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Record W1814723858 · doi:10.1139/p11-093

Irreversible thermodynamics of transport across interfaces

2011· article· en· W1814723858 on OpenAlexvenueno aff
Matthew R. Sears, Wayne M. Saslow

Bibliographic record

VenueCanadian Journal of Physics · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsEntropy productionPhysicsCondensed matter physicsElectric currentCurrent (fluid)MagnetizationSpintronicsHeat currentClassification of discontinuitiesThermodynamicsElectrochemical potentialEntropy (arrow of time)Spin (aerodynamics)Work (physics)Thermal conductionMagnetic fieldElectrodeElectrochemistryQuantum mechanicsFerromagnetism

Abstract

fetched live from OpenAlex

With spintronics applications in mind, we use irreversible thermodynamics to derive the rates of entropy production and heating near an interface when heat current, electric current, and spin current cross it. Associated with these currents are apparent discontinuities in temperature (ΔT), electrochemical potential (Δ[Formula: see text]), and spin-dependent “magnetoelectrochemical potential” (Δ[Formula: see text]). This work applies to magnetic semiconductors and insulators as well as metals, because of the inclusion of the chemical potential, μ, which is usually neglected in works on interfacial thermodynamic transport. We also discuss the (nonobvious) distinction between entropy production and heat production. Heat current and electric current are conserved, but spin current is not, so it necessitates a somewhat different treatment. At low temperatures or for large differences in material properties, the surface heating rate dominates the bulk heating rate near the surface. We also consider the case where bulk spin currents occur in equilibrium. Although a surface spin current (in A/m 2 ) should yield about the same rate of heating as an equal surface electric current, production of such a spin current requires a relatively large “magnetization potential” difference across the interface.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.850

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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