Self‐Recovery of Carbonate‐Contaminated Strontium Titanate (100) Vicinal Surfaces Imaged by Tip‐Enhanced Raman Spectroscopy
Bibliographic record
Abstract
Abstract Strontium titanate (SrTiO3) as a model perovskite has significant applications in catalysis, carbon capture, and advanced electronics. On SrO‐terminated (100) surfaces, carbon dioxide (CO2) is a common chemisorption, altering the electronic and chemical properties. This study employed tip‐enhanced Raman spectroscopy (TERS) and density functional theory (DFT) simulations to explore this CO2 chemisorption. The (100) surface of SrTiO3 exhibits two distinct terminations, SrO and TiO2 with nominally almost the same heights (0.2 nm). Height scans of hydrothermally treated (100) SrTiO3, show values closer to 0.3 and 0.1 nm, where we attribute the difference in height to the selective adsorption of ambient CO2 on one of the terminations. The TERS analysis shows the presence of a 1071 cm−1 Raman peak (characteristic of carbonate vibration), localized exclusively at the SrO terrace, confirming that CO2 preferentially adsorbs onto SrO. Both experimental and DFT results indicate that this CO2 monolayer alters the binding energy between the SrO and TiO2 terminations. This leads to spontaneous yet slow delamination of SrO and the emergence of SrCO3 nanograins on a purely TiO2‐terminated crystal surface. The interpretation is in quantitative agreement with respective volumes of layers and grains throughout the process.
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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".