The effect of nature and pressure of ambient environment on laser-induced breakdown spectroscopy and ablation mechanisms of Si
Bibliographic record
Abstract
Abstract The effect of nature and pressure of ambient environment on laser-induced breakdown spectroscopy (LIBS) and ablation mechanisms of silicon (Si) have been investigated. A Q-switched Nd-YAG laser with the wavelength of 1064 nm, pulse duration of 10 ns, and pulsed energy of 50 mJ was employed. Si targets were exposed under ambient environments of inert gases of argon, neon, and helium for different pressures ranging from 5 to 760 torr. The influence of nature and pressure of ambient gases on the emission intensity of Si plasma have been explored by using the LIBS spectrometer system. The plasma parameters such as electron temperature and number density were determined by applying Boltzmann plot and Stark broadening method, respectively. Our experimental results suggest that the nature and pressure of ambient environment play a significant role for generation, recombination, and expansion of plasma and consequently affect the excitation temperature as well as electron density of plasma. The surface morphological analysis of laser-irradiated Si was performed by using scanning electron microscope (SEM). Various kinds of structures, for example laser-induced periodic surface structures or ripples, cones, droplets, and craters have been generated and their density and size are found to be strongly dependent upon the ambient environment. A quantitative analysis of particulate size and crater depth measured from SEM images showed a strong correlation between plasma parameters and the growth of micro/nanostructures on the modified Si surface.
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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".