Tuning Areal Density and Surface Passivation of ZnO Nanowire Array Enable Efficient PbS QDs Solar Cells with Enhanced Current Density
Bibliographic record
Abstract
Abstract Colloidal PbS quantum dots (QDs) have provoked a revolution in the field of optoelectronic devices owing to their low‐cost fabrication processing and excellent physical properties. Recently, the fabrication of nanostructured PbS QD photovoltaic (PV) devices based on zinc oxide (ZnO) nanowire array appears as an effective strategy for improving the overall device performance. Despite its potentially strong impact on the device performance, the role of nanowire areal density on photon absorption and exciton dynamics has not yet been studied and still remains unexplored. Here, for the first time, the areal density of ZnO nanowires is tuned through controlling the precursor concentration and its impact on PbS QD PV performance is studied. It is found that the device with optimized ZnO nanowire areal density yields significantly increased power conversion efficiency (PCE) (10.1% vs 8.5% of control nanowire‐based device) due to improved antireflection effect and reduced surface recombination states. To further improve the photovoltaic performance, the ZnO nanowire surface is treated with hydrogen plasma. Transient photovoltage (TPV) measurement reveals that this passivation process noticeably reduces the nonradiative charge recombination yielding a champion device with a remarkable PCE of 10.8%.
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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".