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Record W2563641545 · doi:10.1103/physrevb.92.064204

Germanene-like defects in amorphous germanium revealed by three-dimensional visualization of high-resolution pair-distribution functions

2015· article· en· W2563641545 on OpenAlexafffund
Bruno Tomberli, Aly Rahemtulla, Edward Kim, S. Roorda, S. Kycia

Bibliographic record

VenuePhysical Review B · 2015
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsUniversité de MontréalUniversity of GuelphCapilano University
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsGermaneneGermaniumVisualizationMaterials scienceHigh resolutionDistribution (mathematics)Resolution (logic)Condensed matter physicsPhysicsComputer scienceData miningOptoelectronicsSiliconMathematicsMathematical analysisArtificial intelligenceSilicene

Abstract

fetched live from OpenAlex

The structural description of even the most basic monatomic amorphous materials is under considerable debate. In this work, an intuitive computational technique has been developed to construct three-dimensional statistical density maps to directly visualize local atomic structure of amorphous germanium (a-Ge), enabling the interpretation of recent state-of-the-art experiments and simulations. The continuous random network (CRN) model is compared to our experimental model refined through a Reverse Monte Carlo routine. In this refinement a-Ge has two dominant structures: a fourfold coordinated tetrahedron and a buckled threefold coordinated local structure similar to silicene and germanene. These structures account for 95.7% of the total atoms in a 5:2 ratio respectively. Our method shows well defined structural ordering in the second shell of a-Ge. This novel visualization tool enables the interpretation of complex disordered materials and reveals the bimodal structures of a-Ge.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.695

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 head0.255
Teacher spread0.238 · 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 designNot applicable
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

Citations5
Published2015
Admission routes2
Has abstractyes

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