Interfacial bonding between iron and Mo- and Cr-doped tungsten carbides
Bibliographic record
Abstract
Doping or partially substituting WC with metals, e.g., Mo and Cr, can lower its density while keeping the strength of the modified carbides similar to that of WC, making them attractive as the reinforcement for hardfacing overlays and tool steels, since they can be distributed homogeneously in the metal matrix. However, it is unclear if the doped WC has desirable interfacial bonding with the matrix, e.g., iron. In this study, we investigated the interfacial bonding of Mo- and Cr-doped WC, compared to that of mono-WC, with austenite and ferrite irons via first-principles calculations. (112¯0)Carbide//(110)Fe, (101¯0)Carbide//(100)Fe, and (0001)Carbide//(100)Fe interfaces for both ferrite and austenite with the lowest interfacial mismatch were investigated. Characteristics of the formed interfacial bonds were studied based on the electron localization function, electronic density of states, bond order, and net charge. It was demonstrated that the Mo and Cr-doped WC carbides, (W4−x, M)C4, show comparable or higher adhesive work with iron, compared to that of mono-WC with iron. The metal-substituted or doped W4C4 carbides are promising replacements of heavier WC for tool steels and ferrous hardfacing overlays.
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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 | 0.001 | 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".