Exploration of an electron work function - based strategy for tailoring materials
Bibliographic record
Abstract
P of materials are fundamentally dependent on their electron state, which is largely reflected by the electron work function (EWF). A higher work function corresponds to a more stable electronic state with a higher resistance to any attempt of changing the state or related states of a material, such as crystal structure or microstructure caused by mechanical and electrochemical processes. In this talk, close correlation between EWF and material properties will be demonstrated. With this simple characteristic parameter, many material intrinsic properties and processes could be analyzed without involving complex theoretical treatments. Particular attention will be put on the possibility of using EWF as a fundamental parameter for material design, which provides information or clues in a simple or straightforward way for material modification and development. Using Cu-Ni alloy as an example, the correlation between the electron work function (EWF) and mechanical and tribological properties will be demonstrated. One may see that properties of the alloy vary with the electron work function when composition changes, implying that properties of a material can be modified using elements with appropriate work functions. This should also be applicable for tailoring inter-phase boundaries or interfaces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".