Dual Stress-Dissipation Pathways for Enhanced Thermomechanical Stability in Tin–Lead Perovskite Solar Cells
Bibliographic record
Abstract
Substantial temperature fluctuations during device operation, coupled with the coefficient of thermal expansion (CTE) mismatch between the perovskite and substrate, generate significant thermal stress. This stress induces cracking in the perovskite film and interfacial delamination, severely compromising the stability of perovskite solar cells (PSCs). Nevertheless, this issue remains underexplored in tin–lead PSCs, where thermally induced mechanical failure is exacerbated by abundant stress concentration zones within the perovskite. Herein, we engineer dual stress-dissipation pathways by incorporating a poly[(prop-2-enamide)- co -(prop-2-enoic acid)] (PAA) copolymer. A pre-compressive stress established during film formation counteracts subsequent operational thermal tensile stresses. Concurrently, a flexible hydrogen-bonding network between PAA and perovskite provides an additional dissipation pathway, alleviating the stress concentration. Furthermore, PAA’s flexible carbon chains enhance perovskite film flexibility and reduce CTE mismatch, thereby inhibiting thermally induced cracks and delamination. Consequently, PAA-optimized devices retain 85.0% of their initial efficiency after 1200 h of thermal cycling between 25 and 85 °C.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".