Origin of Intrinsically Low Thermal Conductivity in Talnakhite Cu<sub>17.6</sub>Fe<sub>17.6</sub>S<sub>32</sub> Thermoelectric Material: Correlations between Lattice Dynamics and Thermal Transport
Bibliographic record
Abstract
Understanding the nature of phonon transport in solids and the underlying mechanism linking lattice dynamics and thermal conductivity is important in many fields, including the development of efficient thermoelectric materials where a low lattice thermal conductivity is required. Herein, we choose the pair of synthetic chalcopyrite CuFeS 2 and talnakhite Cu 17.6 Fe 17.6 S 32 compounds, which possess the same elements and very similar crystal structures but very different phonon transport, as contrasting examples to study the influence of lattice dynamics and chemical bonding on the thermal transport properties. Chemically, talnakhite derives from chalcopyrite by inserting extra Cu and Fe atoms in the chalcopyrite lattice. The CuFeS 2 compound has a lattice thermal conductivity of 2.37 W m –1 K –1 at 625 K, while Cu 17.6 Fe 17.6 S 32 features Cu/Fe disorder and possesses an extremely low lattice thermal conductivity of merely 0.6 W m –1 K –1 at 625 K, approaching the amorphous limit κ min . Low-temperature heat capacity measurements and phonon calculations point to a large anharmonicity and low Debye temperature in Cu 17.6 Fe 17.6 S 32, originating from weaker chemical bonds. Moreover, Mössbauer spectroscopy suggests that the state of Fe atoms in Cu 17.6 Fe 17.6 S 32 is partially disordered, which induces the enhanced alloy scattering. All of the above peculiar features, absent in CuFeS 2, contribute to the extremely low lattice thermal conductivity of the Cu 17.6 Fe 17.6 S 32 compound.
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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".