzyl chloride conversion of 93% was found for Fe3+/Zn2+ - doped biochar, with values of 87 and 81% for the Fe3+ - and Zn2+ - doped biochar respectively. This research established metal doped coconut shell biochar as an effective, low-cost, and environmentally friendly heterogeneous catalysts for organic transformations, presenting a viable alternative to conventional corrosive and non-recyclable catalysts in industrial alkylation processes.

Inverters are electronic devices that convert direct current (DC) into alternating current (AC) and are used in a wide range of applications, including power generation systems, renewable energy systems, and portable electronic devices. The choice of battery for an inverter is an important decision that can impact the performance and efficiency of the inverter. In this project, the aim was to specify the factors that should be considered when choosing a battery for an inverter. To achieve this goal, a literature review on inverters, batteries, and deep cycle batteries, and analyzed the available information on the performance, lifespan, and cost of different types of batteries, was conducted. Based on the review and analysis, a 65AH 24V inverter was chosen to power the inverter as it had little requirements for maintenance and met the require specifications

This project addresses the inefficiencies of managing shuttle services in university campuses. The study aimed to develop operational efficiency and improve passenger experience by leveraging advanced technology. The solution was designed to provide users with accurate arrival times and route information through an intuitive web application, minimizing wait times and increasing satisfaction using a three tier client-server architecture and UML modeling to define clear interactions between Student, Drivers and Administrators. Implementations utilized PHP, XAMPP, and my SQL to deliver a responsive platform. Key functionalities of the system includes real-time tracking, automated scheduling, and effective communication channels for passengers and operators. Digital Shuttle Management System serves as a sustainable transportation solution, aimed at optimizing fuel consumption and reducing manual appointment scheduling system. By integrating these elements, the project facilitates a smarter, more responsive shuttle service tailored to meet the evolving demands of mobility

This study presents a comparative analysis of three major frontend frameworks—React, Vue, and Angular—with the goal of identifying their strengths, weaknesses, and suitability for different types of web development projects. The research adopted a mixed-method approach that combined experimental performance testing with a developer survey. The experimental analysis focused on key performance metrics such as rendering speed, memory usage, CPU utilization, and bundle size, while the survey gathered developers’ perceptions on usability, learning curve, community support, and development efficiency. Findings revealed that no single framework is superior in all aspects. React recorded the fastest initial load time and the highest overall performance score, making it ideal for applications that prioritize quick content delivery. Vue demonstrated the best runtime efficiency, smallest bundle size, and lowest resource consumption, proving effective for lightweight and performance- sensitive projects. Angular, although heavier in resource usage, stood out for its comprehensive structure, built-in tools, and scalability—features that make it highly suitable for large enterprise systems. Survey results showed strong developer satisfaction across all three frameworks, with most respondents acknowledging their active communities, extensive learning resources, and productivity benefits. The study concludes that framework selection should depend on project requirements and team expertise rather than popularity. It recommends React for performancefocused projects, Vue for resource-efficient and adaptable applications, and Angular for large- scale, structured enterprise solutions.

The Greater Ughelli Depobelt within the Niger Delta Basin represents a mature hydrocarbon province where precise lithostratigraphic, biostratigraphic, and source rock characterizations remain critical for mitigating exploration risks. This study presents a comprehensive evaluation of the ABC-1 well, combining sedimentological analysis, microfossil biostratigraphy, and organic geochemistry to reconstruct the paleoenvironment, establish a chronostratigraphic framework, and assess the oil and gas potential. Lithological analysis of ditch cutting samples reveals a classic deltaic succession belonging to the Agbada and Akata Formations, characterized by alternating sequences of sandstones, siltstones, and marine shales. Biostratigraphic dating, utilizing index planktonic foraminifera and palynomorph assemblages, indicates an age range from Late Oligocene to Early Miocene. Identified biozones correspond to specific global chronostratigraphic markers, allowing for the delineation of major maximum flooding surfaces (MFS) and sequence boundaries (SB) that define the structural and stratigraphic traps within the depobelt.

