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Record W3139239168 · doi:10.3390/polym13071009

Characterization of an Experimental Two-Step Self-Etch Adhesive’s Bonding Performance and Resin-Dentin Interfacial Properties

2021· article· en· W3139239168 on OpenAlexaff
Abu Faem Mohammad Almas Chowdhury, Arefin Alam, Mônica Yamauti, Pedro Álvarez Lloret, Pipop Saikaew, Ricardo M. Carvalho, Hidehiko Sano

Bibliographic record

VenuePolymers · 2021
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of British Columbia
FundersMinistry of Education, Culture, Sports, Science and Technology
KeywordsAdhesiveMaterials scienceDentinMolarComposite materialBond strengthDental bondingLayer (electronics)Dentistry

Abstract

fetched live from OpenAlex

This study evaluated an experimental two-step self-etch adhesive (BZF-29, BZF) by comparing it with a reference two-step self-etch adhesive (Clearfil Megabond 2, MB) and a universal adhesive (G-Premio Bond, GP) for microtensile bond strength (μTBS) and resin-dentin interfacial characteristics. Twenty-four human third molars were used for the μTBS test. Bonded peripheral dentin slices were separated to observe the resin-dentin interface and measure the adhesive layer thickness with SEM. μTBS data of the central beams were obtained after 24 h and 6 months of water storage. Fracture modes were determined using a stereomicroscope and SEM. Nine additional third molars were used to determine the elastic modulus (E) employing an ultra microhardness tester. Water storage did not affect μTBS of the tested adhesives (p > 0.05). μTBS of BZF and MB were similar but significantly higher than GP (p < 0.05). BZF achieved the highest adhesive layer thickness, while GP the lowest. E of BZF and MB were comparable but significantly lower than GP (p < 0.05). Except for GP, the predominant fracture mode was nonadhesive. The superior bonding performance of BZF and MB could be attributed to their better mechanical property and increased adhesive thickness imparting better stress relief at the interface.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.393

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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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