Phase Quantification of Heterogeneous Surfaces Using DFT-Simulated Valence Band Photoemission Spectra
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Quantifying the crystallographic phases present at a surface is an important challenge in fields such as functional materials and surface science. X-ray photoelectron spectroscopy (XPS) is routinely employed in surface characterization to identify and quantify chemical species through core line analysis. Valence band (VB) spectra contain characteristic but complex features that provide information on the electronic density of states (DoS) and thus can be understood theoretically using density functional theory (DFT). Here, we present a method of fitting experimental photoemission spectra with DFT models for quantitative analysis of heterogeneous systems, specifically mapping the anatase to rutile ratio across the surface of mixed-phase TiO 2 thin films. The results were correlated with mapped photocatalytic activity measured using a resazurin-based smart ink. This method allows large-scale functional and surface composition mapping in heterogeneous systems and demonstrates the unique insights gained from DFT-simulated spectra on the electronic structure origins of complex VB spectral features.
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 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) | 0.003 | 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".