Encapsulation of Perovskite Solar Cells with Thin Barrier Films
Bibliographic record
Abstract
Long-term stability is a requisite for the widespread adoption and commercialization of perovskite solar cells (PSCs). Encapsulation constitutes one of the most promising ways to extend devices for lifetime without noticeably sacrificing the high power conversion efficiencies that make this technology attractive. Among encapsulation strategies, the most investigated methods are as follows: (1) glass-to-glass encapsulation, (2) polymer encapsulation, and (3) inorganic thin film encapsulation (TFE). In particular, the use of UV-, heat-, water-, and/or oxygen-resistant thin films to encapsulate PSCs is a new and promising strategy for extending devices for lifetime. Thin films can be deposited directly onto the PSC, as in TFE, or can be used in conjunction with glass-to-glass and polymer encapsulation to effectively prevent the photo-, thermal-, oxygen-, and moisture-induced degradation of the perovskite. This chapter will outline perovskite degradation mechanisms and provide a summary of the progress made to-date in the encapsulation of PSCs, with a particular focus on the most recent and promising advances that employ thin films. Additionally, the strengths and limitations of TFE approaches will be identified and contrasted against existing encapsulation strategies. Finally, possible directions for future research that can further enhance encapsulation effectiveness and extend PSC for lifetimes towards the 25-year target will be proposed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".