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Record W4376621819 · doi:10.4103/jcd.jcd_41_23

Influence of nonthermal argon plasma on the micro-shear bond strength between resin cement and translucent zirconia

2023· article· en· W4376621819 on OpenAlexaff
Safoura Ghodsi, Kimia Salimi, Faezeh Atri, Sara Valizadeh, Majid Sahebi, Neshatafarin Manouchehri

Bibliographic record

VenueJournal of Conservative Dentistry · 2023
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCubic zirconiaMaterials scienceCementBond strengthComposite materialStereo microscopeUniversal testing machineAdhesiveUltimate tensile strengthLayer (electronics)Ceramic

Abstract

fetched live from OpenAlex

Background: Considering the potential of translucent zirconia for application in esthetic restorations, it is necessary to find effective methods with the least adverse effects to increase its bond strength to resin cement. Aims: This study aimed to test if different conservative surface treatments and cement types could affect the micro-shear bond strength (μSBS), failure mode, and bonding interface between resin cement and translucent zirconia. Materials and Methods: < 0.05), and the mode of failure was determined by a stereomicroscope (×10). The cement-zirconia interface and the surface hydrophilicity (contact angle) were also evaluated. Statistical Analysis: < 0.05). Failure mode, contact angle, and cement-zirconia interface were analyzed descriptively. Results: = 0.075) groups. All plasma specimens in the incubator failed prematurely. The mode of failure in all specimens was adhesive. The lowest and highest contact angles were seen in Pr + plasma and the control groups, respectively. Conclusion: The use of Pr could successfully improve the bond strength of resin cement to translucent zirconia while plasma was not an acceptable and durable substitute.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.292
Teacher spread0.258 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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