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Record W1412375272 · doi:10.1038/srep12823

The electrical conductivity of Al2O3 under shock-compression

2015· article· en· W1412375272 on OpenAlexafffund
Hanyu Liu, John S. Tse, W. J. Nellis

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsUniversity of Saskatchewan
FundersNational Natural Science Foundation of ChinaWestern Canada Research GridCompute CanadaUniversity of SaskatchewanNational Science Foundation
KeywordsSapphireMaterials scienceCompression (physics)Shock (circulatory)OpacityElectrical resistivity and conductivityConductivityShock frontThermal conductivityThermodynamicsThermodynamic equilibriumMetalScatteringShock waveComposite materialMetallurgyChemistryOpticsPhysicsLaserPhysical chemistry

Abstract

fetched live from OpenAlex

Sapphire (Al2O3) crystals are used below 100 GPa as anvils and windows in dynamic-compression experiments because of their transparency and high density. Above 100 GPa shock pressures, sapphire becomes opaque and electrically conducting because of shock-induced defects. Such effects prevent temperature and dc conductivity measurements of materials compressed quasi-isentropically. Opacities and electrical conductivities at ~100 GPa are non-equilibrium, rather than thermodynamic parameters. We have performed electronic structure calculations as a guide in predicting and interpreting shock experiments and possibly to discover a window up to ~200 GPa. Our calculations indicate shocked sapphire does not metallize by band overlap at ~300 GPa, as suggested previously by measured non-equilibrium data. Shock-compressed Al2O3 melts to a metallic liquid at ~500 GPa and 10,000 K and its conductivity increases rapidly to ~2000 Ω(-1)cm(-1) at ~900 GPa. At these high shock temperatures and pressures sapphire is in thermal equilibrium. Calculated conductivity of Al2O3 is similar to those measured for metallic fluid H, N, O, Rb, and Cs. Despite different materials, pressures and temperatures, and compression techniques, both experimental and theoretical, conductivities of all these poor metals reach a common end state typical of strong-scattering disordered materials.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.241
Teacher spread0.213 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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