XANES and XPS investigations of the local structure and final-state effects in amorphous metal silicates: (ZrO<sub>2</sub>)<sub>x</sub>(TiO<sub>2</sub>)<sub>y</sub>(SiO<sub>2</sub>)<sub>1−x−y</sub>
Bibliographic record
Abstract
Amorphous quaternary [(ZrO(2))(x)(TiO(2))(y)(SiO(2))(1-x-y)] and ternary [(ZrO(2))(x)(SiO(2))(1-x)] silicates were synthesized using a sol-gel method and examined via XPS and XANES. Metal silicates are important industrial materials, though structural characterization is complicated because of their amorphous nature. Hard (Ti K- and Zr K-edge) and soft (Ti L(2,3)-edge) X-ray XANES spectra suggest the Ti and Zr coordination numbers in the quaternary silicates remain constant as the metal identity or total metal content (x, y, or x + y in the chemical formula) is varied. XPS core-line spectra from the quaternary silicates show large decreases in Ti 2p(3/2), Zr 3d(5/2), Si 2p(3/2), and O 1s binding energies due to increasing final-state relaxation with greater next-nearest neighbour substitution of Si for less-electronegative Ti/Zr, which was confirmed by analysis of the O Auger parameter. These decreases in binding energy occur without any changes in the ground-state energies (e.g., oxidation state) of these atoms, as examined by Ti L(2,3)-edge, Si L(2,3)-edge, and O K-edge XANES. Because most spectroscopic investigations are concerned with ground-state properties, knowledge of the contributions from final-state effects is important to understand the spectra from materials of interest.
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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".