Rapid Quantification of Film Thickness and Metal Loading for Electrocatalytic Metal Oxide Films
Bibliographic record
Abstract
The thicknesses and metal loadings of amorphous nickel, iron, and iridium oxide films widely used for solar fuel electrocatalysis were determined by cross-sectional scanning electron microscopy (SEM) and X-ray fluorescence (XRF) spectroscopy measurements. The thicknesses for a series of films, which were systematically varied from 10 to 400 nm using photodeposition techniques, were accurately measured by cross-sectional SEM using a protocol that successfully resolves the relevant catalyst layers. XRF measurements recorded on each of the films provided a strong linear correlation ( R 2 > 0.97) with the thicknesses determined by cross-sectional SEM. The electrochemical surface areas (ECSAs) determined by double-layer capacitance measurements, a technique widely used in the electrocatalysis community, showed a linear relationship for iridium oxide film thicknesses but not with those consisting of nickel and iron. These results highlight the limitations of using ECSA to determine catalyst film thicknesses and metal loadings. The noninvasive XRF technique is demonstrated to be a far superior method for reporting on the thickness and loadings of thin metal oxide films.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".