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Record W4289520768 · doi:10.1155/2022/4690656

Evaluation of the Mechanical Properties of Three Resin‐Modified Glass‐Ionomer Materials

2022· article· en· W4289520768 on OpenAlexaff
Heleine Maria Chagas Rêgo, Sheila Butler, Maria Jacinta Moraes Coelho Santos

Bibliographic record

VenueBioMed Research International · 2022
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsWestern University
Fundersnot available
KeywordsDistilled waterGlass ionomer cementMaterials scienceUltimate tensile strengthComposite materialFlexural strengthSignificant differenceAcrylic resinMaterials testingChemistryChromatography

Abstract

fetched live from OpenAlex

This study is aimed at evaluating the flexural strength (FS), fracture toughness (FT), and diametral tensile strength (DTS) of three resin‐modified glass‐ionomer cements (RMGICs): Ketac Nano, Riva Light Cure, and Fuji II LC. One hundred twenty specimens were prepared from the RMGIC materials ( n = 10). The cements were mixed and inserted into different mould sizes according to the test performed: FS: rectangular Teflon mould (32 mm × 3.15 mm × 2 mm); FT: notchless triangular prism (NTP) Teflon mould (6 mm × 6 mm × 6 mm × 12 mm); and DTS: ring road stainless steel mould (6 mm × 3 mm). Specimens were light cured for 20 seconds on each surface and stored in distilled water at 37 ° C ± 2 ° C for seven days prior to tests. To evaluate the influence of storage in the mechanical properties of the RMGIs, specimens tested for DTS were stored in distilled water at 37 ° C ± 2 ° C for 32 days prior to test. Data were analyzed by ANOVA and Tukey’s test ( α = 0.05). Fuji II LC presented significantly higher values for all tests employed when compared to Ketac Nano and Riva LC RMGIs. There was no significant difference on DTS before and after the 32‐day storage for each material. Fuji II LC presented superior mechanical properties when compared to Ketac Nano, and Riva LC storage showed no influence on the mechanical properties of the RMGI materials tested.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.029
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.278
GPT teacher head0.410
Teacher spread0.132 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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