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Record W1967406851 · doi:10.1103/physrevb.82.134415

Influence of adsorbates on the electronic and magnetic properties of graphane with H-vacancy defects

2010· article· en· W1967406851 on OpenAlexafffund
Julia Berashevich, Tapash Chakraborty

Bibliographic record

VenuePhysical Review B · 2010
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Manitoba
FundersCanada Research Chairs
KeywordsGraphaneVacancy defectMaterials scienceCondensed matter physicsChemical physicsNanotechnologyGraphenePhysics

Abstract

fetched live from OpenAlex

The influence of adsorbates on the electronic and magnetic properties of graphane possessing the H-vacancy defects was investigated using the quantum-chemistry methods. We found that the H vacancies in graphane significantly improve its chemical reactivity to environment due to the unpaired electron left on the vacancy. Therefore, depending on the type of molecule adsorbed, the interaction between the adsorbate and the vacancy can occur with or without the formation of the bond. In case of the bond formation the unpaired electron supplied by the vacancy is removed and the electronic and magnetic properties of graphane become similar to that of pure defect-free graphane. Moreover, adsorption leads to degradation or vanishing of magnetism induced by the H vacancies in graphane, except in the case when the ${\text{CO}}_{2}$ molecule is attached, which is found to generate localized state with the unpaired electron after bonding with the H vacancy. Stability of ferromagnetism induced by the ${\text{CO}}_{2}$ molecules bound to the vacancies is found to be low. There are two reasons for low stability: localization of the spin density of the localized state entirely on adsorbate that minimize the interference of the spin tails of these states and the possibility of pairing of two neighboring ${\text{CO}}_{2}$ molecules to ${\text{C}}_{2}{\text{O}}_{4}$.

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 score0.039
Threshold uncertainty score0.119

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 head0.009
GPT teacher head0.258
Teacher spread0.248 · 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

Citations22
Published2010
Admission routes2
Has abstractyes

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