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Record W2003501744 · doi:10.1088/0953-8984/21/2/025602

Electron–phonon coupling and spin fluctuations in 3d and 4d transition metals: implications for superconductivity and its pressure dependence

2008· article· en· W2003501744 on OpenAlexafffund
Sangita Bose

Bibliographic record

VenueJournal of Physics Condensed Matter · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCondensed matter physicsSuperconductivityElectronCoupling constantFermi levelSpin (aerodynamics)Atom (system on chip)Valence electronDensity of statesTransition metalPhysicsChemistryQuantum mechanicsThermodynamics

Abstract

fetched live from OpenAlex

We have calculated the electron-phonon coupling for the complete 4d series and the nonmagnetic 3d transition metals using the linear response method and the linear muffin-tin orbitals' basis. A comparison of the linear response results and those obtained via the rigid muffin-tin approximation is provided. Based on the calculated values of the electron-phonon coupling constants, band density of states and the measured values of the electronic specific heat constants, we estimate the spin-fluctuation effects, i.e. the electron-spin-fluctuation (electron-paramagnon) coupling constants in these systems. For the sake of comparison, several other metals, Cu, Zn, Ag, Cd, Al and Pb, are also studied. Alternative estimates of the electron-paramagnon coupling constants are obtained from the values of the Stoner parameters and the band densities of states at the Fermi level. Implications of these results on the superconductivity and its pressure dependence as well as the alloying effects of superconductivity in these systems are discussed. It is pointed out that spin fluctuations play an important role in the validity of the Matthias rule that in metallic systems the optimum conditions for (electron-phonon) superconductivity occur for 5 and 7 valence electrons/atom.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.508

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 communication0.0000.001
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 head0.022
GPT teacher head0.283
Teacher spread0.261 · 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 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

Citations21
Published2008
Admission routes2
Has abstractyes

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