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Record W2983138072 · doi:10.1063/1.5121841

Intensification of shock damage through heterogeneous phase transition and dislocation loop formation due to presence of pre-existing line defects in single crystal Cu

2019· article· en· W2983138072 on OpenAlexaff
K. Vijay Reddy, Chuang Deng, Snehanshu Pal

Bibliographic record

VenueJournal of Applied Physics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceDislocationShock (circulatory)Condensed matter physicsCrystal (programming language)Enhanced Data Rates for GSM EvolutionDeformation (meteorology)Phase transitionCritical resolved shear stressPhase (matter)Partial dislocationsSingle crystalShock waveStress (linguistics)CrystallographyShear (geology)Composite materialMechanicsChemistryPhysics

Abstract

fetched live from OpenAlex

In general, shock wave deformation studies of perfect single crystals may cause disagreement with the experimental findings as the complete elimination of all defects in the metallic system is not possible in reality. Here, we have studied the influence of edge and screw dislocations on the intensification of damage produced during the propagation of shock at various velocities. Various analyses have been performed such as common neighbor analysis, atomic strain analysis, stress analysis, and kinetic energy mapping to investigate the underlying plastic deformation mechanisms. Results have revealed that the presence of edge dislocations has caused intensified damage through localized amorphization and phase transition. In comparison with the perfect crystal, the presence of pre-existing edge dislocations has incurred an additional damage of ∼17% to the specimen region. On the other hand, the presence of screw dislocations in the specimen causes damage through shear bands and dislocation loop formation, which is found to constitute greater than 80% of the specimen region.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.252

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 head0.028
GPT teacher head0.268
Teacher spread0.240 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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