Trapping and detrapping of electrons photoinjected from silicon to ultrathin SiO2 overlayers. II. In He, Ar, H2, N2, CO, and N2O
Bibliographic record
Abstract
Photon-induced gas-assisted charging (PIGAC) of 1.5 nm thick SiO2 overlayers by photoemission from the Si substrate is demonstrated to be a universal feature for all gases. In our case (multi)photoemission is induced by high-intensity 800 nm, 150 fs pulses in samples at 295 K. O2 is more effective than other gases, probably due to the accumulation of surface charge following the formation of O2− on the surface. For the other gases, the efficiency decreases with increasing molecular (or atomic) size, pointing to a mechanism that is dependent on the proximity of the gas molecules to charge traps. Combined measurements of photoemission current and the contact-potential-difference detected charge spillover from the irradiated spot to the rest of the surface. Transfer of PIGAC electrons to long-lifetime charge traps was also detected for all gases. Its efficiency is the highest for He, probably due to the larger effective surface (and thus larger PIGAC) created by He penetration into the oxide layer. Detrapping of trapped electrons also occurs with PIGAC, and is particularly effective for CO and H2. Its mechanism and gas specificity are not understood as yet, but the strong increase of detrapping with decreasing temperature suggests a dependence on longer proximity of the gas molecules to the traps due to an increased surface residence time.
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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".