Synthesis of 3D Nanonetwork Si Structures via Direct Ultrafast Pulsed Nanostructure Formation Technique
Bibliographic record
Abstract
Silicon is one of the most used semiconductor materials around the world. This research was conducted on silicon to improve its opto-electrical properties including bandgap and optical conductivity using direct ultrafast pulsed nanostructure formation (DUPNF). The power and frequency of high-intensity picosecond laser pulses were varied, and nanostructures were formed. The optical properties were examined using optical spectroscopy in both VIS and NIR. The structural characteristics were examined using EDX and SEM imaging. It was found that varying the power and frequency of the laser processing caused a change in the bandgap, as well as the structural characteristics of the silicon semiconductor. Increasing the frequency and power showed the formation of nanostructures of silicon and their variation in the properties of the formed nanostructures. The critical breakdown field of the silicon nanostructures was higher than the bulk silicon.
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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".