DEPARTMENT OF PHYSICS

The study is focused on the measurement of temperature using fiber optic sensor using an OTDR to measure attenuation

This study examines gully erosion in Uniben, Ovia North East Local Government Area in Benin City, Edo State, Nigeria, using a combination of remote sensing and electrical resistivity approaches. Using the Wenner-Schlumberger array, fourteen profiles were used for 2D Electrical Resistivity Imaging (ERI), and RES2DINV was used to analyze the results. The Inverted 2D Resistivity structure from the study region is used to portray the data in this model in a color-coded manner.The section's vertical scale represents the depths, measured in meters, and its horizontal scale represents the lateral distance. A maximum spread of 200 meters was modelled, and all profiles were examined down to a comparable depth of 39.6 meters. The 2-D resistivity structure
analysis indicates that alluvium, laterite, and clay are present in the first four layers near the surface (12.6 – 31.9 m), and that this presence increases significantly as one descends (31.9 –39.6 m) to suggest that sand (alluvium and Laterite) may be the primary cause of the gully in the area. By integrating geoelectric sections derived from the 2D data, the study delineates the lithostratigraphy of the study area, predominantly identifying sand formations down to a depth of approximately 27 meters.
Moreover, the research includes the application of remote sensing techniques to monitor gully development over time and estimate the extent of gully erosion in the area. It encompasses Digital Elevation Models, as illustrated by heat maps, that employ the Normalized Difference Vegetation Index (NDVI) to evaluate the vulnerability of gully erosion in the studied region. The five zones on the NDVI maps are red, yellow, and green, respectively, denoting places that are very vulnerable to gully erosion, areas that are moderately prone, and areas that are less susceptible. NDVI data were calculated for each season during a four-year period, giving a multi-dimensional picture of the environmental changes in the area.

In this work, the structural, mechanical, and electronic properties of PbSeperovskite materials are investigated in detail ab initio using spin-polarizedDFT, using the Ultra Soft Pseudopotential (USPP) method in the QuantumEspresso(QE)software package, the total energy was calculated and the lattice constantsoptimized using the Perdew-Burke-Ernzerhof (PBE) formulationof theGeneralized Gradient Approximation (GGA). In excellent agreement with previously published theoretical values, thestudyproduced optimized equilibrium lattice parameters, band structures, elasticconstants, and elastic moduli. Additionally, the Density of States (DOS) andbandstructures were analyzed in order to comprehensively study the electricalcharacteristics. The findings support the efficacy of the computational techniques used andofferathorough understanding of the structural, mechanical, and electrical propertiesofPbSe perovskites. These discoveries add to the growing corpus of informationon x perovskite materials and provide insightful information for upcoming studiesandtechnological uses.

This study aimed to investigate subsurface lithological structures at the University of Benin, Benin City, Nigeria, using the 2D electrical resistivity method. Field data was acquired using the Wenner-Alpha configuration, and processed using RES2DINV software to generate 2D resistivity models. The results revealed a multi-layered subsurface structure, typically consisting of an upper layer of alluvium (soil) with varying amounts of clay, underlain by layers of shale and sandstone. The resistivity values ranged from 131 Ωm to 6239 Ωm. Vertical Electrical Sounding (VES) data provided further insights, identifying five distinct layers and providing more accurate depth and thickness information compared to the Wenner array. The study demonstrates the effectiveness of the 2D electrical resistivity method in characterizing subsurface structures and provides valuable information for construction planning and geological assessments in the study area.

Several methods have been employed to calculate the mechanical properties of GaN with reliable results. In this work, we employed the energy strain method from first-principles calculation to review the structural and mechanical properties of GaN. From our results, we observed that there is agreement in some of the elastic constants when compared with literature review.

This project focuses on the design, construction, and experimental analysis of three distinct meter bridges fabricated using aluminum, steel, and copper base plates. The primary objective was to measure and compare the internal resistance of a constantan wire using these three different conductive materials, and to analyze how the type of base metal affects the accuracy, sensitivity, and stability of resistance measurements. The study is based on the fundamental principle of the Wheatstone Bridge, which provides a reliable method for comparing and determining unknown resistances by achieving a state of balance between two arms of an electrical network. The meter bridge, being a modified form of the Wheatstone bridge, was selected due to its simplicity, accuracy, and wide applicability in electrical measurement laboratories. During fabrication, each meter bridge consisted of a one-meter uniform wire mounted on a polished metal base (aluminum, steel, or copper), fitted with thick brass strips, standard resistors, binding posts, and a jockey for variable contact. A Leclanché cell served as the power source, and a center-zero galvanometer was employed to detect the balance point. Constantan was chosen as the test wire due to its negligible temperature coefficient of resistance and high mechanical stability. Experimental readings were taken for various known resistances, and the corresponding balance lengths were recorded. The internal resistance of the constantan wire was computed and analyzed.

Facies prediction refers to the task of determining the type of rock or sediment in a particular area, based on various physical and chemical properties. Machine learning techniques can be used to build predictive models for facies prediction, using data
on the characteristics of different rock types and the corresponding measurements made at various locations. These models can then be used to make predictions about the facies of new, previously unseen locations. There are several benefits to using machine learning for facies prediction. One benefit is that the models can be trained on large amounts of data, allowing them to make highly accurate predictions. Additionally, machine learning models can be updated as new data becomes available, enabling them to improve over time.

This study investigates the subsurface stratigraphy of Benin City, Edo State, Nigeria, using 2D Electrical Resistivity Imaging (ERI) techniques. The research aims to delineate geological formations, assess groundwater potential, and map subsurface layers critical for geotechnical and hydrogeological applications. Data were acquired along two traverses using the Wenner-Schlumberger array, revealing distinct layers with resistivity values ranging from 762 Ωm to 9940 Ωm, corresponding to topsoil, dry sand, and compact silty materials.
The analysis identified three subsurface layers, with depths extending to 54.3 meters and lateral extents up to 130 meters. Stratigraphic mapping provided detailed insights into the thickness, composition, and distribution of geological units. The results demonstrated a strong correlation between resistivity values and known geological formations, enabling the identification of aquifers and their potential water-bearing properties. These findings are crucial for urban planning, infrastructure development, and environmental management.
This study underscores the efficacy of ERI techniques in subsurface investigations, offering a reliable approach for mapping stratigraphy and evaluating groundwater resources. The integration of resistivity data with geotechnical insights provides a comprehensive framework for sustainable development in urban areas, ensuring better decision-making for future infrastructure and environmental projects.

The purpose of this project is to pevent a theoretical model (by I. Hamitonian for the thickness of the active region of the Quantum Well Solar Cell Wafer. The model is quite rich in parameters that can be tuned to get desired results.

The face-centered cubic (FCC) crystal structure is one of the most significant arrangements in materials science, particularly in metals such as aluminum, copper, and gold. This research explores the crystallographic arrangement of FCC atoms into distinct planes, emphasizing their geometric configuration, atomic packing, and the significance of close-packed structures. The study provides an in-depth analysis of Miller indices to describe the most prominent planes in FCC lattices, including the {111}, {110}, and {100} planes.