Fundamental Flaw in the Current Construction of the TiO<sub>2</sub> Electron Transport Layer of Perovskite Solar Cells and Its Elimination
Bibliographic record
Abstract
The top-performing perovskite solar cells (efficiency > 20%) generally rely on the use of a nanocrystal TiO 2 electron transport layer (ETL). However, the efficacies and stability of the current stereotypically prepared TiO 2 ETLs employing commercially available TiO 2 nanocrystal paste are far from their maximum values. As revealed herein, the long-hidden reason for this discrepancy is that acidic protons (∼0.11 wt %) always remain in TiO 2 ETLs after high-temperature sintering due to the decomposition of the organic proton solvent (mostly alcohol). These protons readily lead to the formation of Ti–H species upon light irradiation, which act to block the electron transfer at the perovskite/TiO 2 interface. Affront this challenge, we introduced a simple deprotonation protocol by adding a small amount of strong proton acceptors (sodium ethoxide or NaOH) into the common TiO 2 nanocrystal paste precursor and replicated the high-temperature sintering process, which wiped out nearly all protons in TiO 2 ETLs during the sintering process. The use of deprotonated TiO 2 ETLs not only promotes the PCE of both MAPbI 3 -based and FA 0.85 MA 0.15 PbI 2.55 Br 0.45 -based devices over 20% but also significantly improves the long-term photostability of the target devices upon 1000 h of continuous operation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".