Rapid and Green Production of Mo<sub>2</sub>C Nanoparticles with High Photo‐Thermalization via Single‐Step Femtosecond‐Laser Irradiation
Bibliographic record
Abstract
Photothermal cancer therapy demands nanomaterials with specific traits, including selective absorption of biotransparent near‐infrared (NIR) light, efficient light‐to‐heat conversion, biocompatibility, dispersibility, and prolonged temporal stability. These desirable properties are achieved by synthesizing Mo2C nanoparticles via an environmentally friendly femtosecond‐laser ablation method. Mo2C flakes are dispersed in water and treated with different laser powers for different durations. This process produces Mo2C nanoparticles in a single step in 10 min with water as the only additional material, forming stable colloidal solutions with no contaminants or hazardous waste. Structural and compositional characterization indicates laser‐induced amorphization of the nanoparticles, including gradual oxidation that enhances NIR light absorption. Notably, the Mo2C nanoparticle solution prepared using a 1.6‐W laser power in 10 min demonstrates photothermal conversion efficiencies exceeding 45% and 50% and temperature increases of 21 and 22 °C when illuminated with biotransparent 800 and 1064 nm NIR light, respectively. Furthermore, the solution exhibits exceptionally stable photothermal behavior over 6 months. These Mo2C nanoparticles, prepared by a rapid and clean laser manufacturing method, hold great promise for advancing photothermal therapy to combat cancer noninvasively.
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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".