Advancing Carbon-Based Perovskite Solar Cells: Experimental Validation, Optimization, and Machine Learning Integration

Perovskite solar cells (PSCs) have garnered significant attention due to their exceptional efficiency and cost-effectiveness, positioning them as a leading candidate in pursuing sustainable energy solutions. However, transitioning from laboratory-scale research to large-scale commercial production presents substantial challenges. This thesis aims to address these challenges by investigating three critical aspects: the influence of rheological properties in fabrication, material synthesis for electron transport layer (ETL) modification, and the integration of machine learning to facilitate the scale-up process. Since the high costs and stability issues associated with silver or gold-based PSCs, this thesis focuses on carbon-based PSCs due to their cost-effectiveness and enhanced stability. The potential impact of these findings on the field of renewable energy and solar cell technology is immense, offering a promising future for the widespread adoption of PSCs. The influence of rheological properties on carbon-based PSC (C-PSC) performance is investigated. This involves examining the rheological impact of the mesoporous-TiO2 (m-TiO2) layer. Using SCAPS (Solar Cell Capacitance Simulator) simulations, theoretical comparisons of these variations are made and validated through experimental data. This study explores how morphological variations according to the pastes' viscosity and thickness affect the bandgap and device photovoltaic performance. Two types of m-TiO2 samples, Type 1 and Type 2, are analysed, each with six different thickness configurations. Material synthesis for ETL modification is explored by integrating morphology-modulated CeO2 into existing ETL layers. This involves synthesising rod and cube CeO2 nanoparticles and examining their performance when combined with TiO2 layers in C-PSCs. Detailed studies on these new materials' structural, electrical, and interfacial properties are conducted, and their efficiency performance is analysed through J-V, EIS (Electrochemical Impedance Spectroscopy), and EQE (external quantum efficiency) studies. Machine learning (ML) addresses scalability challenges in PSC production. ML algorithms are trained on extensive theoretical datasets to predict and optimise fabrication processes. Various ML models, including artificial neural networks (ANN), linear regression (LR), random forest (RF), and K-nearest neighbours (KNN), are employed to determine the most effective model. A dataset of 700 data points is curated via SCAPS-1D simulation, encompassing variations in ETL and perovskite layer thickness and bandgap characteristics. These complex and multifaceted challenges were overcome in the course of this research, demonstrating the depth and complexity of the work. For the m-TiO2 rheology, the best configurations for Type 1 and Type 2 samples exhibit theoretical efficiencies of 16.40% and 16.81%, respectively. Experimental replication yields efficiencies of 10.12% and 12.20%, respectively. These results are further scrutinised using impedance spectroscopy and external quantum efficiency (EQE) analysis. The rod structure CeO2 shows superior light scattering, resulting in the highest power conversion efficiency (PCE) of 12.749% for the combined TiO2 and rod structure CeO2 configuration. The TiO2-only configuration and combined TiO2-CeO2 cube yield PCEs of 10.468% and 9.637%, respectively. These findings highlight the potential of morphology-modulated CeO2 as an efficient alternative for ETL layers. The ANN-based ML model demonstrates the highest prediction accuracy for optimising PSC fabrication parameters. By analysing key parameters influencing fabrication, the ML model forecasts performance ranges associated with specific conditions, providing a robust framework for enhancing scalability in PSC production. In summary, this research tackles pivotal challenges in commercialising perovskite solar cells by optimising fabrication techniques, significantly enhancing material stability, and leveraging sophisticated machine learning models to optimise scalability and production efficiency. These innovations collectively pave the way for the widespread adoption of cost-effective and high-performance solar technology

Application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in Drone Thermal Imaging for Solar Farm Monitoring

This is the final version. Available on open access from MDPI via the DOI in this recordThe impact of multimedia in day-to-day life and its applications will be increased greatly with the proposed model (MSVPC)–5G Multicast SDN network eminence video transmission obtained using PSO and cross layer progress in wireless nodes. The drone inspection and analysis in a solar farm requires a very high number of transmissions of various videos, data, animations, along with all sets of audio, text and visuals. Thus, it is necessary to regulate the transmissions of various videos due to a huge amount of bandwidth requirement for videos. A software-defined network (SDN) enables forwarder selection through particle swarm optimization (PSO) mode for streaming video packets through multicast routing transmissions. Transmission delay and packet errors are the main factors in selecting a forwarder. The nodes that transfer the videos with the shortest delay and the lowest errors have been calculated and sent to the destination through the forwarder. This method involves streaming to be increased with the highest throughput and less delay. Here, the achieved throughput is shown as 0.0699412 bits per second for 160 s of simulation time. Also, the achieved packet delivery ratio is 81.9005 percentage for 150 nodes on the network. All these metrics can be changed according to the network design and can have new results. Thus, the application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in drone thermal imaging helps in monitoring solar farms more effectively, and may lead to the development of certain algorithms in prescriptive analytics which recommends the best practices for solar farm development.Engineering and Physical Sciences Research Council (EPSRC