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Metal Pnictides: Structures and Thermoelectric Properties

2017· other· en· W2973156563 on OpenAlexaff
Abdeljalil Assoud, Holger Kleinke

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThermoelectric effectTernary operationMaterials scienceMetalLithium (medication)Thermoelectric materialsSuperconductivityCrystal structureThermal conductivityNanotechnologyCondensed matter physicsCrystallographyChemistryMetallurgyPhysicsComposite materialThermodynamicsComputer science

Abstract

fetched live from OpenAlex

Abstract This chapter provides an overview of the structures and thermoelectric properties of metal pnictides, with focus on the antimonides. In addition, several references are provided in the introduction to refer the reader to the other potential uses of metal pnictides, including applications in hydrogen storage, lithium batteries, photocatalysis, heterogeneous catalysis, lithium‐ion conductors, and superconductivity.We present an encompassing picture of the binary antimonides of interest, group by group, followed by selected ternary antimonides. In addition to the heavily investigated family of filled skutterudites, several other antimonides with highly complex crystal structures exhibit outstanding thermoelectric performance, mostly because of their extraordinarily low thermal conductivity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.230
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.000

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 head0.041
GPT teacher head0.283
Teacher spread0.242 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations2
Published2017
Admission routes1
Has abstractyes

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