Electron work function guided tailoring of (W4-x, Mx)C4 /doped Ni matrix interfacial bonding: Insights from first-principles calculations
Bibliographic record
Abstract
Heavy tungsten carbide (WC) may cause inhomogeneous distributions in WC-metal matrix composite hardfacing overlays, thus negatively affecting its performance as reinforcement. WC can be lightened by partially substituting W with lighter metals, e.g., Mo and Cr, while maintaining its strength. However, the bonding between modified WC and matrix metals such as nickel (a typical metal-matrix for overlays) is uncertain. This article reports a study on the interfacial bonding between (W 4-x , M x )C 4 ( M =Mo or Cr) and Ni matrix via first-principle calculations. Different atomic interactions i.e., metal-metal and metal-carbon interactions, at the interface were studied to understand the interfacial bonding through analyses of electron work function (EWF), electron localization function, electronic density of states, bond order, and net charge. It was demonstrated that the lighter (W 4-x , M x )C 4 carbides exhibit strong bonding with Ni, comparable to or even stronger than that of WC/Ni interface, and the interfacial bonding includes covalent, ionic and metallic bond components. It is demonstrated that the interfacial bonding can be tuned by doping elements in the Ni matrix with different EWFs, e.g., Mn, Cu, Au, Pt, and Se. Efforts have been made to verify a hypothesis that EWF is an indicator, which can be used to guide selecting effective dopants for tailoring the interfacial bond strength.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".