High-Efficiency Colloidal Quantum Dot Photovoltaics via Robust Self-Assembled Monolayers
Bibliographic record
Abstract
The optoelectronic tunability offered by colloidal quantum dots (CQDs) is attractive for photovoltaic applications but demands proper band alignment at electrodes for efficient charge extraction at minimal cost to voltage. With this goal in mind, self-assembled monolayers (SAMs) can be used to modify interface energy levels locally. However, to be effective SAMs must be made robust to treatment using the various solvents and ligands required for to fabricate high quality CQD solids. We report robust self-assembled monolayers (R-SAMs) that enable us to increase the efficiency of CQD photovoltaics. Only by developing a process for secure anchoring of aromatic SAMs, aided by deposition of the SAMs in a water-free deposition environment, were we able to provide an interface modification that was robust against the ensuing chemical treatments needed in the fabrication of CQD solids. The energy alignment at the rectifying interface was tailored by tuning the R-SAM for optimal alignment relative to the CQD quantum-confined electron energy levels. This resulted in a CQD PV record power conversion efficiency (PCE) of 10.7% with enhanced reproducibility relative to controls.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".