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Record W4213221623 · doi:10.3390/polym14040729

Impact of Immersion Media on Physical Properties and Bioactivity of Epoxy Resin-Based and Bioceramic Endodontic Sealers

2022· article· en· W4213221623 on OpenAlexaff
Thais Gomes de Moraes, Alan Silva de Menezes, Renata Grazziotin‐Soares, Rafael Ubaldo Moreira e Moraes, Paulo Vítor Campos Ferreira, Ceci Nunes Carvalho, José Bauer, Edilausson Moreno Carvalho

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsUniversity of Saskatchewan
FundersFundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do MaranhãoConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBioceramicEpoxyImmersion (mathematics)Materials scienceDentistryComposite materialMedicineMathematics

Abstract

fetched live from OpenAlex

This study assessed the effects of immersion media [distilled water (dw), phosphate buffered saline (pbs) and simulated body fluid (sbf)] in the physical properties [fluid uptake/sorption/solubility and alkalinization activity (pH)] and bioactivity of a bioceramic sealer: the BioRoot RCS (BioRoot) (Septodont). The epoxy-resin sealer AH Plus (Dentsply) was used as comparison. Sealers were immersed in dw, pbs and sbf to evaluate the fluid uptake/sorption/solubility and pH's media. Bioactivity was assessed with SEM/EDS, FTIR-ATR and XRD. BioRoot solubility was as follows: sbf > pbs = dw. BioRoot had alkaline pH, and AH Plus had neutral pH, regardless of the medium. BioRoot presented mineral precipitates and peaks indicating hydroxyapatite-precursors in pbs and sbf. AH Plus physical properties were not affected by immersion media and it had no bioactivity. pbs and sbf should be preferred to investigate bioceramic sealers over distilled water, because they were able to highlight the sealer properties. BioRoot maintained the alkaline environment and favored hard tissue deposition.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.422

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 head0.033
GPT teacher head0.278
Teacher spread0.246 · 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 designObservational
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

Citations20
Published2022
Admission routes1
Has abstractyes

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