Bibliographic record
Abstract
The development of photovoltaic devices, solar cells, plays a key role in renewable energy sources. Semiconductor colloidal quantum dots (CQDs), including lead chacolgenide CQDs that have tunable electronic bandgaps from infrared to visible, serve as good candidates to harvest the broad spectrum of sunlight. CQDs can be processed from solution, allowing them to be deposited in a roll-to-roll printing process compatible with low-cost fabrication of large area solar panels. Enhanced multiexciton generation process in CQD, compared with bulk semiconductors, enables the potential of exceeding Shockley-Queisser limit in CQD photovoltaics. For these advantages, CQDs photovoltaics attract great attention in academics, and extensive research works accelerate the development of CQD based solar cells. The record efficiency of CQD solar cells increased from 5.1% in 2011 to 9.9% in 2015. The improvement relies on optimized material processing, device architecture and various efforts to improve carrier collection efficiency. In this review, we have summarized the progress of CQD photovoltaics in year 2012 and after. Here we focused on the theoretical and experimental works that improve the understanding of the device physics in CQD solar cells, which may guide the development of CQD photovoltaics within the research community.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".