Perovskite Solar Cells for Extreme Environments and Aerospace Applications: Degradation Mechanisms, Engineering Strategies, and AI Prediction

Perovskite solar cells (PSCs) have emerged as a disruptive photovoltaic technology for aerospace and extreme environment applications, driven by their substantial power-to-weight ratio and mechanical flexibility. However, continuous operation under harsh conditions, characterized by the AM0 spectrum, deep vacuum, extreme thermal cycling, and ionizing radiation, exposes the fundamental thermodynamic instability of traditional organic–inorganic hybrid perovskites. This comprehensive review systematically synthesizes 131 recent studies to provide a holistic framework for designing ultrastable, radiation-hardened PSCs. We critically examine the underlying degradation mechanisms, including vacuum-induced volatile desorption, UV-triggered halide segregation, and thermomechanical fracture at buried interfaces. To overcome these critical barriers, we highlight advanced engineering strategies: the transition to all-inorganic CsPbX3 and lead-free double/chalcogenide perovskites (e.g., Cs2SnI6, CaHfS3), the implementation of dopant-free inorganic transport layers coupled with self-assembled monolayers (SAMs) for cascade band alignment, and the integration of polymeric scaffolds for fracture energy toughening. Furthermore, we emphasize the imperative shift toward solvent-free vacuum deposition techniques (ALD, PLD). A distinctive focus of this review is the integration of Artificial Intelligence; specifically, we evaluate Deep Learning architectures, such as Long Short-Term Memory (LSTM) networks, for predictive State of Health (SOH) monitoring, underscoring the vital transition from simulated to empirical datasets. Finally, coupled with Material Flow Cost Accounting (MFCA), this review outlines a strategic roadmap for the commercialization and deployment of autonomous, self-diagnosing photovoltaic platforms in next-generation satellite and deep-space missions.

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Publication Details

Journal
Clean Technologies
Published
2026-07-16
DOI
https://doi.org/10.3390/cleantechnol8040111
Primary Topic
Perovskite Materials and Applications
Type
article
Field-Weighted Citation Impact
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article

Perovskite Solar Cells for Extreme Environments and Aerospace Applications: Degradation Mechanisms, Engineering Strategies, and AI Prediction

Assel Nurmukan, Aigerim Akylbayeva, Darkhan Yerezhep, A. Aldiyarov et al.
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Perovskite Materials and Applications
article

Perovskite Solar Cells for Extreme Environments and Aerospace Applications: Degradation Mechanisms, Engineering Strategies, and AI Prediction

Assel Nurmukan, Aigerim Akylbayeva, Darkhan Yerezhep, A. Aldiyarov, Zhansaya Omarova, Nussupov Yerzhan, M. Abdizhalel, Ayazhan Dossymbekova, Bergaliyeva Saltanat
article en

Abstract

Perovskite solar cells (PSCs) have emerged as a disruptive photovoltaic technology for aerospace and extreme environment applications, driven by their substantial power-to-weight ratio and mechanical flexibility. However, continuous operation under harsh conditions, characterized by the AM0 spectrum, deep vacuum, extreme thermal cycling, and ionizing radiation, exposes the fundamental thermodynamic instability of traditional organic–inorganic hybrid perovskites. This comprehensive review systematically synthesizes 131 recent studies to provide a holistic framework for designing ultrastable, radiation-hardened PSCs. We critically examine the underlying degradation mechanisms, including vacuum-induced volatile desorption, UV-triggered halide segregation, and thermomechanical fracture at buried interfaces. To overcome these critical barriers, we highlight advanced engineering strategies: the transition to all-inorganic CsPbX3 and lead-free double/chalcogenide perovskites (e.g., Cs2SnI6, CaHfS3), the implementation of dopant-free inorganic transport layers coupled with self-assembled monolayers (SAMs) for cascade band alignment, and the integration of polymeric scaffolds for fracture energy toughening. Furthermore, we emphasize the imperative shift toward solvent-free vacuum deposition techniques (ALD, PLD). A distinctive focus of this review is the integration of Artificial Intelligence; specifically, we evaluate Deep Learning architectures, such as Long Short-Term Memory (LSTM) networks, for predictive State of Health (SOH) monitoring, underscoring the vital transition from simulated to empirical datasets. Finally, coupled with Material Flow Cost Accounting (MFCA), this review outlines a strategic roadmap for the commercialization and deployment of autonomous, self-diagnosing photovoltaic platforms in next-generation satellite and deep-space missions.

Clean TechnologiesVol. 8(4)
Almaty Management University (KZ), Al-Farabi Kazakh National University (KZ), Satbayev University (KZ)
Openalex Percentile: Top 15%
Perovskite Materials and Applications
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