Nanostructured Back Reflectors for Efficient Colloidal Quantum‐Dot Infrared Optoelectronics
Bibliographic record
Abstract
Abstract Colloidal quantum dots (CQDs) can be used to extend the response of solar cells, enabling the utilization of solar power that lies to the red of the bandgap of c‐Si and perovskites. To achieve largely complete absorption of infrared (IR) photons in CQD solids requires thicknesses on the micrometer range; however, this exceeds the typical diffusion lengths (≈300 nm) of photoexcited charges in these materials. Nanostructured metal back electrodes that grant the cell efficient IR light trapping in thin active layers with no deterioration of the electrical properties are demonstrated. Specifically, a new hole‐transport layer (HTL) is developed and directly nanostructured. Firstly, a material set to replace conventional rigid HTLs in CQD devices is developed with a moldable HTL that combines the mechanical and chemical requisites for nanoimprint lithography with the optoelectronic properties necessary to retain efficient charge extraction through an optically thick layer. The new HTL is nanostructured in a 2D lattice and conformally coated with MoO3/Ag. The photonic structure in the back electrode provides a record photoelectric conversion efficiency of 86%, beyond the Si bandgap, and a 22% higher IR power conversion efficiency compared to the best previous reports.
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 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 | 0.001 |
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".