In Situ Iodide Passivation Toward Efficient CsPbI3 Perovskite Quantum Dot Solar Cells
Bibliographic record
Abstract
All-inorganic CsPbI3 quantum dots (QDs) have demonstrated promising potential in photovoltaic (PV) applications. However, these colloidal perovskites are vulnerable to the deterioration of surface trap states, leading to a degradation in efficiency and stability. To address these issues, a facile yet effective strategy of introducing hydroiodic acid (HI) into the synthesis procedure is established to achieve high-quality QDs and devices. Through an in-depth experimental analysis, the introduction of HI was found to convert PbI2 into highly coordinated [PbIm]2−m, enabling control of the nucleation numbers and growth kinetics. Combined optical and structural investigations illustrate that such a synthesis technique is beneficial for achieving enhanced crystallinity and a reduced density of crystallographic defects. Finally, the effect of HI is further reflected on the PV performance. The optimal device demonstrated a significantly improved power conversion efficiency of 15.72% along with enhanced storage stability. This technique illuminates a novel and simple methodology to regulate the formed species during synthesis, shedding light on further understanding solar cell performance, and aiding the design of future novel synthesis protocols for high-performance optoelectronic devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".