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Record W2955960973 · doi:10.1002/ange.201901106

Hybride organisch‐anorganische thermoelektrische Materialien und Baueinheiten

2019· article· de· W2955960973 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 · 2019
Typearticle
Languagede
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversity of Windsor
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsChemistry

Abstract

fetched live from OpenAlex

Abstract Organisch‐anorganische Hybridmaterialien gelten als neue Kandidaten auf dem Gebiet der thermoelektrischen Materialien. Sie haben ein großes Potenzial, die thermoelektrische Leistung zu verbessern, indem die niedrige Wärmeleitfähigkeit organischer Materialien einerseits sowie der hohe Seebeck‐Koeffizient und die hohe elektrische Leitfähigkeit anorganischer Materialien andererseits genutzt werden. In diesem Aufsatz soll ein Überblick über das Grenzflächen‐Engineering in der Synthese verschiedener organisch‐anorganischer thermoelektrischer Hybridmaterialien sowie über die dimensionale Gestaltung zur Optimierung ihrer thermoelektrischen Eigenschaften gegeben werden. Die Grenzflächeneffekte werden hinsichtlich Nanostrukturen, physikalischer Eigenschaften und chemischer Dotierung zwischen anorganischen und organischen Komponenten betrachtet. Einige Schlüsselfaktoren, die die thermoelektrische Effizienz und Leistung elektronischer Bauelemente bestimmen, werden ebenso diskutiert wie die Wärmeleitfähigkeit, der elektrische Transport, die elektronischen Bandstrukturen und die Bandkonvergenz der Hybridmaterialien.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.202
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0350.027

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2019
Admission routes1
Has abstractyes

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