Y and La Doping in CaMnO<sub>3</sub> Compounds: Effects of Dopant Identity and Amount on Charge Transport Kinetics
Bibliographic record
Abstract
Enhancing electronic transport properties of thermoelectric oxides is of great technological importance. Oxides are promising candidates for waste heat harvesting at elevated temperatures as well as for electricity generation in low‐power applications. To this purpose, fundamental understanding of their electrical and thermal conduction mechanisms is essential. Herein, the conduction mechanism of CaMnO3 materials is focused on and how dopant identity and amount alter the kinetic properties of charge transport is investigated. Ca1−xRxMnO3 compounds with R = Y and La are synthesized, where 0 ≤ x ≤ 0.13, and the electrical conductivity and Seebeck coefficient for temperatures ranging from 300 to 1050 K, indicating that Y‐doped compounds are usually more conductive than their La‐doped counterparts, are measured. Analysis of both in terms of the small polaron hopping model reveals that Y doping reduces conduction activation energies, resulting in higher electrical conductivity and charge carrier mobility. Remarkably high values of thermoelectric power factor for the Ca0.97La0.03MnO3 compound, for example, 300 μWm−1 K−2 at 1050 K are observed; furthermore, these values are preserved for a wide temperature range, rendering this compound a good candidate for heat‐to‐electrical power generation at elevated temperatures.
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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".