Journal of Engineering in Industrial Research

Abstract In recent times, the synthesis and simulation of perovskite materials have gained significant interest in renewable energy. The diverse metal-halide perovskites are very useful in optoelectronic devices. However, lead-based perovskites are toxic, unstable, and vulnerable to degradation when exposed to high temperatures, and their instability is the major restriction for their commercial use. This research paper centered on the first-principles calculations of structural and optoelectronic features of CaAO₃ (A = Ti, Zr, and Hf) by employing the Generalized Gradient Approximation (GGA) Perdew-Burke-Ernzerhof (PBE), and DFT+TB-mBJ exchange-correlation functional. The calculations of the electronic properties revealed that the CaAO₃ (A = Ti, Zr, Hf) has indirect and direct band gaps. To treat the electron-electron interactions, we utilized the PBE exchange-correlation functional, which revealed non-phonon-assisted energy gaps of 2.5 eV, 3.51 eV, and 3.70 eV, and phonon-assisted energy gaps of 1.88 eV, 3.21 eV, and 3.64 eV for CaAO₃ (A = Ti, Zr, and Hf), respectively. These values are consistent with the reported theoretical literature. By employing the DFT+mBJ exchange-correlation functional, the direct band gaps (non-phonon-assisted) of 3.21 eV, 4.70 eV, and 5.21 eV and indirect band gaps (phonon-assisted) of 2.73 eV, 4.24 eV, and 5.03 eV were obtained, which are in excellent agreement with the limited experimental values reported, though with slight underestimation. Moreover, the optical absorption spectra, dielectric constants, and refractive index calculations testify to their applications in optoelectronic devices.

Abstract The annual pattern of diesel pollutant emissions is a significant environmental concern, with key pollutants such as suspended particulate matter, nitrous oxide, sulfur trioxide, sulfur dioxide, and nitrogen oxides impacting human health and the environment. A literature review focused on diesel-emitted pollutants was conducted using online databases. Data on diesel-related air pollution in Iran were collected, examining emissions of various pollutants from 2000 to 2017. Data distribution was analyzed using the Kolmogorov-Smirnov test. Descriptive and correlation analyses were performed using SPSS v27 to explore relationships among greenhouse gases and air pollutant emissions. The study examines the impact of diesel on pollutants and greenhouse gas emissions in Iran from 2000 to 2017. It shows a small decline in diesel's share of NO_x emissions, attributed to better combustion efficiency and emission control systems. In contrast, diesel's share of SO₂ emissions has risen, likely due to increased diesel use despite a shift to natural gas and renewable energy. CO emissions have remained stable due to improved technologies, and CH₄ emissions have significantly dropped due to reduced methane slip and bio-based diesel adoption. The study also indicates a decrease in diesel's contribution to SPM, linked to better fuels and particulate filters. The correlation analysis reveals significant inverse relationships between CO₂ and N₂O emissions and suggests that reducing one type of pollution may affect another.

Abstract This study effectively demonstrated the use of Gliricidia sepium (G. sepium) leaf extract in the formation and characterization of a green-synthesized silver/titanium dioxide (Ag/TiO₂) nanocomposite, as well as its use as an enhanced photoanode material in dye-sensitized solar cells (DSSCs). The production of the silver nanoparticles (AgNPs) was established by UV-Vi’s spectra of the plasmon resonance band, which was detected at around 420 nm. Due to the incorporation of the nanoparticles with TiO₂, the energy gap clearly decreased. The functional groups and their probable contribution to the formation of the nanoparticles and nanocomposite were identified by Fourier Transform Infrared (FTIR) investigations. X-ray Diffraction (XRD) studies revealed that the produced nanoparticles and nanocomposite had average crystallite sizes of 3.69 nm and 4.04 nm, respectively. Spherical, smoother, and more uniform particles were visible in the synthesized nanocomposite's Scanning Electron Microscopy (SEM) image. The TiO₂ nanocomposite's Energy Dispersive Spectra (EDS) confirmed the existence of Ag and other constituent elements. The creation of nanoparticles and nanocomposite with average particle sizes of 4.14 nm and 5.39 nm was further confirmed by the evaluation of the particles’ Transmission Electron Microscopy (TEM) micrographs. The conductivity of the TiO₂ photoanode was improved by 73.11% after the inclusion of the AgNPs, leading to promising electrical characteristics that indicate its suitability in DSSCs.

