Work of Adhesion Analysis for Metal-Substituted W<sub>4</sub>C<sub>4</sub> Carbides in a Cobalt Matrix
Bibliographic record
Abstract
The density of WC, which is greater than that of metals, can be reduced by partially substituting heavy W with metals, e.g., Mo and Cr, while retaining the desired strength. This makes them effective as reinforcements for hard-facing overlays and tool alloys, as they can be homogeneously dispersed in the metal matrix. Since it is unclear if the modified WC has good interfacial bonding with metals such as cobalt, one of the typical metal matrixes for hardfacing overlays, the interfacial bonding between cobalt and WC doped with Mo and Cr, respectively, was investigated via first principle calculations. The selected interfaces having the lowest interfacial mismatches with both HCP and FCC cobalt are (11 2 0) Carbide //(001) Co, (10 1 0) Carbide //(100) Co, (10 1 0) Carbide //(110) Co, and (0001) Carbide //(110) Co . The characteristics of created interfacial connections were analyzed using methods such as the electron localization function, electronic density of states, bond order, and net charge. It is demonstrated that WC carbides partially substituted with Mo and Cr (called (W 4– x, M)C 4, M = Mo or Cr) are adherent to Co as strong as or even better than that of mono-WC. The metal-substituted or doped W 4 C 4 carbides are promising candidates as reinforcements for hardfacing overlays, cutting tools, and bearings without interfacial bonding concerns.
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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) | 0.003 | 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".