Thermoelectric performance of Ni, Co, and Fe nanoparticles incorporated into their metal borates glassy matrices
Bibliographic record
Abstract
Abstract Here, we present our current attempt to intrinsically dope Ni 0 , Co 0 , and Fe 0 nanoparticles within Ni II ‐, Co II ‐, and Fe II ‐borate glassy matrices, respectively. The system was prepared by one‐pot reaction of the desired M T II salt with excess NaBH 4 through an in‐situ reduction and hydrolysis processes to afford metallic M T 0 nanoparticles dispersed into the M T ‐BO 3 matrix. The composition and structural characteristics of these M T 0 :M T ‐BO 3 materials were identified by thermal oxidation, ATR‐IR, X‐ray powder diffraction, and magnetic techniques as glassy/amorphous borate matrices containing magnetic nanoparticles. The electrical conductivity ( σ ) of cold‐pressed discs of these metal‐doped composites shows that they behave as nonohmic semiconductors within the temperature range of 303 ≤ T ≤ 373 K suggesting a mixed electronic‐ionic conduction. However, their thermal conductivity ( κ ) occurs through phonon lattice vibration dynamics rather than electronic. The σ / κ ratio shows a steep non‐linear increase from 9.4 to 270 KV −2 in Ni 0 :Ni‐BO 3 . In contrast, a moderate‐weak increase is observed for Co 0 :Co‐BO 3 and Fe 0 :Fe‐BO 3 analogs. The obtained materials are examined for thermoelectric (TE) applications by determining their Seebeck coefficient ( S ) power factor (PF), figure of merit (ZT), and conversion efficiency ( η %). All the TE data shows that Ni 0 :Ni‐BO 3 ( S , 80 μVK −1 ; PF, 97.7 mWm −1 K −1 ; ZT 0.54; η , 2.15%) is a better TE semiconductor than the other two M T 0 :M T ‐BO 3 . This finding shows that Ni 0 :Ni‐BO 3 is a promising candidate to exploit low‐temperature waste heat from body heat, sunshine, and small domestic devices for small‐scale TE applications.
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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 | 0.000 | 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) | 0.000 | 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".