Effect of bonding and static atomic displacements on composition quantification in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>In</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mrow><mml:mtext>Ga</mml:mtext></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mtext>N</mml:mtext><mml:mi>y</mml:mi></mml:msub><mml:msub><mml:mrow><mml:mtext>As</mml:mtext></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>
Bibliographic record
Abstract
This study addresses the influence of static atomic displacements (SAD) and chemical bonding in (InGa)(NAs) alloys on structure factors for electron scattering and stresses their importance for compositional analysis. First, SAD are derived using valence force field (VFF) methods and their reliability is demonstrated by calculating residual atomic forces using density-functional theory (DFT). A systematic study of structure factors for low indium and nitrogen contents is given by means of full DFT calculations on the one hand and atomistic models on the other. We show that the consideration of SAD via VFF together with the inclusion of bonding via modified atomic scattering amplitudes is in best agreement with a full DFT calculation, providing the possibility to include bonding effects also if large cells containing ${10}^{6}$ atoms are considered. Second, a technique is presented, which allows the extraction of atomically resolved indium and nitrogen composition maps by evaluating strain and contrast in transmission electron microscopic two-beam images simultaneously. As the fringe contrast is compared with Bloch wave simulations, which in turn require accurate structure factors, we exemplify the influence of SAD and bonding on composition profiles regarding an ${\text{In}}_{0.08}{\text{Ga}}_{0.92}{\text{N}}_{0.03}{\text{As}}_{0.97}$ quantum well structure. In this respect, imaging conditions may be chosen for which SAD are of minor influence for conventional transmission electron microscopy, whereas relative errors larger than 25% can arise for the compositions if bonding is neglected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.005 | 0.010 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.906 | 0.009 |
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; both teacher heads agree on what is shown here.
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".