Infrared Colloidal Quantum Dot Photovoltaics <i>via</i> Coupling Enhancement and Agglomeration Suppression
Bibliographic record
Abstract
Materials optimized for single-junction solar spectral harvesting, such as silicon, perovskites, and large-band-gap colloidal quantum dot solids, fail to absorb the considerable infrared spectral energy that lies below their respective band gap. Here we explore through modeling and experiment the potential for colloidal quantum dots (CQDs) to augment the performance of solar cells by harnessing transmitted light in the infrared. Through detailed balance modeling, we identify the CQD band gap that is best able to augment wafer-based, thin-film, and also solution-processed photovoltaic (PV) materials. The required quantum dots, with an excitonic peak at 1.3 μm, have not previously been studied in depth for solar performance. Using computational studies we find that a new ligand scheme distinct from that employed in better-explored 0.95 μm band gap PbS CQDs is necessary; only via the solution-phase application of a short bromothiol can we prevent dot fusion during ensuing solid-state film treatments and simultaneously offer a high valence band-edge density of states to enhance hole transport. Photoluminescence spectra and transient studies confirm the desired narrowed emission peaks and reduced surface-trap-associated decay. Electronic characterization reveals that only through the use of the bromothiol ligands is strong hole transport retained. The films, when used to make PV devices, achieve the highest AM1.5 power conversion efficiency yet reported in a solution-processed material having a sub-1 eV band gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".