Carbon-Based Quantum Dots for Photovoltaic Devices: A Review
Bibliographic record
Abstract
Interest in carbon quantum dots (CQDs) has recently boomed due to their potential to enhance the performance of various solar technologies as nontoxic, naturally abundant, and cleanly produced nanomaterials. CQDs and their other variations, such as nitrogen-doped carbon quantum dots (NCQDs) and graphene quantum dots (GQDs), have improved the performance of luminescent solar concentrators (LSCs) and photovoltaic (PV) cells due to their excellent optical properties. As fluorophores in LSCs, CQDs are mostly transparent to visible light and have absorption/re-emission spectra that can be easily controlled. The outstanding optical properties of CQDs make them promising materials to replace expensive, heavy-metal-based fluorophores. Various CQDs have also been used as or doped into the photoanode, counter electrode, hole transport layer (HTL), and electron transport layer (ETL) of dye-sensitized solar cells (DSSCs), organic solar cells (OSC), perovskite solar cells (PSCs), and other PV cell configurations. The addition of CQDs into the various solar cell components has reduced electron recombination, increased charge density, and boosted electron mobility, improving the performance of the PV cells. Enhancing the power conversion efficiency (PCE) of photovoltaic devices is essential in propagating green energy technology. Thus, CQDs offer an affordable, safe, and environmentally friendly method to advance photovoltaic 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".