Role of Bond Adaptability in the Passivation of Colloidal Quantum Dot Solids
Bibliographic record
Abstract
Colloidal quantum dot (CQD) solids are attractive materials for photovoltaic devices due to their low-cost solution-phase processing, high absorption cross sections, and their band gap tunability via the quantum size effect. Recent advances in CQD solar cell performance have relied on new surface passivation strategies. Specifically, cadmium cation passivation of surface chalcogen sites in PbS CQDs has been shown to contribute to lowered trap state densities and improved photovoltaic performance. Here we deploy a generalized solution-phase passivation strategy as a means to improving CQD surface management. We connect the effects of the choice of metal cation on solution-phase surface passivation, film-phase trap density of states, minority carrier mobility, and photovoltaic power conversion efficiency. We show that trap passivation and midgap density of states determine photovoltaic device performance and are strongly influenced by the choice of metal cation. Supported by density functional theory simulations, we propose a model for the role of cations, a picture wherein metals offering the shallowest electron affinities and the greatest adaptability in surface bonding configurations eliminate both deep and shallow traps effectively even in submonolayer amounts. This work illustrates the importance of materials choice in designing a flexible passivation strategy for optimum CQD device performance.
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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 | 0.001 | 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".