Leveraging Non‐Radiative Transitions in Asphaltenes‐Derived Carbon Dots for Cancer Photothermal Therapy
Bibliographic record
Abstract
Cancer photothermal therapy leverages the capability of photothermal agents to convert light to heat for cancer cell ablation and necrosis. However, most conventional photothermal agents (Au, CuS, Pd, mesoporous silica nanoparticles, and indocyanine green dye) either face scalability challenges or photobleached upon prolonged irradiation which jeopardizes practical applications. Here, asphaltenes-derived carbon dots (ACDs, 5 nm) are rationally engineered as a low-cost and photostable photothermal agent with negligible in vivo cytotoxicity. The abundant water-solvating functional groups on the ACDs surface endows them with excellent water re-dispersibility that outperforms those of most commercial nanomaterials. Photothermal therapeutic property of the ACDs is mechanistically described by non-radiative transitions of excited electrons at 808 nm via internal conversions and vibrational relaxations. Consequently, the ACDs offer cancer photothermal therapy in mice within 15 days post-exposure to one-time near infrared irradiation. This pioneering study showcases the first utilization of asphaltenes-based materials for cancer therapy and is expected to arouse further utilization of such materials in various cancer theranostics.
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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) | 0.000 | 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 | 0.000 |
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".