Acid-Assisted Ligand Exchange Enhances Coupling in Colloidal Quantum Dot Solids
Bibliographic record
Abstract
Colloidal quantum dots (CQDs) are promising solution-processed infrared-absorbing materials for optoelectronics. In these applications, it is crucial to replace the electrically insulating ligands used in synthesis to form strongly coupled quantum dot solids. Recently, solution-phase ligand-exchange strategies have been reported that minimize the density of defects and the polydispersity of CQDs; however, we find herein that the new ligands exhibit insufficient chemical reactivity to remove original oleic acid ligands completely. This leads to low CQD packing and correspondingly low electronic performance. Here we report an acid-assisted solution-phase ligand-exchange strategy that, by enabling efficient removal of the original ligands, enables the synthesis of densified CQD arrays. Our use of hydroiodic acid simultaneously facilitates high CQD packing via proton donation and CQD passivation through iodine. We demonstrate highly packed CQD films with a 2.5 times increased carrier mobility compared with prior exchanges. The resulting devices achieve the highest infrared photon-to-electron conversion efficiencies (>50%) reported in the spectral range of 0.8 to 1.1 eV.
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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".