Indian Journal of Pure & Applied Physics (IJPAP)

Numerical Modelling of High Efficiency ZnO/GaAs Solar Cells Using Triple-Layer Anti-Reflective Coating

Fatinah Najwa Roslan — 2026-02-03

The choice of substrate’s material plays vital role in order to achieve good efficiency. Zinc oxide (ZnO) and gallium arsenide (GaAs) were thus selected as n-type and p-type, respectively, because of their properties that can help generate high current and voltage, which will indirectly result in high efficiency. However, reflectance of light was still occurred due to its design of solar cell. Applying triple layer anti-reflective coating (TLARC) on the top of n-ZnO/p-GaAs solar cell will be study in this paper to reduce the reflectance and maximizing the transmittance. A broad wavelength range of 250 nm to 1200 nm will be used to analyze all nine triplet materials of triple layer anti-reflective coating (TLARC). The efficiency of solar cells will be investigated in this research using Personal Computer 1-Dimensional (PC1D) simulation. The parameters used to simulate PC1D are maximum power output , open-circuit voltage , and short-circuit current (). The efficiency of n-ZnO/p-GaAs solar cell without using any anti-reflective coating (ARC) is 17.76% and it increase to 19.65% after SiO₂/ZnO/Si₃N₄ as triple layer anti-reflective coating (TLARC) be used on the top of the solar cell.

A Refined Grodzins Based Signature for Shape Phase Transitions in Rare Earth Nuclei Using the Interacting Boson Approximation

Mudasir Ahmad — 2026-02-03

This study investigates a refined empirical signature, the Grodzins systematics expressed as dE4/2 ×B(E2; 0⁺→2⁺) within the framework of the Interacting Boson Approximation (IBA), focusing on its correlation to the control parameter (ζ) and its role in nuclear shape transitions. By analyzing even-even nuclei with Z < 76, N = 86–102, a critical transition point is identified near ζ ≈ 0.6, corresponding to the boundary between spherical U(5) and deformed SU(3)/γ-soft O(6) regimes, where rapid changes in collectivity and excitation systematics occur. This critical value corresponds to the point where quadrupole–quadrupole correlations begin to dominate over single-boson dynamics, leading to abrupt changes in excitation energy ratios and electromagnetic collectivity, as supported by abrupt shifts in R6/0 energy ratios and B(E2; 0⁺→2⁺) transition probabilities. Notable anomalies around N ~ 90-100, suggest the onset of shape coexistence. This approach offers a sensitive diagnostic for nuclear collectivity and shape transitions without invoking model dependent assumptions.

Theoretical Modeling of PV Integrated Greenhouse for Fish Farming

Manju Bhardwaj — 2026-02-03

Variation of temperature due to climatic changes affects the yield and growth of the fish in cold climatic condition. This will impact the supply of protein rich food due to reduction in aquaculture production; one of the prime sectors for providing large scale employment. In this paper, the theoretical framework using Greenhouse and photovoltaic model is presented to maintain desired temperature in the fish water pond (18˚C to 35˚C). The controlled environment is maintained inside the greenhouse for the survival of fish in harsh weather. Greenhouse room air is heated using solar radiation to minimize evaporation and conduction losses from the surface of water pond as temperature inside greenhouse is high in comparison to atmospheric temperature. Photovoltaic modules produce electrical power which is used in various applications. Various packing factors have been taken to optimize the controlled condition inside system. It has been observed that desired temperature and maximum electrical power is achieved with 0.8 packing factor. It is clear that the proposed structure not only improves fish growth rate, promotes clean environment as renewable energy is used but also provides livelihood to fish farmers.

A Machine Learning-Driven Methodology for the Precise Determination of the Ground State Energy of Helium Atom

Dr Selvarengan Paranthaman — 2026-02-03

Here the ground state energy of helium atom is investigated and also a Machine Learning (ML) model is constructed using Gaussian Process Regression (GPR) algorithm for the same. The parameters free perturbative method in matrix representation approach is used,in which the approximation is improved by adding higher order p-orbital states. The error is reduced to be 1.93%.This allows us to confirm that the accuracy of the energy value will converge with respect to adding higher order states of p-orbital, d-orbital, etc. Since here prediction belongs to regression model, Gaussian Process Regression (GPR) is chosen. With small dataset we extend this work for ML energy prediction model using GPR technique which is used to inter or extrapolate the ground state energy value.Cross validation is also done using R² evaluation metric.

