Stacking Fault Energy of Si Nanocrystals Embedded in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>SiO</mml:mtext></mml:mrow><mml:mn mathvariant="bold">2</mml:mn></mml:msub></mml:mrow></mml:math>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Si nanocrystals (Si nc) were produced by the implantation of Si + into a SiO 2 film on (100) Si, followed by high-temperature annealing. High-resolution transmission electron microscopy (HRTEM) observation has shown that a perfect dislocation (Burgers vector <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi mathvariant="bold">b</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mn>(1</mml:mn><mml:mo>/</mml:mo><mml:mn>2)</mml:mn></mml:mrow><mml:mo>〈</mml:mo><mml:mn>110</mml:mn><mml:mo>〉</mml:mo></mml:mrow></mml:math>) can dissociate into two Shockley partials (Burgers vector <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi mathvariant="bold">b</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mn>(1</mml:mn><mml:mo>/</mml:mo><mml:mn>6)</mml:mn></mml:mrow><mml:mo>〈</mml:mo><mml:mn>112</mml:mn><mml:mo>〉</mml:mo></mml:mrow></mml:math>) bounding a strip of stacking faults (SFs). The width of the SFs has been determined from the HRTEM image, and the stacking fault energy for Si nc has been calculated. The stacking fault energy for Si nc is compared with that for bulk Si, and the formation probability of defects in Si nc is also discussed. The results will shed a light on the dissociation of dislocations in nanoparticles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.343 | 0.001 |
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 it