Effect of addition of SiC and Al2O3 refractories on Kapitza resistance of antimonide-telluride
Bibliographic record
Abstract
Invoking Effective Media Percolation theory (EMPT), Hasselaman-Johnson effective media theory (EMT), and Nan and Birringer EMT, the effect of addition of SiC and Al2O3 nanoparticles on Kapitza resistance (RBd) of Ni0.05Mo3Sb5.4Te1.6 was investigated. Pore size and their volume distribution, and surface area were characterized using BET technique to correlate pore effect and surface area on RBd. Bounds for effective thermal conductivity were determined using Lipton–Vernescu model. Variation of thermal conductance with respect to temperature was studied and compared with the results of other materials. According to EMPT, RBd in Ni0.05Mo3Sb5.4Te1.6/SiC composites ranged from 3.84 × 10-7 to 5.42 × 10-7 m2KW–1 and 3.36 × 10-7 to 3.86 × 10-7 m2KW–1 for Ni0.05Mo3Sb5.4Te1.6/Al2O3 composites. Kapitza radius (aK) for SiC samples was ranged between 2.01 – 2.84 μm; for Al2O3 samples it was 1.86 μm. Hasselman-Johnson model gave RBd values 55%, 51%, and 8% more than what EMPT is predicting, but of the same order and aK values 3.5 μm, 4 μm, 3 μm for SiC samples and 1.2 μm, 0.6 μm, 0.55 μm for Al2O3 samples. Nan-Birringer model yielded large aK of 7.25 μm and RBd ∼ 1.4 × 10–6 m2KW–1 for Ni0.05Mo3Sb5.4Te1.6/SiC. So obtained parameters are reasonable estimates. Variation of effective thermal conductivity in Al2O3 samples is more sensitive to particle size compared to SiC samples. Mechanical properties were studied using micro–indentation technique and their effect on effective thermal properties was ascertained. Addition of Al2O3 nanoparticles have aided in enhancing mechanical properties of bulk material.
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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 | 0.000 | 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 | 0.001 |
| 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".