The skin is the largest sensory organ in the human body and is crucial for protection, regulating temperature, and sensory detection. It acts as a complex boundary between the body and the outside world, reacting to electrical impulses, physical forces, and changes in temperature. A thorough understanding of the biophysical characteristics of the skin is essential for advancements in material science, biomedical engineering, dermatology, and biomedicine. This research explores how the mechanical, thermal, and electrical properties of human skin affect its functionality. The study examines skin elasticity, viscoelastic behavior, heat transfer mechanisms, and electrodermal activity through a comprehensive review of existing studies and experimental methods. This research review explored skin elasticity, viscoelastic behavior, heat transfer, and electrodermal activity. Using nanoindentation and stress- strain analysis, it found that skin elasticity varies by body region and age, with Young's modulus decreasing as we age. Creep and stress relaxation tests confirmed the skin's viscoelastic properties. Thermal properties were assessed using infrared thermography, indicating that well-hydrated skin dissipates heat more effectively. The skin's role in thermoregulation is reinforced by studies on vasodilation and sweating. V Electrodermal activity (EDA) and skin impedance spectroscopy reveal that skin conductivity changes with moisture levels and external stimuli, serving as important indicators of emotional and physiological states. This study also explores how sensory nerves convey tactile and thermal sensations to the nervous system. The findings enhance our understanding of the skin's multifunctionality and have potential applications in medical diagnostics, wearable technology, and prosthetics, aligning with existing research in biophysics and dermatology

The concentrations of heavy metals; lead (Pb) and chromium (Cr) in cow offals (liver, kidney, intestine, and tripe "shaki") were investigated. A total of twelve samples were collected from three different abattoirs in Ovia North-East, Benin City, Nigeria: The University of Benin (UNIBEN), Ekosodin, and the University of Benin Teaching Hospital (UBTH), consisting of four cow offals from each location. Sampling was carried out twice at two-week intervals. Heavy metal extraction was performed using the standard wet-acid digestion method, and concentrations were determined via Atomic Absorption Spectrophotometry (AAS).
Results revealed that chromium (Cr) was detected in all samples, with concentrations ranging from 0.04 to 0.19 mg/kg, while lead (Pb) was variably detected, ranging from not detected (ND) to 0.10 mg/kg. The highest Cr concentration (0.19 mg/kg) was recorded in the kidney sample from UNIBEN (UN-K), while the lowest (0.04 mg/kg) was found in the liver sample from UNIBEN (UN-L). For lead, the highest concentration was observed in the kidney sample from Ekosodin (EK-K) at 0.10 mg/kg.
When compared with the Codex Alimentarius Commission/FAO/WHO permissible limits for heavy metals in edible offals; Pb: 0.50 mg/kg, Cr: 0.05 mg/kg some of the measured Cr values, particularly in the second sampling set, exceeded the recommended safety threshold, such as 0.19 mg/kg in UN-K and 0.16 mg/kg in EK-S. These findings indicate potential contamination and raise concerns about food safety and public health, particularly among populations with high offal consumption.
These results underscore the importance of regular monitoring, improved waste and feed management, and regulatory oversight of abattoir practices to limit environmental and food-chain exposure to toxic metals.

Phishing attacks, which use phoney emails and misleading websites to target people and organisations, have emerged as one of the most common cybersecurity threats. These attacks cause serious security breaches by tricking users into divulging private information, including login credentials and financial information. This project offers a machine learning-based phishing detection tool for emails and web browsers that uses logistic regression to efficiently detect and stop phishing threats. To improve the accuracy of phishing detection, the study investigates different feature extraction methods, dataset preprocessing, and model training approaches. Python was used to develop the system, and the Scikit-learn library was used to implement the model. Both authentic and phishing URLs and emails made up the dataset, which was preprocessed using techniques like feature scaling and outlier removal. Key performance metrics, such as accuracy, precision, recall, F1-score, and a confusion matrix, were used to train and assess the model. The outcomes showed that the suggested model was successful in differentiating between phishing and authentic entities, as evidenced by its high classification accuracy. Notwithstanding its encouraging results, the system has certain drawbacks, including the difficulty of balancing false positives and false negatives and vulnerability to new phishing tactics. To further enhance detection capabilities, future studies could use ensemble approaches or sophisticated deep learning models. By offering users and organisations a reliable and effective detection mechanism, this project supports the continuous efforts to strengthen cybersecurity defences against phishing attacks.

