Carbon quantum dots: Synthesis via hydrothermal processing, doping strategies, integration with photocatalysts, and their application in photocatalytic hydrogen production
Bibliographic record
Abstract
Carbon quantum dots (CQDs), a quasi-spherical carbon-based nanomaterial, have attracted great attention in photocatalysis due to their unique optical and electrochemical properties, such as tunable fluorescence emission, up-conversion property, and the ability to accelerate charge separation. Significant efforts have been made to synthesize, dope, and surface-functionalize CQDs to tune their photo-physical/chemical properties. Hydrothermal (HT) processes provide a clean, cost-effective, and efficient synthesis method to prepare CQDs and their derivatives, through hydrolysis, polymerization, and carbonization reactions. This review summarizes the synthesis of HT-CQDs using various precursors, followed by evaluating the CQDs doping and heterostructure formation strategies based on their fundamental characteristics (e.g., morphology, crystallinity, absorption ability, and photoluminescent properties). Doped CQDs exhibit enhanced optical properties, improved charge transfer efficiency, better electron mobility, and increased photocatalytic activity through introducing new surface states and active sites. Decorating traditional photocatalysts with CQDs improves light absorption and charge separation, significantly boosting the overall photocatalytic activity. The performance of CQD-based photocatalysts in hydrogen (H 2 ) evolution is systematically evaluated as well. CQDs enhance photocatalytic H 2 generation by acting as photosensitizers and/or electron mediators, accelerating the separation of e − -h + pairs and reducing recombination rates. This review highlights the significant advancement in CQDs synthesis, and doping and decorating strategies, showcasing their pivotal role in improving the photocatalytic efficiency of H 2 production. However, limited understanding of mechanisms, precise control over doping and surface functionalization, and scalability of synthesis methods remain key challenges for CQD-based photocatalysts. In the future, integrating artificial intelligence (AI) and machine learning (ML) tools with advanced characterization techniques might help the development of CQD-based photocatalysts, enabling precise property tuning and scalable synthesis approaches.
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 | 0.001 | 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 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".