Enhancing the Hardness of Superhard Transition-Metal Borides: Molybdenum-Doped Tungsten Tetraboride
Bibliographic record
Abstract
By creation of solid solutions of the recently explored low-cost superhard boride, tungsten tetraboride (WB 4 ), the hardness can be increased. To illustrate this concept, various concentrations of molybdenum (Mo) in WB 4, that is, W 1– x Mo x B 4 ( x = 0.00–0.50), were systematically synthesized by arc melting from the pure elements. The as-synthesized samples were characterized using energy-dispersive X-ray spectroscopy (EDS) for elemental analysis, powder X-ray diffraction (XRD) for phase identification, Vickers microindentation for hardness testing, and thermal gravimetric analysis for determining the thermal stability limit. While the EDS analysis confirmed the elemental purity of the samples, the XRD results indicated that Mo is completely soluble in WB 4 over the entire concentration range studied (0–50 at. %) without forming a second phase. When 3 at. % Mo is added to WB 4, Vickers hardness values increased by about 15% from 28.1 ± 1.4 to 33.4 ± 0.9 GPa under an applied load of 4.90 N and from 43.3 ± 2.9 to 50.3 ± 3.2 GPa under an applied load of 0.49 N. Thermal gravimetric analysis revealed that the powders of this superhard solid solution, W 0.97 Mo 0.03 B 4, are thermally stable in air up to ∼400 °C. These results indicate that the hardness of superhard transition-metal borides may be enhanced by making solid solutions with small amounts of other transition metals, without introducing a second phase to their structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".