The effect of powder pre-treatment on the mechanical and thermoelectric properties of spark plasma sintered N-type bismuth telluride
Bibliographic record
Abstract
The effect of N-type (Bi 0.95 Sb 0.05 ) 2 (Se 0.05 Te 0.95 ) 3 powder pre-treatments, including annealing and powder oxide reduction, was determined in terms of both the mechanical and thermoelectric properties for bodies fabricated using spark plasma sintering (SPS). The uniaxial tensile strength was measured and the figure of merit “ZT” was calculated both parallel and perpendicular to the sintering pressure direction. The powder oxide reduction pre-treatment was carried out using 5% H 2 ˗ 95% Ar (vol%) in an in-house built mechanically agitated fluidized bed reduction facility at 380 °C for 24 h, while annealing was carried out at the same temperature and holding time using high purity Ar. Both pre-treatments were compared to the as-manufactured baseline powder sample that was dried under vacuum for 24 h. It was determined that the oxide content decreased from 0.362 to 0.143 wt% after H 2 reduction, while it decreased to 0.341 wt% after annealing. The increase in the mechanical properties was found to be mainly due to the annealing pre-treatment, which resulted in an increased relative density of the sintered samples from 97.0% to 98.0%. The annealing pre-treatment increased the characteristic uniaxial tensile strength from 30.4 to 34.1 MPa in the parallel direction and from 30.8 to 38.0 MPa in the perpendicular direction . The increase in the ZT is attributed to the increase in the Seebeck coefficient , which is in turn due to a decrease in the carrier concentration, as was found by examining the literature. The ZT max (150 °C) increased from 0.54 to 0.63 versus the dried powder in both the parallel and perpendicular directions due to powder oxide reduction and 0.58 and 0.62 in the parallel and the perpendicular directions, respectively, due to annealing versus the dried powder.
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 | 0.001 | 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) | 0.000 | 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; a candidate call from one teacher head, 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".