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Record W2315000783 · doi:10.1098/rsif.2015.0986

Hydroxyapatite formation on graphene oxide modified with amino acids: arginine versus glutamic acid

2016· article· en· W2315000783 on OpenAlexafffund
Maryam Tavafoghi, Nicolas Brodusch, Robert Gauvin, Marta Cerruti

Bibliographic record

VenueJournal of The Royal Society Interface · 2016
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsChemistryMineralization (soil science)Amino acidSupersaturationGlutamic acidArginineNucleationOxideLysineBiomineralizationPrecipitationIonBiophysicsCrystallographyBiochemistryChemical engineeringNitrogenOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Hydroxyapatite (HA, Ca5(PO4)3OH) is the main inorganic component of hard tissues, such as bone and dentine. HA nucleation involves a set of negatively charged phosphorylated proteins known as non-collagenous proteins (NCPs). These proteins attract Ca(2+) and PO4(3-) ions and increase the local supersaturation to a level required for HA precipitation. Polar and charged amino acids (AAs) are highly expressed in NCPs, and seem to be responsible for the mineralizing effect of NCPs; however, the individual effect of these AAs on HA mineralization is still unclear. In this work, we investigate the effect of a negatively charged (Glu) and positively charged (Arg) AA bound to carboxylated graphene oxide (CGO) on HA mineralization in simulated body fluids (SBF). Our results show that Arg induces HA precipitation faster and in larger amounts than Glu. We attribute this to the higher stability of the complexes formed between Arg and Ca(2+) and PO4(3-) ions, and also to the fact that Arg exposes both carboxyl and amino groups on the surface. These can electrostatically attract both Ca(2+) and PO4(3-) ions, thus increasing local supersaturation more than Glu, which exposes carboxyl groups only.

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 score0.218
Threshold uncertainty score0.312

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 head0.207
Teacher spread0.197 · 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

Citations55
Published2016
Admission routes2
Has abstractyes

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