Reducing thermal conductivity in Bi-Se co-doped InTe for next-generation thermoelectric materials
Bibliographic record
Abstract
Chalcogenide semiconductors remain a focal point in developing innovative, high-performance materials for energy conversion technologies. Among these, the narrow-band-gap p-type semiconductor InTe has garnered considerable interest due to its potential for thermoelectric applications. In this study, we significantly reduced the denominator of the figure of merit (ZT) by effectively lowering the thermal conductivity (κ) through strategic Bi/Se co-doping in the InTe matrix. Polycrystalline samples of InTe and Bi/Se co-doped InTe were synthesized using an environmentally sustainable solid-state reaction method. The co-doped samples achieved a remarkable minimum total thermal conductivity of 0.16 W/mK at 600 K, a 4.75-fold reduction compared to pristine InTe (0.76 W/mK). The XRD study confirmed phase stability, while FESEM and EDS analyses revealed uniform microstructures and effective dopant incorporation. Although carrier mobility decreased due to enhanced scattering at point defects and grain boundaries, pristine InTe achieved the highest ZT (∼0.13) at 600 K due to its superior power factor. This study presents Bi and Se co-doped InTe as a promising next-generation, eco-friendly thermoelectric material. The targeted doping strategy effectively reduces thermal conductivity, laying the groundwork for enhancing thermoelectric performance by optimizing the denominator term of the ZT parameter. While the current work primarily focuses on minimizing thermal conductivity, future efforts will aim at enhancing the power factor through precise control of dopant concentrations, striving to achieve a balanced improvement in thermoelectric efficiency.
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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".