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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 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 score0.007
Threshold uncertainty score0.025

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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; 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

Citations15
Published2019
Admission routes1
Has abstractyes

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