Synthesis, Characterization, and Typical Application of Nitrogen‐Doped MoS<sub>2</sub> Nanosheets Based on Pulsed Laser Ablation in Liquid Nitrogen
Bibliographic record
Abstract
Molybdenum disulfide nanosheets (MoS2 NSs) are synthesized via pulsed laser ablation of MoS2 target in liquid nitrogen (LN2) using the Q‐switched Nd:YAG (Nd:Y3Al5O12) laser. Not only LN2 facilitates the condensation of the laser‐induced plasma plume to produce MoS2 NSs alongside main by‐products (namely, MoS2 quantum dots (QDs)), but also provides an optimum condition for nitrogen‐doped MoS2 (N‐MoS2) synthesis as a p‐type semiconductor. The structural, optical, and chemical properties of NSs are investigated using various electron microscopic instruments and spectrometers. These attest to the formation of suspension MoS2 NSs with a few‐layer structure and rather large lateral size. MoS2 NSs enjoy extra chemical components such as Mo–N bonding. Furthermore, photoluminescence (PL) spectroscopy reveals exciton and trion peaks as the evidence of p‐type property. Subsequently, the Hall effect verifies the p‐type property of N‐MoS2 NSs. By making use of the spin coating, the N‐MoS2 diaphragm has been fabricated; then, it is mounted on the Fabry–Pérot interferometer (FPI) assembly using the fishing method. Thus, N‐MoS2 in FPI acts as a sensitive component of the acoustic optical fiber sensor. The diaphragm operates as ultrasensitive membrane to detect acoustic waves enhancing the sensitivity up to 20% mainly due to the smaller Young's modulus.
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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".