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Record W2989634366 · doi:10.1021/acsomega.9b02643

Facile Synthesis of Calcium Hydroxide Nanoparticles onto TEMPO-Oxidized Cellulose Nanofibers for Heritage Conservation

2019· article· en· W2989634366 on OpenAlexafffund
Mounir El Bakkari, Vivek Bindiganavile, Yaman Boluk

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Alberta
FundersCanada Foundation for Innovation
KeywordsCalcium hydroxideNanofiberCelluloseChemical engineeringNanoparticleCarbonizationInorganic chemistryPrecipitationMaterials scienceChemistryAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

Calcium hydroxide is used in diverse applications including heritage conservation where supplying it in the form of nanoparticles allows easy carbonation with atmospheric air contacts. The effects of cellulose nanofibers on the precipitation of calcium hydroxide nanoparticles were investigated by varying the reaction time, concentration, and carboxylation content of cellulose nanofibers. Cellulose nanofibers were very effective in producing calcium hydroxide nanoparticles with less than 50 nm sizes out of calcium nitrate-sodium hydroxide precipitation reactions. The formation of smaller-size calcium hydroxide nanoparticles is believed to be the result of heterogeneous nucleation and growth of calcium hydroxide particles on cellulose nanofibers. The liquid-phase nucleated and grown calcium hydroxide nanoparticles were also deposited onto cellulose nanofibers. The resulting calcium hydroxide nanoparticles were carbonized and generated calcite under atmospheric carbon dioxide in an efficient way.

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 score0.006
Threshold uncertainty score0.729

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 head0.028
GPT teacher head0.283
Teacher spread0.255 · 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

Citations25
Published2019
Admission routes2
Has abstractyes

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