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Record W2915713574 · doi:10.1002/anie.201901106

Hybrid Organic–Inorganic Thermoelectric Materials and Devices

2019· review· en· W2915713574 on OpenAlexaff
Huile Jin, Jun Li, James Iocozzia, Xin Zeng, Pai‐Chun Wei, Chao Yang, Nan Li, Zhaoping Liu, Jr‐Hau He, Tiejun Zhu, Jichang Wang, Zhiqun Lin, Shun Wang

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversity of Windsor
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsThermoelectric effectMaterials scienceThermoelectric materialsEnvironmental scienceProcess engineeringEngineering physicsComposite materialEngineeringPhysicsThermal conductivityThermodynamics

Abstract

fetched live from OpenAlex

Hybrid organic-inorganic materials have been considered as a new candidate in the field of thermoelectric materials since the last decade owing to their great potential to enhance the thermoelectric performance by utilizing the low thermal conductivity of organic materials and the high Seebeck coefficient, and high electrical conductivity of inorganic materials. Herein, we provide an overview of interfacial engineering in the synthesis of various organic-inorganic thermoelectric hybrid materials, along with the dimensional design for tuning their thermoelectric properties. Interfacial effects are examined in terms of nanostructures, physical properties, and chemical doping between the inorganic and organic components. Several key factors which dictate the thermoelectric efficiency and performance of various electronic devices are also discussed, such as the thermal conductivity, electric transportation, electronic band structures, and band convergence of the hybrid materials.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.023
GPT teacher head0.290
Teacher spread0.266 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations231
Published2019
Admission routes1
Has abstractyes

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