Recent advances in carbon-based quantum dots for sensing applications
Bibliographic record
Abstract
Carbon-based quantum dots (CQDs) are an emerging class of nanomaterials with unique features due to their quantum confinement effects. They have attracted tremendous attention due to several special features such as intrinsically low toxicity, high solubility in a variety of solvents, excellent biocompatibility, high specific surface area, abundant edge sites, and their capacity for easy modification with other nanomaterials. Due to these unique attributes, CQDs have been explored for numerous applications, including solar cells, energy storage, catalysis, drug and gene delivery, optoelectronics, sensors, and more. The quest for more sensitive electrodes consisting of cost-effective materials has led to the development of electrochemical sensors using CQDs for the detection of various analytes. In this review, the synthesis and characterization of CQDs are summarized and compared. The sensing applications of CQDs, encompassing biomarker detection, environmental monitoring, food safety and quality control, and the determination of pharmaceutical compounds are highlighted. Further, the challenges and future outlooks in CQDs research are briefly outlined.
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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".