Understanding of sub-band gap absorption of femtosecond-laser sulfur hyperdoped silicon using synchrotron-based techniques
Bibliographic record
Abstract
The correlation between sub-band gap absorption and the chemical states and electronic and atomic structures of S-hyperdoped Si have been extensively studied, using synchrotron-based x-ray photoelectron spectroscopy (XPS), x-ray absorption near-edge spectroscopy (XANES), extended x-ray absorption fine structure (EXAFS), valence-band photoemission spectroscopy (VB-PES) and first-principles calculation. S 2p XPS spectra reveal that the S-hyperdoped Si with the greatest (~87%) sub-band gap absorption contains the highest concentration of S(2-) (monosulfide) species. Annealing S-hyperdoped Si reduces the sub-band gap absorptance and the concentration of S(2-) species, but significantly increases the concentration of larger S clusters [polysulfides (Sn(2-), n > 2)]. The Si K-edge XANES spectra show that S hyperdoping in Si increases (decreased) the occupied (unoccupied) electronic density of states at/above the conduction-band-minimum. VB-PES spectra evidently reveal that the S-dopants not only form an impurity band deep within the band gap, giving rise to the sub-band gap absorption, but also cause the insulator-to-metal transition in S-hyperdoped Si samples. Based on the experimental results and the calculations by density functional theory, the chemical state of the S species and the formation of the S-dopant states in the band gap of Si are critical in determining the sub-band gap absorptance of hyperdoped Si samples.
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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 | 0.001 |
| 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".