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Record W2613700340 · doi:10.1080/23337931.2017.1326009

Water sorption and solubility of polyamide denture base materials

2017· article· en· W2613700340 on OpenAlexfundno aff
Long G. Nguyen, Hilde Molvig Kopperud, Marit Øilo

Bibliographic record

VenueActa Biomaterialia Odontologica Scandinavica · 2017
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsnot available
FundersFaculty of Medicine and Dentistry, University of Alberta
KeywordsSorptionSolubilityPolyamideMaterials scienceChemistryNuclear chemistryPolymer chemistryOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Purpose: Some patients experience adverse reactions to poly(methyl methacrylate)-based (PMMA) dentures. Polyamide (PA) as an alternative to PMMA has, however, not been well documented with regard to water sorption and water solubility. The aim of this in vitro study was to measure water sorption and water solubility of two PA materials compared with PMMA, and to evaluate the major components released from the PA materials and the effect on hardness of the materials.Methods: Ten discs (40.0 mm diameter, 2.0 mm thick) of each material (PA: Valplast and Breflex; PMMA: SR Ivocap HIP) were prepared according to manufacturers’ recommendations. The specimens were tested for water sorption and water solubility, according to a modification of ISO 20795-1:2008. Released substances were analysed by gas chromatography/mass spectrometry (GC/MS).Results: There were statistically significant differences among the materials regarding water sorption, water solubility and time to water saturation. Breflex had the highest water sorption (30.4 μg/mm3), followed by PMMA-material (25.8 μg/mm3) and Valplast (13.6 μg/mm3). Both PA materials had statistically significant lower water solubility than the PMMA. Both PA had a net increase in weight. Analysis by GC/MS identified release of the compound 12-aminododecanolactam from the material Valplast. No release was found from the Breflex material.Conclusions: The PA denture materials show differences in water sorption and solubility, but within the limits of the standard requirements. The PA showed a net increase in weight after long-term water sorption. The clinical implications of the findings are not elucidated.

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 categoriesInsufficient payload (model declined to judge)
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 score0.296
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.292
Teacher spread0.262 · 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.

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

Citations34
Published2017
Admission routes1
Has abstractyes

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