MétaCan
Menu
Back to cohort
Record W3032332838

Thermal conductivity and Seebeck coefficient of Fe and Fe-Si alloys: Implications for variable Lorenz number

2017· article· en· W3032332838 on OpenAlexaff
Richard A. Secco

Bibliographic record

VenueAGUFM · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsWestern University
Fundersnot available
KeywordsWiedemann–Franz lawSeebeck coefficientThermal conductivityElectrical resistivity and conductivityCondensed matter physicsMaterials scienceThermoelectric materialsThermodynamicsThermoelectric effectPhysicsComposite material
DOInot available

Abstract

fetched live from OpenAlex

Abstract The Wiedemann-Franz Law is often used to calculate the thermal conductivity of Fe from experimental measurements of the electrical conductivity. It is shown by measurements of the Seebeck coefficient (S) of solid and liquid Fe at pressures up to 6 GPa and temperatures up to 2100 K that the Sommerfeld value ( L 0  = 2.445 × 10 −8  W Ω K −2 ) of the Lorenz number ( L ) represents more than 99% of the electronic component of the thermal conductivity of Fe. Using experimental values of electrical resistivity and thermal conductivity of Fe, L / L 0 is shown to vary by as much as 1.22 in the solid state and 1.32 in the liquid state, signifying a non-negligible phonon component. An expression for the pressure dependence of L at the melting boundary up to 5 GPa is derived for electron-phonon scattering. For Fe-Si alloys, L / L 0 varies more than for pure Fe and generally increases with increasing Si and state of disorder. New values for the conductive heat flow in a pure Fe core of Mercury are presented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.722

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 science0.0000.000
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 head0.025
GPT teacher head0.256
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAGUFMSame topicHigh-pressure geophysics and materialsFrench-language works237,207