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Venture into Water’s No Man’s Land: Structural Transformations of Solid <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="bold">H</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="bold">2</mml:mn></mml:mrow></mml:msub><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math> under Rapid Compression and Decompression

2018· article· lv· W2903374002 on OpenAlexaff
Chuanlong Lin, Jesse S. Smith, Xuqiang Liu, John S. Tse, Wenge Yang

Bibliographic record

VenuePhysical Review Letters · 2018
Typearticle
Languagelv
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsUniversity of Saskatchewan
FundersScience Challenge ProjectNational Natural Science Foundation of ChinaU.S. Department of EnergyNational Science Foundation
KeywordsCrystallizationAmorphous solidMaterials sciencePhase transitionGlass transitionAmorphous icePhase (matter)CrystallographyCompression (physics)ThermodynamicsPhysicsChemistryPolymer

Abstract

fetched live from OpenAlex

Pressure-induced formation of amorphous ices and the low-density amorphous (LDA) to high-density amorphous (HDA) transition have been believed to occur kinetically below a crossover temperature (T_{c}) above which thermodynamically driven crystalline-crystalline (e.g., ice I_{h}-to-II) transitions and crystallization of HDA and LDA are dominant. Here we show compression-rate-dependent formation of a high-density noncrystalline (HDN) phase transformed from ice I_{c} above T_{c}, bypassing crystalline-crystalline transitions under rapid compression. Rapid decompression above T_{c} transforms HDN to a low-density noncrystalline (LDN) phase which crystallizes spontaneously into ice I_{c}, whereas slow decompression of HDN leads to direct crystallization. The results indicate the formation of HDA and the HDN-to-LDN transition above T_{c} are results of competition between (de)compression rate, energy barrier, and temperature. The crossover temperature is shown to have an exponential relationship with the threshold compression rate. The present results provide important insight into the dynamic property of the phase transitions in addition to the static study.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5440.002

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

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

Citations24
Published2018
Admission routes1
Has abstractyes

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