Determination of the thermo-electronic and structural properties of polycrystalline metals and single-crystal semiconductors
Bibliographic record
Abstract
• Correlation established between thermal, electrical, and structural properties. • FDPA, TRM, and Van der Pauw applied to characterize thermo-electronic properties. • Analysis includes polycrystalline metals and semiconductors with varying structures. • Thermal conductivity strongly depends on crystallite size. This study explores the correlation between thermal, electrical, and structural properties of metals, alloys, and single-crystal semiconductors using photothermal techniques, electrical methods, and X-ray diffraction. Polycrystalline metals (Al, Pt, Ti, Cu, AISI 1030 steel, α-brass) and semiconductors (Si, GaSb-Te) were systematically analyzed. Thermal diffusivity was measured using Frequency Domain Photoacoustic (FDPA), and thermal conductivity and volumetric heat capacity using the Thermal Relaxation Method (TRM). A thermal diffusivity image for a Si sample was taken to show the influence of mechanical damage on the thermal transport properties. The structural properties were determined using X-ray diffraction, while the electrical properties were evaluated using the Van der Pauw method. The results show a strong correlation between the thermal, electrical, and structural properties of the materials, specifically with respect to crystallite size and space group. The thermal conductivity is influenced by the crystallite size, while electrical conductivity varies due to extrinsic factors, especially for polycrystalline metals. These results suggest that the Wiedemann-Franz theory needs to be re-evaluated by considering both intrinsic and extrinsic influences on material behavior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".