This project models the spread of information in social media networks through advanced computational techniques and simulations. In today’s digital age, social media platforms serve as primary channels for information dissemination. As a result, the rate at which information—and misinformation—spreads increases exponentially, leading to significant implications for society. Understanding the dynamics of information spread proves crucial for various applications, including public health messaging, marketing strategies, and efforts to combat misinformation. The project employs graph theory as a foundational framework to represent social networks, allowing for the visualization and analysis of user interactions and relationships. By constructing a model that captures the essential characteristics of these networks, we simulate how information propagates across different nodes and edges within the network. Key components of the study include an examination of the role of influencers— individuals who possess a higher degree of connectivity and significantly accelerate information dissemination. By identifying these pivotal nodes, the model provides insights into how targeted messaging can effectively reach larger audiences. Additionally, the project investigates the impact of network structure on the spread of information. Researchers analyze different configurations of social networks, such as those characterized by high clustering or short path lengths, to determine how these factors influence the rate and extent of information diffusion. Through a series of simulations, this study explores various scenarios, including the effects of strategic interventions, such as promoting specific influencers or modifying the network structure to enhance information flow. Ultimately, the findings from this project aim to contribute to a deeper understanding of information dynamics in social media, offering valuable insights for practitioners and researchers alike in the fields of public health, marketing, and information science. By enhancing our grasp of these dynamics, we can better leverage social media for positive outcomes while mitigating the risks associated with the rapid spread of misinformatio

Kola (Cola nitida) leaves are widely known for their ethnobotanical uses, yet their by- products remain underexplored as sources of bioactive compounds for environmental applications. Fermentation of plant-derived substrates often enhances their physicochemical profile, making them useful in biostimulation processes that support microbial activity for pollutant degradation. Against this background, this study investigated Cola nitida whey, focusing on its extraction, fermentation behavior, and nutrient composition, with the aim of assessing its potential application as a bioremediant. Fresh kola leaves were processed through washing, grinding, boiling, and filtration to obtain whey, which was digested with nitric acid and subjected to physicochemical analysis. Parameters evaluated included pH, electrical conductivity (EC), moisture content, total organic matter (TOM), total organic carbon (TOC), nitrate, phosphate, nitrogen, phosphorus, and potassium. Results showed dynamic changes across the five- week fermentation period. pH decreased initially from 5.29 in Week 1 to 5.18 in Week 3 before rising to 6.01 in Week 5, while EC steadily increased from 1595.50 to 2129.50 µS/cm, reflecting ionic release. Moisture content rose from 92.08% to 94.09%, whereas TOM and TOC increased overall, with TOM ranging from 54.06 to 106.55% and TOC from 31.36 to 61.80%. Nutrient levels indicated progressive mineralization: nitrate rose from 174.16 to 1152.36 mg/kg, phosphate from 411.67 to 971.81 mg/kg, nitrogen from 39.56 to 261.06 mg/kg, and phosphorus from 122.75 to 295.46 mg/kg. Potassium fluctuated but stabilized at 2.55 mg/kg by Week 5. These findings suggest that kola-leaf whey develops enriched organic and mineral content during fermentation, creating a nutrient-rich medium favorable for microbial proliferation. The shift toward near-neutral pH at later stages further supports microbial activity, while the elevated nitrate and phosphate levels highlight its suitability as a low-cost, plant-derived stimulant for bioremediation, although regulated application is recommended to minimize eutrophication risks.