Cs2BiAgI6 is a lead-free inorganic perovskite material exhibits exceptional photoelectric characteristics and great environmental stability. HTL/Cs2BiAgI6is/ETLs solar cells was investigated numerically by using SCAPS 1-D Capacitance Simulator. IGZO, TiO2, WO3, MoO3, and SnO2 have been chosen as ETLs, while CuO, CuI, and MoO3 are as HTLs. The values of electrical parameters were calculated as function of thickness of the absorber layer, ETLs, HTLs, interface defect densities, doping densities, and working temperature. Comparative study shows that best configuration of obtain solar cell is MoO3/Cs2BiAgI6/IGZO. The obtain value of Jsc, Voc, FF and PCE are 23.80 mA/cm2, 1.193 V, 83.46 %, 23.711 % respectively. The value of quantum efficiency is 80-90 % in the range of 350-750 nm. These results will open the door for the widespread use of stable and environmentally friendly perovskite solar cells by providing theoretical recommendations for high performance of Cs2BiAgI6 based photovoltaic solar cells (PSCs).

Photovoltaic (PV) panels are similar in many aspects to the leaves of trees, both are standing in the Sun to capture the sunlight, however, PV panels get soiled especially in desert areas, while the leaves remain clean to a very good extent. The question is, why leaves remain clean while PV panels get soiled quite easily? The leaves are hanging on the stem of trees and these stems are flexible to motion, such that if the wind blows in any direction over the stem it vibrates allowing any deposited particle to fall off the surface. The objective of this research is to develop a fixation method for PV panels similar to the stems of trees, such that the panel can vibrate as the wind blows in order to minimize dust accumulation. Different fixation methods for the PV panel are designed, and the air flow around the panel is simulated using the CFD package Ansys Fluent. It has been found that a PV panel pivoted at its lower edge, such that it can revolve around the lower edge, together with a vertical wind shield attached to its upper edge and a spring attached at the middle of its backside has the largest vibration amplitude due to the applied wind compared to the other designs. Experiments have been done to infer the influence of the new fixation method of the PV panel on dust accumulation over the panel. After 6 weeks of operation, it has been found that the efficiency of the PV panel that is flexibly fixed has dropped by only 5%, while the efficiency of the panel that is rigidly fixed has dropped by 25%. It can be concluded that a PV panel operating a light post should be fixed on a flexible base that allows the panel to vibrate as the wind blows over it in order to mitigate dust.

To achieve carbon neutrality and sustainable development, innovative solar-to-fuel systems have been designed through the integration of solar energy harvesting and electrochemical devices. Over the last decade, there have been notable advancements in enhancing the efficiency and durability of these solar-to-fuel systems. Despite the advancements, there remains significant potential for further improvements in the performance of systems. Enhancements can be achieved by optimizing electrochemical catalysts, advancing the manufacturing technologies of photovoltaics and electrochemical cells, and refining the overall design of these systems. In the realm of catalyst optimization, the effectiveness of materials can be significantly improved through active site engineering and strategic use of functional groups. Similarly, the performance of electrochemical devices can be enhanced by incorporating specific additives into electrolytes and optimizing gas diffusion electrodes. Improvements in solar harvesting devices are achievable through efficient passivant and self-assembled monolayers, which enhance the overall quality and efficiency of these systems. Additionally, optimizing the energy conversion efficiency involves the strategic use of DC converters, photoelectrodes, and redox media. This review aims to provide a comprehensive overview of the advancements in solar-powered electrochemical energy conversion systems, laying a solid foundation for future research and development in the field of energy sustainability.

Rising on-farm electricity demand, coupled with surges in electricity prices, has increased costs associated with milk production. Additionally, the use of grid electricity with a high carbon footprint depreciates the environmental performance of dairy farming. We assessed the potential of photovoltaic (PV) systems installed on dairy parlours under different policy incentives to reduce electricity costs and the carbon footprint of dairy farms in Ireland. The HOMER Pro software was employed to model electricity consumption, generation and economic performance of four 15-year PV project scenarios for 11 Irish farms. Scenarios considering the targeted agricultural modernisation scheme II (TAMS) and the microgeneration support scheme are assessed. The results show that PV systems are a feasible option to power dairy farms when current energy prices and inflation rates are applied. Small systems eligible for TAMS grants presented an average discounted payback period of 5 years, making them a better option for farmers than larger projects, which had an average payback period of 8.5 years. The deployment of PV systems reduced the GHG intensity of electricity consumed at the farms by up to 29 %.

