Electronically Active Impurities in Colloidal Quantum Dot Solids
Bibliographic record
Abstract
Colloidal quantum dot films have seen rapid progress as active materials in photodetection, light emission, and photovoltaics. Their processing from the solution phase makes them an attractive option for these applications due to the expected cost reductions associated with liquid-phase material deposition. Colloidally stable nanoparticles capped using long, insulating aliphatic ligands are used to form semiconducting, insoluble films via a solid-state ligand exchange in which the original ligands are replaced with short bifunctional ligands. Here we show that this ligand exchange can have unintended and undesired side effects: a high molecular weight complex can form, containing both lead oleate and the shorter conductive ligand, and this poorly soluble complex can end up embedded within the colloidal quantum dot (CQD) active layer. We further show that, by adding an acidic treatment during film processing, we can break up and wash away these complexes, producing a higher quality CQD solid. The improved material leads to photovoltaic devices with reduced series resistance and enhanced fill factor relative to controls employing previously reported CQD solids.
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 | 0.001 | 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".