Survey of Polymer Self-Healing Mechanisms in Perovskite Solar Cells
Bibliographic record
Abstract
Perovskite solar cells (PSCs) have emerged as a rising next-generational photovoltaic technology due to low fabrication costs through solution processing as compared to traditional silicon solar cells and high-power conversion efficiency. However, the poor long-term operational stability due to environmental and mechanical degradation remains a hindrance to commercialization. Herein, self-healing polymer additives are utilized by researchers to enhance the photovoltaic performance of PSCs by enabling self-restorative behavior from physical damage or chemical degradation. This review explores the design and application of self-healing polymers in both flexible and rigid PSCs, contrasting the two main reversible bonding mechanisms: physical bonds, such as hydrogen bonds, and chemical bonds, such as dynamic covalent disulfide bonds. Physical bonds provide passive healing at ambient conditions; meanwhile, chemical bonds offer a stronger restoration under external stimuli such as heat or light. These polymers are exceptionally effective at mitigating mechanical stress and cracks in flexible PSCs and combating moisture-induced degradation in rigid PSCs. The applications of self-healing polymers are categorized based on substrate type, healing mechanism, and perovskite composition, with the benefits and limitations of each approach highlighted. Additionally, the review explores the potential of multifunctional self-healing polymers to passivate defects at the grain boundaries and on surface of perovskite films, thereby enhancing the overall photovoltaic performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".