Thermoelectric properties of Ni0.05Mo3Sb5.4Te1.6 composites with NiSb nanocoating
Bibliographic record
Abstract
NiSb nanoparticles by 0.034, 0.074 and 0.16 volume fractions were successfully coated on bulk polycrystalline Ni0.05Mo3Sb5.4Te1.6 thermoelectric (TE) particles through a solvothermal route without deteriorating the bulk Ni0.05Mo3Sb5.4Te1.6 material. The samples were consolidated through hot pressing and their thermoelectric (TE) properties were characterized. At 400 K, 500 K, and 600 K, 0.074 NiSb sample exhibited 22%, 16% and 11.3% increases in the power factor (P.F.) compared to bulk material. The main contributing factor to this enhanced power factor is the elevated electrical conductivity. For the same sample, the reciprocal relationship between Seebeck coefficient and electrical conductivity is decoupled. Sample 0.16 NiSb exhibited the highest electrical conductivity among the three samples. The thermal conductivity of the 0.16 sample was less temperature sensitive compared to other samples. HRTEM and SEM tools were applied to comprehend microstructural features and their relationship to TE transport properties. Pore effect on the thermal and electrical conductivity was elucidated. This study shows that grain-boundary manipulation via this wet chemistry technique is indeed an economically viable method to fabricate and optimize the transport properties of bulk TE materials.
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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".