FACULTY OF PHYSICAL SCIENCE

Crude oil spills in Nigeria poison farmland, killing plants and microbes while adding dangerous hydrocarbons and heavy metals that hurt crops and human health. This study tests a low-cost, eco-friendly fix using fermented whey from guinea grass (Panicum maximum) leaves to boost soil microbes that eat oil. The aim was to see how well this whey cleans oil-polluted soil by cutting total petroleum hydrocarbons (TPH) and restoring soil quality. The scope included collecting leaves (Panicum maximum) from University of Benin sports complex, processing the leaves into whey and fermenting the whey at various periods for twelve days. The whey samples were further characterized to further determine pH, electrical conductivity, moisture, organic matter, carbon, nitrate, phosphate, nitrogen, phosphorus, potassium, and microbial counts to evaluate the whey with the best potential with respect to fermentation time—day 12 with pH 5.74, EC 4804 µS/cm, TOC 67.27%, nitrate 394.17 mg/kg, and high microbial growth (819 CFU/ml at 10⁻¹). Bioremediation potential of the whey was evaluated by treating agricultural soil. The day-12 whey results showed that soil treated with 200 ml was best, raising pH from 5.21 to 7.19, nitrogen from 19.10 to 27.18 mg/kg, phosphorus from 12.65 to 14.98 mg/kg, potassium from 6.23 to 12.45 mg/kg. TPH results showed that hexatriacontane rose to 1.17mg/L increased in the treated soil compared to the untreated contaminated soil, indicating its formation as a biodegradation intermediate during the breakdown of heavier hydrocarbons. Hexatriacontane showed a significant reduction to 0.05mg/L after whey treatment, demonstrating effective microbial attack on heavy hydrocarbon fractions. Heavy metals analysis indicates a significant reduction in metal concentration as in iron from 45.67mg/kg to 32.45mg/kg, zinc 18.90 mg/kg to 12.34 mg/kg copper 7.56 mg/kg to 5.12 mg/kg. Day 12 fermented Panicum maximum whey gave the best remediation potential.

The phytochemical, proximate, and mineral composition of the mixed solvents extract of the leaves of Ficus capensis collected from university of Benin, Edo state was investigated. The parameters investigated were determined using standard biochemical methods. Phytochemical studies carried out on mixed ethanol and methanol extract of Ficus capensis leaves showed high levels of flavonoids, terpenoids, tannins and alkaloids while glycosides, saponins, steroids were in trace amount, with no anthraquinone present. The proximate analysis of the leaves revealed that Ficus capensis leaves has a high carbohydrate content of (36.45%), moisture content of (20.22%), crude fiber (20.41) significant amount of crude protein (15.70%), lipids (4.24%) and very low amount ash content of (2.98%). This composition shows that the sample could be a good source of carbohydrate, moisture, and fiber. The mineral content showed the presence of zinc, calcium, magnesium, phosphorus, potassium and iron content. This indicates the tendency of Ficus capensis to be able to control osmotic balance, essential for bone formation, lower blood pressure and also act a source of antioxidant vitamins and minerals. Additionally, the pH, electrical conductivity of the soil, and Thin Layer Chromatography (TLC) analysis of the leaf extracts were conducted. In which the TLC was used to identify the component present in the
extracts. The presence of these phytochemicals and minerals in this leafy vegetable supports the use of the leaves of Ficus capensis for food and ethno medicinal purposes in many parts of Nigeria and across the globe.

This study is aimed at determining the Caffeine content in two varieties of kolanuts,cola acuminata (red kola)and cola nitida(white kola). The Samples were obtained from New Benin market,Edo state. The extraction of Caffeine was done using solvent extraction method and infra-red spectroscopy was used to detect the functional groups present in the Caffeine isolated. The percentage yield for the cola nitida was 0.04% while that of cola acuminata was 0.06%. the I.R band observed at 1646.24cm-1 (C=C) stretch of Alkene, 1700.93cm-1 (C=O) stretch of amide and 3437.36cm-1 (N-H) stretch of amine among others for the cola nitida (white kolanuts) and 1647.25cm-1 (C=C) stretch of
alkenes,1700.54(C=0) stretch of amide and 3442.11cm-1 (N-H) stretch of amine among others for the Cola Acuminata (red kolanuts). The values of these bands suggest the presence of amine groups which confirms the presence of alkaloids (Caffeine). From this research, the caffeine content for the Samples was determined and found that the red kolanuts (cola acuminata) contains more caffeine compared to the white kolanuts (cola nitida).

This research project provides a solution which would help enable the ease of navigating through a new campus environment as every year there are new students in every university. In this study, I highlighted the importance of the campus navigation system using University Of Benin, ugbowo, as a case study. Agile methodology was adopted for this project, and a few data collection method was adopted. This study evolved into a feasible navigation system, which would help ease the confusion that comes with entering a new university campus

The idea of a library has moved beyond the physical world and found a new home in the increasingly digital era. The developing environment of digital libraries and their significant influence on knowledge transmission, preservation, and accessibility are explored in this abstract. Digital libraries
act as repositories for enormous and varied informational collections, encompassing anything from scientific research and classic literature to multimedia archives and cultural heritage. They enable people to conveniently access information at any time and from any location, democratizing knowledge in ways that were previously unthinkable. Digital libraries also have a significant impact on the preservation of delicate and endangered artifacts, protecting cultural treasures and priceless historical records. Their comprehensive metadata systems and archiving methods make it possible to permanently preserve knowledge for future generations.

