Modeling and Design of Solar Cell Materials

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Optoelectronics".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 453

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
Interests: photocatalysis; solar cell; single photon emission; laser; sensing

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Guest Editor
Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA
Interests: perovskite; smart organic materials; solar cells; opto-electronic devices; sensors

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Guest Editor
Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA
Interests: quantum dots; sensing; carbon-based materials; nano crystals; perovskites; solar cells

Special Issue Information

Dear Colleagues,

The global energy demand is growing at an exponential rate with time due to the increasing world population, industrial and economic growth of developing countries, and widespread use of electrical and electronic equipment and gadgets. At the same time, conventional energy sources are running out rapidly, resulting in significant environmental pollution. Therefore, the development of cost-effective, clean, and efficient alternative energy sources is highly important. Among all the renewal energy sources, solar technology is the most significant energy alternative because the sun is an infinite energy source for Earth.

Solar cells are typically categorized into three generations based on the materials used, their efficiency, and the level of commercialization. The first-generation solar cells are also known as traditional, conventional, or wafer-based cells, which are monocrystalline and polycrystalline silicon. Second-generation cells are mainly thin film-based cells, such as amorphous silicon, cadmium telluride (CdTe), and copper indium gallium selenide (CIGS). Third-generation solar cells involve solution-processable technology, such as dye-sensitized solar cells (DSSCs), organic solar cells (OSCs), and perovskite solar cells (PSCs). Recently, these third-generation solar cells, especially PSCs and OSCs, have drawn huge research attention owing to their low cost, easy processability, and high power conversion efficiency (PCE). In the past decade, great efforts have been dedicated to designing and developing this technology. Moreover, it is predicted that multijunction tandem cells (silicon PSC, PSC-PSC, OSC-PSC, etc.) will completely transform the clean energy segment. Additionally, AI is becoming increasingly influential in the design and optimization of solar cells, driving improvements in efficiency, materials discovery, and manufacturing processes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Material design for DSSCs;
  2. Innovative materials for OSCs;
  3. Perovskite solar cells;
  4. Tandem solar cells;
  5. Modeling of solar cell devices;
  6. AI in solar cell design.

Dr. Arka Chatterjee
Dr. Rabindranath Garai
Dr. Nilanjana Nandi
Guest Editors

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Keywords

  • solar cell
  • perovskite
  • AI
  • design
  • tandem
  • modeling of solar cell

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Published Papers (1 paper)

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Research

27 pages, 5530 KiB  
Article
Optoelectronic Devices Analytics: MachineLearning-Driven Models for Predicting the Performance of a Dye-Sensitized Solar Cell
by Emeka Harrison Onah, N. L. Lethole and P. Mukumba
Electronics 2025, 14(10), 1948; https://doi.org/10.3390/electronics14101948 - 10 May 2025
Viewed by 255
Abstract
Optoelectronic devices, which combine optics and electronics, are vital for converting light energy into electrical energy. Various solar cell technologies, such as dye-sensitized solar cells (DSSCs), silicon solar cells, and perovskite solar cells, among others, belong to this category. DSSCs have gained significant [...] Read more.
Optoelectronic devices, which combine optics and electronics, are vital for converting light energy into electrical energy. Various solar cell technologies, such as dye-sensitized solar cells (DSSCs), silicon solar cells, and perovskite solar cells, among others, belong to this category. DSSCs have gained significant attention due to their affordability, flexibility, and ability to function under low light conditions. The current research incorporates machine learning (ML) models to predict the performance of a modified Eu3+-doped Y2WO6/TiO2 photo-electrode DSSC. Experimental data were collected from the “Dryad Repository Database” to feed into the models, and a detailed data visualization analysis was performed to study the trends in the datasets. The support vector regression (SVR) and Random Forest regression (RFR) models were applied to predict the short-circuit current density (Jsc) and maximum power (Pmax) output of the device. Both models achieved reasonably accurate predictions, and the RFR model attained a better prediction response, with the percentage difference between the experimental data and model prediction being 0.73% and 1.01% for the Jsc and Pmax respectively, while the SVR attained a percentage difference of 1.22% and 3.54% for the Jsc and Pmax respectively. Full article
(This article belongs to the Special Issue Modeling and Design of Solar Cell Materials)
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