Orthogonally Arranged Bimolecular Passivation: Sequential Interface Engineering Improves Charge Transfer at the Perovskite/PCBM Interface
Bibliographic record
Abstract
Although surface passivation has significantly contributed to the rapid increase in the power conversion efficiency (PCE) of p-i-n perovskite solar cells (PSCs), the nonradiative recombination and energy level alignment at the perovskite/PCBM interface are still the key factors affecting the overall efficiency of p-i-n PSCs. Herein, a sequential interface engineering (SIE) strategy is developed to enhance charge transfer at the perovskite/PCBM interface in inverted p-i-n perovskite solar cells. p -Anisidine-2-sulfonic acid (pAsA) is used to passivate surface defects on the perovskite layer. Its multiactive-site binding aligns with the Pb 2 + ion spacing on the perovskite surface. Then, vertically aligned ethylenediamine diiodide (EDAI 2 ) is introduced into the perovskite/PCBM interface, forming dipoles to further passivate defects, reduce nonradiative recombination, and improve energy level alignment. The rationality of orthogonal bimolecular passivation is proven by theoretical calculations. The SIE strategy not only suppresses nonradiative recombination but also optimizes the surface morphology and energy level alignment at the interface. As a result, the MA-free inverted perovskite solar cells achieve a remarkable power conversion efficiency of 24.85%, and the stability is significantly enhanced by retaining 84.92% of the initial efficiency after 2000 h of MPP tracking under room-temperature conditions. The work underscores the potential of a synergistic passivation approach for advancing the performance and stability of PSCs.
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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".