Semi-conductor thin films of copper-sulphide (CuS), were successfully deposited on glass substrates and room temperature, using improved solution growth technique (SGT) at different bath concentration. Absorbance spectra(A) data were measured by a
T80+UV/VIS Double beam spectrometer was used to obtain the spectra absorbance data, while the other optical and solid-state properties of the films were obtained by calculations based on theory. The average optical and solid-state properties include:
Absorbance(A)=0.001-0.322, Transmittance(T)=0.476-1.002, Reflectance (R)=-0.002- 0.202, Absorbing power(a)=-0.002-0.742, Refractive index(n)=0.914-2.629. Average solid-state properties are, Film Thickness(t)=0.476-1.002 and band gap (Eg)= 1.34- 3.47eV. It is observed that the films have n>1.9, these films could find useful applications as:
(i) Construction of poultry house, (ii) Solar cells, (iii) anti-dazzling coatings. The deposited films with band gap (Eg) within light energy range (-1.3-3.5 eV) of electromagnetic spectrum could find application in solar electricity for rural electrification to improve the standard of living, Telecommunication, Remote monitoring and control system.

This project was centered on card based security system. The current process of security is being operated manually and due to this procedure numerous problem are been encountered. A design was taken to computerized the manual process in order to check this problem. The problems were identified after series of interviews and examination of documents after which analysis was made and a computerized procedure recommended. This project will also suggest how to successfully implement the computerized procedure
and to overcome the obstacle that would hinder the successful implementation of the system. The new system was designed using Microsoft visual basic 6.0 programming language. This language was chosen because of its easy syntax and features for developing windows based applications.

This paper presents a comprehensive comparative search analysis of three prominent generative AI models: ChatGPT, Gemini, and Perplexity. By focusing on their architecture, performance, training processes, and real-world applications, we provide a detailed case study that highlights the strengths and limitations of each model in various natural language processing (NLP) tasks. The analysis covers aspects such as model scalability, accuracy, response time, and adaptability across different domains. Through systematic benchmarking and evaluation of these models on both factual and creative prompts, we explore their potential to revolutionize industries such as education, customer service, and content generation. This study also aims to inform future developments in generative AI by identifying gaps and opportunities for improvement in model design and training methodologies.

This research gives attention to the non-energy use of Lignite samples collected from Ohordua town of Edo State and Azagba-Ogwashi in Delta State. A total of six composite Lignite samples,from ten sampled points were subjected to analytical study, with a view to determining their humic content and properties for use in soil beneficiation and conditioning.Proximate and Ultimate analytical technique was used to characterize the lignite from which humic acid was extracted under alkaline condition, and precipitated using acidic medium of 6M HCl.X-Ray Diffraction and X-Ray Fluorescence analytical methods were employed to determine the mineralogical and elemental composition of the samples respectively,while Ultra-Violent visible spectroscopy was used for organic characterization of the humic acid.Proximate analyses of the Lignite show percentage composition of Ohordua and Azagba-Ogwashi Lignite respectively,Fixed Carbon (65.22 wt% and 67.16wt%),Moisture Content (26 wt% and 25.4wt%) and Ash (5.02 wt% and 4.5 wt%).Ultimate analyses shows Hydrogen (6.60 wt% and 6.22wt%), Nitrogen (1.34 wt% and 1.19wt%,) Sulphur (0.77 wt% and 0.70wt%), Oxygen (26.07 wt% and 24.73 wt%), for Ohordua and Azagba-Ogwashi respectively.The analytical results suggest the existing lignite with varying thickness is of Lignite rank. The Ultimate analyses of the extracted Humic acid show Carbon (57.5wt% and 55.8wt%)%, Hydrogen (5.1 wt% and 3.9wt%), Nitrogen (2.5 wt% and 1.1wt%), Sulphur (Nil and 0.3wt%) and Oxygen (34.9 wt% and 38.9wt%,) for Ohordua and Azagba-Ogwashi, respectively.The mineralogical composition in the lignite samples shows Kaolinite was the main clay mineral identified. Non clay minerals such as anorthite, drierite, yeelimite, spurite, belite, wollastonite, hematite, pyrite, bauxite and quartz were detected.For Ultra-Violent visible spectroscopy characterization of the Humic Acid, E2/E3 and E4/E6 absorption values for Ohordua (21.69 and 4.50) respectively, while Azagba-Ogwashi reveal (19.39 and 4.6) respectively. The humic substance has percentage aromaticity values of 33.90% and 34.06%, total acidity has 10.4% and 10.1%, COOH has 3.4% and 3.3%, Phenolic has 7.0% and 6.8% and acidity ratio has 0.49% and 0.48%, for Ohordua and Azagba-Ogwashi, respectively which depicts the Humic Acid has a high degree of Humification and a very good total acidity ratio. Results from this research indicate that the Lignite from both locations are similar in geochemistry, mineralogy and organic properties. Although,humification is high in both samples, Ohordua Lignite with lesser aromaticity and E4/E6 ratio, higher E2/E3 ratio and higher acidity ratio is more suitable for soil beneficiation and conditioning than that of Azagba-Ogwashi.

This project focuses on developing an intelligent system for heart disease prediction usingAdaptiveNeuro-Fuzzy Inference Systems (ANFIS) and Genetic Algorithm (GA). Cardiovascular diseaseisaleading cause of mortality worldwide, necessitating improved diagnostic tools. The proposedsystem leverages the strengths of neural networks and fuzzy logic to handle the complexitiesanduncertainties inherent in medical data. The ANFIS model is optimized using GAto enhancetheaccuracy of predictions. The system was tested on the UCI Cleveland heart disease dataset, achieving a satisfactory accuracy rate of 91.25%. This indicates the potential of the systemtoassistclinicians in making reliable diagnoses based on a combination of key medical attributes. Futurework may involve extending the system to diagnose other complex diseases and incorporatingadditional heuristic algorithms to further refine model performance