Design and Analysis of Chiral MZI Based All-Optical Galois Field Adder

Arindam Changder — 2026-02-03

The design of a Chiral Mach-Zehnder interferometer based 4-bit all-optical Galois field adder operating at significantly low power. Quartz has been utilized as the chiral material which rotates the plane of polarization of a light signal and this polarization rotation is the backbone of the all-optical switching module. The presence or absence of the control signal significantly changes the output of the CMZI switch. This switching module solely gives the all-optical logic XOR operation and with the help of four such parallel XOR gates we can design a CMZI based 4-bit all-optical Galois field adder. The circuit has been analysed by calculating performance indicating parameters such as amplitude modulation (AM~0.008695 dB), extinction ratio (ER~13.27 dB), contrast ratio (CR~16.28 dB), Q-factor (Q~34.48 dB), relative eye opening (REO~ 95.29 %) etc to establish the practical feasibility of the device. These satisfactory values suggest that this device may play a significant role in advancing the next generation optical technologies.

A Mathematical and Simulation Study of Lanthanum-Modified PZT for Enhanced Serendipity Shaped Cantilever Piezoelectric Energy Harvester

Hemant Narayan — 2026-02-03

Piezoelectric energy harvesting is widely explored for powering low-frequency structural-health-monitoring (SHM) systems. The effect of lanthanum (La) mixing on the performance of PZT-5A and PZT-5J, particularly in nonuniform cantilever geometries, remains insufficiently explored. In this work, the effect of La concentrations of 1.12, 2.24, and 3.36 percentage of weights in a serendipity shaped cantilever piezoelectric energy harvester were examined using copper, silicon, and structural-steel substrates with nickel electrodes. A reduced single-degree-of-freedom electromechanical model was developed to interpret the simulated behaviour. Results show that La mixing enhances output voltage and harvested power across all configurations. The highest performance was obtained for PZT-5A with structural steel and 3.36 % La, yielding 15.47 V and 9.98 mW at 32 Hz. For PZT-5J with 3.36 % La on copper, 13.466 V and 7.55 mW were achieved. Analytical calculations predicted a maximum power of 9.319 mW at an optimal load of 50 kΩ. Capacitance of 96.2 nF for PZT-5A and 107.5 nF for PZT-5J corresponded to electrical reactance near 51.7 kΩ and 46.3 kΩ. An extraction efficiency of 26.1 % under 2 g excitation was estimated. Overall, La mixing improved the output by 12–13 %, providing an effective design pathway for enhanced PZT-based energy harvesters for low-frequency SHM applications.

A Bio-Inspired Gazelle Optimization Algorithm for Parameter Tuning of Coordinated PSS-SSSC to Damp Low-Frequency Oscillation in Power Systems

Sritosh Kumar Sahoo — 2026-02-03

This study presents a novel Gazelle optimization algorithm that improves power system stability by damping
low-frequency oscillations. The Gazelle optimization algorithm (GOA) optimizes the controller parameters of coordinated PSS and SSSC controllers, with the main objective of reducing rotor speed deviation. The GOA effectiveness was validated with its benchmark functions and demonstrated in a multi-machine power system as a test system. A statistical analysis has been carried out with different benchmark functions to demonstrate the superiority of the suggested algorithm along with three other optimization techniques, which are the sinecosine algorithm, moth-flame algorithm and ant lion optimization algorithm. A nemenyi hypothesis test was implemented to determine the lowest mean rank, along with the Wilcoxon signed-rank test, which shows the effecacy of the proposed algorithm. The results, which are backed by statistical analysis, demonstrate that the suggested GOA approach performs better than three alternative heuristic algorithms. The GOA-tuned parameters are finally implemented with a multimachine power system network as a test system. Different parameters, such as speed deviation between different machines, voltage injected by SSSC, and tie-line power, are compared with three other optimization techniques in the results section. From the result analysis, it has been concluded that the proposed Gazelle optimization algorithm gives superior performance than others, which proves its real-time applicability in the modern power system world.

