<b>The Effect of Scattering of Phonons, Surface and Grain Boundary on electrical Properties for Tungsten Nanometallic</b>
Bibliographic record
Abstract
In the context of increasing the use of nanometals in electrical and electronic applications and improving their unique electrical properties, this research explains the effect of the mechanisms of scattering of phonons at room-temperature (293K) in addition to the scattering and reflection of electrons at the surface and at grain boundary on the electrical resistivity of Tungsten metal at different thicknesses. The electrical resistivity of Tungsten was obtained by solving the Boltzmann transport equation which the electron scattering coefficient at the surface (p) is calculated by the Fuch-Sondheimer model, and the grain boundary reflection coefficient (R) by the Mayadas-Shatzkes model were calculated as (p=0.89) and (R=0.18) for Tungsten metal based on the mean of the free path of the electrons. The results showed that there is a linear relationship between the mechanisms of scattering and resistivity, and an inverse relation between electrical resistivity (ρ) and the thickness of the nanometal (d) and extending to a large range of thicknesses. Moreover, the defects of the crystal lattice and the roughness of the surface have an evident impact on the electrical properties of Tungsten metal. In addition, we obtained an excellent consistency between experimental data and theoretical results of electrical resistivity. These results provide important predictions for the use of nano-Tungsten as an interconnection between micro integrated electronic circuits and in various electrical devices
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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) | 0.001 | 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".