Abstract This study presents the design and preliminary validation of a self-sustaining automatic weather station (AWS) that uniquely combines high temporal-resolution data acquisition with embedded short-term forecasting capability. Unlike conventional low-cost AWS units that primarily function as data loggers, the proposed system integrates lightweight neural network and stochastic modeling routines for real-time prediction of atmospheric variables. The compact station incorporates sensors for air temperature, soil temperature, relative humidity, atmospheric pressure, solar radiation, and precipitation, all interfaced with an ATMEGA328 microcontroller and a 24-bit analog-to-digital converter for enhanced measurement accuracy. Continuous off-grid operation is achieved through a solar-rechargeable 12 V battery, while dual data handling- local microSD logging and GSM transmission at 10-minute intervals with redundant fail-safe storage ensures reliability in remote deployments. Simulated outputs for temperature, humidity, pressure, and solar radiation demonstrate realistic diurnal variability, confirming the system’s temporal sensitivity. The novelty of this work lies in embedding forecasting functionality directly within a low-cost, modular AWS platform, something not achieved in previous studies that either provide simple logging capability or rely on infrastructure-heavy systems for prediction. By integrating machine learning–based short-term forecasting with dual-mode redundancy in a solar-powered, field-deployable unit, the system addresses the unique challenges of data-sparse and resource-constrained regions such as the Niger Delta. This makes it a scalable solution for microclimatic monitoring, agricultural decision support, and localized early warning applications. Future efforts will focus on field deployment, long-term performance evaluation, remote web interfacing, and integration with larger meteorological networks.

Abstract  
This study investigated the synthesis of biodiesel from Jatropha curcas seeds using methanol as an esterification agent. The process commenced with the extraction of oil from the seeds through solvent extraction, followed by a comprehensive analysis of the oil’s physicochemical properties to evaluate its suitability for biodiesel production. The extracted oil exhibited favorable properties, including a saponification value of 187.14 mg KOH/g oil NaOH, an acid value of 1.00 mg/L, a peroxide value of 6.600 mg/g, free fatty acid content of 0.990 mg/g, an iodine value of 57.147 mg/g, specific gravity (dimensionless, relative to water at 25 °C) (dimensionless, relative to water at 25 °C) of 0.802, and a density of 0.911 g/cm³. Its liquid state at 25 °C further supports its potential as a viable feedstock. Biodiesel was subsequently synthesized via a catalyzed transesterification reaction and subjected to a quality assessment. The resulting biodiesel displayed a pH of 5.50, a cloud point of 2 °C, a pour point of -2 °C, and a flash point of 147 °C. Additional measured properties include a density of 0.911 g/cm³, specific gravity (dimensionless, relative to water at 25 °C) (dimensionless, relative to water at 25 °C) of 0.802, a saponification value of 187.14 mg KOH/g oil, a peroxide value of 53.1 mg/g NaOH, an acid value of 2.38 mg/L, free fatty acid content of 1.19 mg/g, and an iodine value of 54.247. The close alignment of these values with established biodiesel standards confirms that Jatropha curcas seed oil is a suitable and commercially viable raw material for biodiesel production.

Abstract Copper oxide nanoparticles (CuO NPs) are known for their antimicrobial properties, and adding other elements can improve how well they work and how safe they are. In this study, we made nickel-doped copper oxide (Ni-CuO) nanoparticles using a hydrothermal method and examined their biological effects. We prepared both pure CuO and Ni-doped CuO nanoparticles with 2%, 4%, and 6% nickel at 200°C for 12 hours. We used X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and UV-Vis spectroscopy to analyze the samples. To test antibacterial activity, we used the agar well diffusion method with Escherichia coli, Klebsiella pneumoniae, and Bacillus subtilis. We also checked biocompatibility using hemolysis and brine shrimp cytotoxicity tests. The XRD results showed a monoclinic CuO structure with an average crystallite size of 9.8 nm. Adding nickel lowered the optical band gap from 1.8–2.0 eV to 1.5–1.7 eV. The 6% Ni-doped CuO had the strongest antibacterial effect, showing a 15 mm inhibition zone against B. subtilis at 1500 μg/mL. Hemolytic activity dropped as Nickel content increased, from 78.9% in pure CuO to 59.2% in 6% Ni-doped samples at the same concentration. These findings suggest that nickel doping improves both the antibacterial performance and biocompatibility of CuO nanoparticles, which could make them useful for biomedical applications.