Intelligent Fault Detection in PSO-MPPT based Photovoltaic and Electric Vehicle Integrated System Under Partial Shading Conditions

S Venkata Ramudu Naik — 2026-02-03

A method to amend the operation of a Photovoltaic (PV) system plus Electric vehicles (EVs) integrated system, under varied partial shading conditions and varying irradiance conditions using an optimized Maximum Power Point Tracking (MPPT) technique. As solar energy becomes more popular, it's crucial to optimize PV panel efficiency to meet growing demand. Nevertheless, PV panels face challenges, including Partial Shading Conditions (PSCs), which significantly impacts their efficiency. In this study, Particle Swarm Optimization (PSO) algorithm based MPPT is used to calculate the Global Maximum peak point (GMPP) from peak power measurements and an improved grid management scheme based on a Novel Convolutional Neural Network (NCNN) was developed and trained using an Enhanced Golden Search Algorithm (EGSA). EGSA can adjust hyperparameters effectively, which promotes model performance and accelerates the convergence rate. Partial shading-induced faults are identified with high sensitivity, and a diagnosis accuracy of 99.6 % and a fast response time of 0.45 s are obtained. With the help of Simulation results the efficacy of the propositioned method is tested and validated.

Reliability Analysis of Switch Placement in Radial Distribution Network Considering Failure Probability

Manish Kumar Madhav — 2026-02-03

A comprehensive reliability assessment of radial distribution networks (RDN) by examining the operational impact and failure probability of protective devices such as isolators, fuses, and remote-controlled switches (RCS). The study starts with analytical evaluation of the 4-bus RDN and demonstrates the absence of protection causes complete feeder outages for any interruption. Reliability is significantly improved when fuses and isolators are added, as they enable selective isolation and faster service restoration. The work further investigates the practical scenario in which protective devices may fail, and results show notable deterioration in reliability indices when considering switch failure probability. To address this, a genetic algorithm (GA)-based optimization model is proposed for the optimal placement of isolators and RCS while explicitly incorporating their failure rates. The proposed method aims to minimize total system cost, including investment and customer interruption cost. The proposed method is tested on a modified IEEE 34-bus network confirms that 17 protective devices provide the optimal trade-off between economic investment, interruption reduction, and overall system reliability, whereas additional devices offer negligible improvement. The findings highlight the critical importance of incorporating realistic switch reliability during planning, ensuring accurate estimation of network performance and enhancing distribution system resilience.

Integrating Demand Response and Solar PV in Indian Coal Power Plants: A Hippopotamus Optimization Approach to Emission Mitigation

Vivek Saxena — 2026-02-03

Coal-fired electricity generation remains a dominant global source of greenhouse gas emissions, contributing approximately 15.22 billion metric tons of CO₂ in 2022. This study addresses the urgent need for decarbonization within the Indian coal-based power sector by examining the coordinated use of demand response (DR) strategies and optimally integrated solar photovoltaic (PV) systems. Solar PV is considered as a strategic complementary resource capable of reducing the dependence on conventional coal-fired units while enhancing clean distributed generation. The optimal siting and sizing of PV installations are determined using the Hippopotamus Optimization (HO) algorithm, which incorporates solar irradiance availability, temporal load patterns, and the emission intensity of coal-based supply. The proposed framework aims to increase renewable energy penetration, reduce technical losses, and achieve significant CO₂ mitigation while addressing solar intermittency and dynamic demand variations. Simulation results obtained on the IEEE 33-bus distribution system indicate that coordinated DR–PV integration leads to substantial reductions in peak demand, system losses, and up to 30.79 % reduction in CO₂ emissions compared to the base case. These findings highlight the effectiveness of combining renewable deployment with demand-side measures in achieving a sustainable, efficient, and environmentally responsible energy transition, offering valuable guidance for policymakers and utility planners engaged in coal-sector decarbonization.