Metal/Metal‐Oxide Interfaces: How Metal Contacts Affect the Work Function and Band Structure of MoO<sub>3</sub>
Bibliographic record
Abstract
Abstract When transition metal oxides are used in practical applications, such as organic electronics or heterogeneous catalysis, they often must be in contact with a metal. Metal contacts can affect an oxide's chemical and electronic properties within the first few nanometers of the contact, resulting in changes to an oxide's chemical reactivity, conductivity, and energy‐level alignment properties. These effects can alter an oxide's ability to perform its intended function. Thus, the choice of contacting metal becomes an important design consideration when tailoring the properties of transition‐metal oxide thin films or nanoparticles. Here, metal/metal‐oxide interfaces involving a widely used oxide in organic electronics, MoO3, are examined. It is demonstrated that metal contacts tend to reduce the Mo6+ cation to lower oxidation states and, consequently, alter MoO3’s valence electronic structure and work function when the oxide layer is very thin (less than 10 nm). MoO3 becomes semimetallic and has a lower work function near metal contacts. The observed behavior is attributed to two causes: 1) charge transfer from the metal Fermi level into MoO3’s low‐lying conduction band and 2) an oxidation‐reduction reaction between the metal and MoO3 that results in oxidation of the metal and reduction of MoO3. These results illustrate how interfaces are important to an oxide's ability to provide energy‐level alignment.
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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".