Defect‐Rich MoSe<sub>2</sub> 2H/1T Hybrid Nanoparticles Prepared from Femtosecond Laser Ablation in Liquid and Their Enhanced Photothermal Conversion Efficiencies
Bibliographic record
Abstract
Abstract MoSe2 2H/1T hybrid nanoparticles are prepared by femtosecond laser ablation of MoSe2 powder in isopropyl alcohol with different laser powers and ablation times, and their formation mechanisms and photothermal conversion efficiencies (PTCEs) are studied. Two types of spherical nanoparticles are observed. The first type is onion‐structured nanoparticles that are formed by nucleation on the surfaces of melted droplets followed by inward growth of {002} planes of MoSe2. The second type is polycrystalline nanoparticles, formed by coalescence of crystalline nanoclusters fragmented from the powder during the laser ablation. The nanoparticle size in all samples shows a bimodal distribution, corresponding to different fragmentation mechanisms. The 2H‐to‐1T phase transition in the nanoparticles is likely caused by electron doping from the laser‐induced plasma. The PTCEs of the nanoparticles increase with laser power and ablation time; the highest PTCE is around 38%. After examining the bandgaps and the Urbach energies of the nanoparticles, it is found that the high PTCEs are primarily attributed to defects and structural disorder in the laser‐synthesized nanoparticles, which allow absorption of photons with energies smaller than the bandgap energy and facilitate non‐radiative recombination of photoexcited carriers.
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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".