Solution‐Processed In<sub>2</sub>O<sub>3</sub>/ZnO Heterojunction Electron Transport Layers for Efficient Organic Bulk Heterojunction and Inorganic Colloidal Quantum‐Dot Solar Cells
Bibliographic record
Abstract
We report the development of a solution‐processed In2O3/ZnO heterojunction electron transport layer (ETL) and its application in high efficiency organic bulk‐heterojunction (BHJ) and inorganic colloidal quantum dot (CQD) solar cells. Study of the electrical properties of this low‐dimensional oxide heterostructure via field‐effect measurements reveals that electron transport along the heterointerface is enhanced by more than a tenfold when compared to the individual single‐layer oxides. Use of the heterojunction as the ETL in organic BHJ photovoltaics is found to consistently improve the cell's performance due to the smoothening of the ZnO surface, increased electron mobility and a noticeable reduction in the cathode's work function, leading to a decrease in the cells’ series resistance and a higher fill factor (FF). Specifically, non‐fullerene based organic BHJ solar cells based on In2O3/ZnO ETLs exhibit very high power conversion efficiencies (PCE) of up to 12.8%, and high FFs of over 70%. The bilayer ETL concept is further extended to inorganic lead‐sulphide CQD solar cells. Resulting devices exhibit excellent performance with a maximum PCE of 8.2% and a FF of 56.8%. The present results highlight the potential of multilayer oxides as novel ETL systems and lay the foundation for future developments.
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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".