Engineering surface ligands on colloidal quantum dots for solar energy harvesting
Bibliographic record
Abstract
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Colloidal quantum dots (CQDs) can be solution-processed and obtained in low cost and large quantity, and CQD-based devices have been reported in many applications. Here we will showcase our recent works on chalcogenide CQDs for applications in solar energy harvesting. The surfaces of highly monodispersed chalcogenide CQDs (i.e. PbS, CdSe) are covered with long-chain molecular ligands. These surface ligands stabilize CQDs in organic solvents but need to be replaced with short-chain molecules, even single atoms, in applications where fast charge transport between CQDs is required. If this ligand exchange process happens when forming CQD solids in the device, it is called solid-state ligand exchange (SSLE). We have demonstrated CQD-based flexible touch sensors [1], narrow-band photodetectors [2], ultrafast photodiodes [3] and solar cells [4] using SSLE. The long-chain ligands can also be exchanged to short ones in organic solvents, and this solution-phase ligand exchange (SPLE) method provides better charge transport and more flexibility for engineering CQDs into devices [5]. In the presentation, we will discuss two applications of SPLE-produced CQDs for solar energy harvesting - PbS CQDs for homojunction solar cells [6] and CdSeS/ZnS core-shell CQDs for luminescent solar concentrators [7].
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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) | 0.003 | 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".