Rapid and Green Production of Mo<sub>2</sub>C Nanoparticles with High Photo‐Thermalization via Single‐Step Femtosecond‐Laser Irradiation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 Mo 2 C nanoparticles via an environmentally friendly femtosecond‐laser ablation method. Mo 2 C flakes are dispersed in water and treated with different laser powers for different durations. This process produces Mo 2 C 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 Mo 2 C 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 Mo 2 C nanoparticles, prepared by a rapid and clean laser manufacturing method, hold great promise for advancing photothermal therapy to combat cancer noninvasively.
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.
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 | 0.001 |
| 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 it