Thermoelectric Properties of Ni<sub>0.05</sub>Mo<sub>3</sub>Sb<sub>5.4</sub>Te<sub>1.6</sub> with Embedded SiC and Al<sub>2</sub>O<sub>3</sub> Nanoparticles
Bibliographic record
Abstract
Abstract First, a large sample of Ni 0.05 Mo 3 Sb 5.4 Te 1.6 was prepared by heating the elements in the stoichiometric ratio. β‐SiC nanoparticles were added in volume fractions, f , of 0.01, 0.02, and 0.034 to three different portions of the material, and a portion without SiC nanoparticles was retained as a reference. All four samples were subjected to consolidation by hot‐pressing. Furthermore, Al 2 O 3 nanoparticles were added in volume fractions of f = 0.01, 0.0216, and 0.0325 to three other portions, again retaining a fourth protion as reference. These four samples were consolidated by spark‐plasma sintering. The thermoelectric transport properties of these composites were characterized from 325 to 740 K. For the sample with 0.01 volume fraction of SiC, there was an enhancement in figure of merit by 18 % compared with the reference sample, mainly due to an 18 % reduced thermal conductivity. The 9 % reduction in thermal conductivity of the sample with 0.01 volume fraction of Al 2 O 3 was not enough to compensate the loss in the power factor of 18 %, leading to a decrease in the figure of merit. Microstructural information obtained by SEM, TEM, and BET was used to elucidate the phase and transport properties. The spark‐plasma‐sintered bulk sample has a figure of merit that is 35 % higher than the bulk sample consolidated through hot‐pressing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".