Synthesis of WTe<sub>2</sub> Nanowires with Increased Electron Scattering
Bibliographic record
Abstract
Tungsten ditelluride (WTe 2 ) has many interesting properties such as its extremely large nonsaturating magnetoresistance and quantum spin Hall state in the monolayer limit. The anisotropic crystal structure of WTe 2 can allow for isolation of particular crystal directions to study the predicted Weyl states or crystal-symmetry-dependent magnetoresistance when studied at limited dimensions. In particular, the recent demonstration of superconductivity in WTe 2 monolayer suggests that realizing nanowire geometry for WTe 2 may be important to investigate potential Majorana zero modes predicted in one-dimensional topological superconductors. In this work, we demonstrate a large-yield, low-temperature synthesis of WTe 2 nanowires, an approximate one-dimensional system, by converting WO 3 nanowires via tellurization. The nanowires are single crystalline and have a higher resistivity than WTe 2 exfoliated flakes with similar thickness. The increased resistivity is attributed to increased scattering from imperfect surfaces and higher surface-to-volume ratios of the WTe 2 nanowires. We demonstrate that the synthesis method is generalizable to other transition-metal dichalcogenides, laying the foundation for further study of this class of materials in the one-dimensional limit.
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".