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Emerging Technologies in Silicon Solar Cells for Sustainable Energy Systems

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 632

Special Issue Editors


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Guest Editor
Educational Scientific Institute of High Technologies, Taras Shevchenko National University of Kyiv, 64 Volodymyrska, 01033 Kyiv, Ukraine
Interests: carbon and silicon nanoparticles and its application in optoelectronics and theranostics; solar cells; bioimaging; physico- chemistry of surface and interfaces; chemical and biosensors; hydrogen energy

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Guest Editor
V.E. Lashkaryov Institute of Semiconductor Physics NAS of Ukraine, 41 Nauki Avenue, 03028 Kyiv, Ukraine
Interests: nanomaterials; solar cells; semiconductors and dielectrics; gas and biosensors; surface physics; electronics

Special Issue Information

Dear Colleagues,

Solar energy, as a key sustainable resource, continues to be a vital solution to the increasing global demand for energy. Since their inception, silicon solar cells have been recognized as one of the best economically and environmentally viable renewable resources capable of replacing fossil fuels. However, the cost of solar energy remains more expensive than that of carbon-based fuels. Given the current maturity of silicon PV manufacturing technologies, achieving further cost reductions presents significant challenges since their efficiency is significantly constrained by several factors, including insufficient photon absorption, carrier recombination, ohmic losses, thermal losses, and reflection losses. Among promising key technologies aimed at improving the efficiency of silicon solar cells are considered new approaches in surface passivation, anti-reflective coatings, surface texturing, multi-junction solar cells, interdigitated back-contact solar cells, the application of band-gap engineering, optical enhancement, and hybrid nanostructures. The aim of this Special Issue is to encourage researchers and practitioners to share, exchange, and communicate their original, high-quality articles  in the field of new and emerging silicon solar cell technologies to improve cell efficiency and cost reduction and ensure their sustainability. This Special Issue will cover a wide range of topics including, but not limited to, the following:

  • Innovative design and optimization of silicon based solar cells;
  • Efficiency of solar energy conversion;
  • Modeling and numerical simulation of silicon solar cells;
  • Energy management and intelligent control of PV modules;
  • Cutting-edge technologies in silicon for solar cell production;
  • New theories, methods, and techniques to increase silicon solar cell efficiency;
  • The aging of silicon solar cells;
  • Silicon sustainable PV systems.

Prof. Dr. Valeriy Skryshevsky
Prof. Dr. Anatoliy Evtukh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • photovoltaics
  • silicon
  • solar cell
  • efficiency
  • optimization
  • solar cell design
  • PV manufacturing technologies
  • nanomaterials
  • band-gap engineering

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Published Papers (1 paper)

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Research

16 pages, 2018 KiB  
Article
Toward Sustainable Solar Energy: Predicting Recombination Losses in Perovskite Solar Cells with Deep Learning
by Syed Raza Abbas, Bilal Ahmad Mir, Jihyoung Ryu and Seung Won Lee
Sustainability 2025, 17(12), 5287; https://doi.org/10.3390/su17125287 - 7 Jun 2025
Viewed by 486
Abstract
Perovskite solar cells (PSCs) are emerging as leading candidates for sustainable energy generation due to their high power conversion efficiencies and low fabrication costs. However, their performance remains constrained by non-radiative recombination losses primarily at grain boundaries, interfaces, and within the perovskite bulk [...] Read more.
Perovskite solar cells (PSCs) are emerging as leading candidates for sustainable energy generation due to their high power conversion efficiencies and low fabrication costs. However, their performance remains constrained by non-radiative recombination losses primarily at grain boundaries, interfaces, and within the perovskite bulk that are difficult to characterize under realistic operating conditions. Traditional methods such as photoluminescence offer valuable insights but are complex, time-consuming, and often lack scalability. In this study, we present a novel Long Short-Term Memory (LSTM)-based deep learning framework for dynamically predicting dominant recombination losses in PSCs. Trained on light intensity-dependent current–voltage (J–V) characteristics, the proposed model captures temporal behavior in device performance and accurately distinguishes between grain boundary, interfacial, and band-to-band recombination mechanisms. Unlike static ML approaches, our model leverages sequential data to provide deeper diagnostic capability and improved generalization across varying conditions. This enables faster, more accurate identification of efficiency limiting factors, guiding both material selection and device optimization. While silicon technologies have long dominated the photovoltaic landscape, their high-temperature processing and rigidity pose limitations. In contrast, PSCs—especially when combined with intelligent diagnostic tools like our framework—offer enhanced flexibility, tunability, and scalability. By automating recombination analysis and enhancing predictive accuracy, our framework contributes to the accelerated development of high-efficiency PSCs, supporting the global transition to clean, affordable, and sustainable energy solutions. Full article
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