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Record W2621800374 · doi:10.1038/am.2017.77

Intrinsically low thermal conductivity from a quasi-one-dimensional crystal structure and enhanced electrical conductivity network via Pb doping in SbCrSe3

2017· article· en· W2621800374 on OpenAlexfundno aff
Dingfeng Yang, Wei Yao, Yanci Yan, Wujie Qiu, Lijie Guo, Xu Lu, Ctirad Uher, Xiaodong Han, Guoyu Wang, Tao Yang, Xiaoyuan Zhou

Bibliographic record

VenueNPG Asia Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsnot available
FundersChongqing Institute of Green and Intelligent Technology, Chinese Academy of SciencesFundamental Research Funds for the Central UniversitiesChinese Academy of SciencesNational Natural Science Foundation of ChinaCanada Excellence Research Chairs, Government of CanadaU.S. Department of Energy
KeywordsMaterials scienceThermal conductivityThermoelectric materialsThermoelectric effectAnharmonicityDopingCondensed matter physicsLone pairPhononElectronOptoelectronicsNanotechnologyComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

The development of new routes for the production of thermoelectric materials with low-cost and high-performance characteristics has been one of the long-term strategies for saving and harvesting thermal energy. Herein, we report a new approach for improving thermoelectric properties by employing the intrinsically low thermal conductivity of a quasi-one-dimensional (quasi-1D) crystal structure and optimizing the power factor with aliovalent ion doping. As an example, we demonstrated that SbCrSe3, in which two parallel chains of CrSe6 octahedra are linked by antimony atoms, possesses a quasi-1D property that resulted in an ultra-low thermal conductivity of 0.56 W m−1 K−1 at 900 K. After maximizing the power factor by Pb doping, the peak ZT value of the optimized Pb-doped sample reached 0.46 at 900 K, which is an enhancement of 24 times that of the parent SbCrSe3 structure. The mechanisms that lead to low thermal conductivity derive from anharmonic phonons with the presence of the lone-pair electrons of Sb atoms and weak bonds between the CrSe6 double chains. These results shed new light on the design of new and high-performance thermoelectric materials. A material that is counter-intuitively both a good electrical conductor and thermal insulator has been made by a team in China and the USA. The ideal thermoelectric material would have a high electrical conductivity and a low thermal conductivity. But this is challenging as both properties are facilitated by electrons. Now, Guoyu Wang (Chinese Academy of Sciences), Tao Yang, Xiaoyuan Zhou (Chongqing University) and their colleagues present an idea for seeking and optimizing low-dimensional thermoelectric materials. They started with a material with an intrinsically low thermal conductivity due to its atomic structure — the atoms in SbCrSe3 form one-dimensional chains held together by weak bonds, which inhibits thermal conduction. They then boosted the material's electrical conductivity by adding lead atoms. This approach could be applied to other materials with a similar one-dimensional atomic structure. SbCrSe3, described as a quasi-1D structure with CrSe6 double chains, possesses an intrinsic low thermal conductivity due to the large anharmonicity and weak chemical bonds. By substituting Sb with Pb, a peak ZT= 0.46 at 900 K is obtained in Pb0.05Sb0.95CrSe3 sample. This is about 24 times larger value than measured on pristine SbCrSe3.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.253
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations65
Published2017
Admission routes1
Has abstractyes

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