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Record W2474656750 · doi:10.1038/srep25393

A Chemical Template for Synthesis of Molecular Sheets of Calcium Carbonate

2016· article· en· W2474656750 on OpenAlexfundno aff
Ina Rianasari, Farah Benyettou, Sudhir Kumar Sharma, Thomas N. Blanton, Serdal Kırmızıaltın, Ramesh Jagannathan

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsnot available
FundersYork UniversityNew York University Abu Dhabi
KeywordsLamellar structureAmorphous calcium carbonateCrystallizationPolymerCalcium carbonateMaterials scienceCalciteGrapheneCarboxylateChemical engineeringAcrylic acidNanotechnologyChemistryOrganic chemistryMineralogyCopolymerComposite material

Abstract

fetched live from OpenAlex

Inspired by the discovery of graphene and its unique properties, we focused our research to develop a scheme to create nacre like lamellar structures of molecular sheets of CaCO3 interleaved with an organic material, namely carbon. We developed a facile, chemical template technique, using a formulation of poly(acrylic) acid (PAA) and calcium acetate to create lamellar stacks of single crystal sheets of CaCO3, with a nominal thickness of 17 Å, the same as a unit-cell dimension for calcite (c-axis = 17.062 Å), interleaved with amorphous carbon with a nominal thickness of 8 Å. The strong binding affinity between carboxylate anions and calcium cations in the formulation was used as a molecular template to guide CaCO3 crystallization. Computational modeling of the FTIR spectra showed good agreement with experimental data and confirmed that calcium ions are bridged between polymer chains, resulting in a net-like polymer structure. The process readily lends itself to explore the feasibility of creating molecular sheets of other important inorganic materials and potentially find applications in many fields such as super capacitors and "low k di-electric" systems.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.252
Teacher spread0.235 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

Explore more

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