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work.

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems\' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.

In recent years, the photoelectric conversion efficiency of three-dimensional (3D) perovskites has seen significant improvements. However, the commercial application of 3D perovskites is hindered by stability issues and the toxicity of lead. Two-dimensional (2D) perovskites exhibit good stability but suffer from low efficiency. Designing efficient and stable lead-free 2D perovskite materials remains a crucial unsolved scientific challenge. This study, through structural prediction combined with first-principles calculations, successfully predicts a 2D perovskite, CsTeI5. Theoretical calculations indicate that this compound possesses excellent stability and a theoretical efficiency of up to 29.3%, showing promise for successful application in thin-film solar cells. This research provides a new perspective for the design of efficient and stable lead-free 2D perovskites.

Biosensors have emerged as vital tools for the detection and monitoring of essential biological information. However, their efficiency is often constrained by limitations in the power supply. To address this challenge, energy harvesting systems have gained prominence. These off-grid, independent systems harness energy from the surrounding environment, providing a sustainable solution for powering biosensors autonomously. This continuous power source overcomes critical constraints, ensuring uninterrupted operation and seamless data collection. In this article, a comprehensive review of recent literature on energy harvesting-based biosensors is presented. Various techniques and technologies are critically examined, including optical, mechanical, thermal, and wireless power transfer, focusing on their applications and optimization. Furthermore, the immense potential of these energy harvesting-driven biosensors is highlighted across diverse fields, such as medicine, environmental surveillance, and biosignal analysis. By exploring the integration of energy harvesting systems, this review underscores their pivotal role in advancing biosensor technology. These innovations promise improved efficiency, reduced environmental impact, and broader applicability, marking significant progress in the field of biosensors.

This manuscript analyses changes in the optical parameters of a commercial alumina nanoporous structure (AnodiscTM or AND support) due to surface coverage by the ionic liquid (IL) AliquatCl (AlqCl). XPS measurements were performed for chemical characterization of the composite AND/AlqCl and the AND support, but XPS resolved angle analysis (from 15° to 75°) was carried out for the homogeneity estimation of the top surface of the ANDAlqCl sample. Optical characterization of both the composite AND/AlqCl and the AND support was performed by three non-destructive and non-invasive techniques: ellipsometry spectroscopy (SE), light transmittance/reflection, and photoluminescence. SE measurements (wavelength ranging from 250 nm to 1250 nm) allow for the determination of the refraction index of the AND/AlqCl sample, which hardly differs from that corresponding to the IL, confirming the XPS results. The presence of the IL significantly increases the light transmission of the alumina support in the visible region and reduces reflection, affecting also the maximum position of this latter curve, as well as the photoluminescence spectra. Due to these results, illuminated I-V curves for both the composite AND/AlqCl film and the AND support were also measured to estimate its possible application as a solar cell. The optical behaviour exhibited by the AND/AlqCl thin film in the visible region could be of interest for different applications.

In recent years, the use of solar photovoltaic (PV) energy, which is one of the leading renewable energy sources, has become increasingly widespread around the world due to its numerous advantages. However, PV-based electricity generation necessitates a large amount of land. Agrivoltaic (AV) systems, an innovative approach to combining agricultural and electricity production in the same area through solar modules positioned several meters above the surface of the ground, are growing rapidly in renewable energy and farming communities. This study explores Turkey\'s solar power generation and agricultural activities, combining crop cultivation and electricity generation for sustainable development on the same land. Furthermore, the AV potential for the most agriculture ten cities in different climate zones in Turkey is investigated using the PVsyst program. A list of the most commonly grown crops in the ten selected cities and the types of AV systems that can be employed with these crops is provided. The results show that AV systems present a great opportunity for the optimal integration of solar power generation with food production, especially for the cities of Konya, Kayseri, and Manisa, with the most ideal conditions for agricultural and solar power production. By combining the solar power potential of the country with the production capacity of arable lands, the increasing energy needs can be met and more efficient agricultural production can be provided. This study is expected to demonstrate that in specific regions of Turkey, AV farming will be suitable for certain crops.