Transition Metal Oxide Work Functions: The Influence of Cation Oxidation State and Oxygen Vacancies
Bibliographic record
Abstract
Abstract Transition metal oxides are capable of a wide range of work functions. This quality allows them to be used in many applications that involve charge transfer with adsorbed molecules, for example as heterogeneous catalysts, as charge‐injection layers in organic electronics, and as electrodes in fuel cells. Chemical and structural factors can alter transition‐metal oxide work functions, often making their work functions difficult to control. Little is known about the effects of the cation oxidation state and point defects on the oxide work function. It is necessary to understand how such chemical and structural factors affect work functions in order to controllably tune transition metal oxides for desired applications. Here, a correlation between the oxide work function and cation oxidation state is demonstrated. This correlation is attributed to the change in cation electronegativity with oxidation state. A model is presented that relates the work function to the oxygen deficiency for d 0 oxides in the limit of dilute oxygen vacancies. It is proposed that the rapid initial decrease in work function, observed for d 0 oxides, is caused by an increase in the density of donor‐like defect states. It is also shown that oxides tend to have decreased work functions near a metal/metal‐oxide interface as a consequence of the relationship between defects and work function. These insights provide guidelines for tuning transition metal oxide work functions.
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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 | 0.001 |
| 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".