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Record W2790045336 · doi:10.1149/08512.0031ecst

From Salt to Germanene: A Cookbook for Electrochemical Formation of 2D Materials (Inspired by R. Adžić)

2018· article· en· W2790045336 on OpenAlexaff
Jakub Drnec, John L. Stickney, David A. Harrington

Bibliographic record

VenueECS Transactions · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGermaneneMonolayerIonic bondingMaterials scienceHoneycomb structureBilayerNanotechnologyChemical vapor depositionChemical physicsIonHoneycombMonatomic gasPolarity (international relations)ElectrochemistrySiliceneChemistryElectrodeComposite materialGraphenePhysical chemistry

Abstract

fetched live from OpenAlex

2D materials with honeycomb lattices are increasingly studied due to their unique electronic and mechanical properties. The typical preparation techniques, such as chemical vapor deposition and cleavage of bulk crystals, are either complicated or limited to only certain classes of materials. Here we discuss basic ordering principles in 2D monoatomic layers and show that electrochemical deposition can also result in 2D films with desired honeycomb structures. The principles are first studied on model ionic 2D layers prepared in ultrahigh vacuum. Two ordering possibilities are identified: a bilayer structure for covalently bonded films and a mixed monolayer structure for ionically bonded films. Typically, the most energetically favorable configuration in mixed layers is when ions with one polarity are closely surrounded by ions of the opposite polarity. This translates into a layer with honeycomb structure on substrates with p6/p3 symmetry. This phenomenon can be used to prepare germanene, a layer with such a structure.

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 categoriesInsufficient payload (model declined to judge)
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 score0.423
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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