Thiols Passivate Recombination Centers in Colloidal Quantum Dots Leading to Enhanced Photovoltaic Device Efficiency
Bibliographic record
Abstract
The use of thiol-terminated ligands has recently been reported to enhance 10-fold the power conversion efficiency (PCE) of colloidal quantum dot (CQD) photovoltaic (PV) devices. We find herein that, in a representative amine-capped PbS colloidal quantum dot materials system, improved mobility following thiol treatment accounts for only a 1.4-fold increase in PCE. We then proceed to investigate the origins of the remainder of the quadrupling in PCE following thiol treatment. We find through measurements of photoluminescence quantum efficiency that exposure to thiols dramatically enhances photoluminescence in colloidal quantum dot films. The same molecules increase open-circuit voltage (V(oc)) from 0.28 to 0.43 V. Combined, these findings suggest that mid-gap states, which serve as recombination centers (lowering external quantum efficiency (EQE)) and metal-semiconductor junction interface states (lowering V(oc)), are substantially passivated using thiols. Through exposure to thiols, we improve EQE from 5 to 22% and, combined with the improvement in V(oc), improve power conversion efficiency to 2.6% under 76 mW/cm(2) at 1 microm wavelength. These findings are consistent with recent reports in photoconductive PbS CQD photodetectors that thiol exposure substantially removes deep (0.3 eV) electron traps, leaving only shallow (0.1 eV) traps.
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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".