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Record W1978323476 · doi:10.1063/1.4890503

An atomic charge model for graphene oxide for exploring its bioadhesive properties in explicit water

2014· article· en· W1978323476 on OpenAlexafffund
Douglas Stauffer, Nadiya Dragneva, Wely B. Floriano, Robert C. Mawhinney, Giovanni Fanchini, Sam French, Oleg Rubel

Bibliographic record

VenueThe Journal of Chemical Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversity of CalgarySouth Health CampusWestern UniversityLakehead UniversityThunder Bay Regional Research Institute
FundersNatural Sciences and Engineering Research Council of CanadaLakehead University
KeywordsGrapheneAdsorptionBiomoleculeOxideMolecular dynamicsAtomic chargeSurface chargeBioadhesiveNanotechnologyMaterials scienceElectrostaticsForce field (fiction)Ab initioBiocompatibilityComputational chemistryChemical physicsDrug deliveryChemistryMoleculePhysicsPhysical chemistryOrganic chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Graphene Oxide (GO) has been shown to exhibit properties that are useful in applications such as biomedical imaging, biological sensors, and drug delivery. The binding properties of biomolecules at the surface of GO can provide insight into the potential biocompatibility of GO. Here we assess the intrinsic affinity of amino acids to GO by simulating their adsorption onto a GO surface. The simulation is done using Amber03 force-field molecular dynamics in explicit water. The emphasis is placed on developing an atomic charge model for GO. The adsorption energies are computed using atomic charges obtained from an ab initio electrostatic potential based method. The charges reported here are suitable for simulating peptide adsorption to GO.

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 score0.161
Threshold uncertainty score0.269

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 head0.050
GPT teacher head0.228
Teacher spread0.179 · 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

Citations53
Published2014
Admission routes2
Has abstractyes

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