MétaCan
Menu
Back to cohort
Record W3196690898 · doi:10.3390/ceramics4030034

The Effect of Different Surface Treatments on the Micromorphology and the Roughness of Four Dental CAD/CAM Lithium Silicate-Based Glass-Ceramics

2021· article· en· W3196690898 on OpenAlex
Muna Bebsh, Asmaa Haimeur, Rodrigo França

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCeramics · 2021
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHydrofluoric acidSurface roughnessMaterials sciencePolishingCeramicSurface finishComposite materialScanning electron microscopeMetallurgy

Abstract

fetched live from OpenAlex

Objective: This study aimed to investigate and compare the effect of various surface treatments on the micromorphology and the roughness of four CAD/CAM lithium silicate-based glass-ceramics (LSGC). Method: Eighty specimens of four LDGC materials (IPS e. max® CAD (Ivoclar-Vivadent, Liechtenstein, Schaan), Vita Suprinity® (Vita Zahnfabrik, Bad Säckingen, Germany), Celtra Duo® (Dentsply, Hanau-Wolfgang, Germany) and n!ce (Straumann, Basel, Switzerland)) were used for this study. All specimens were highly polished with 400, 600, 1200 grit silicon carbide paper and then polished with 3 µm and 1 µm polycrystalline diamond suspension liquid with grinding devices. Each group of ceramic was assigned to one of the following three surface treatments (1) sand-blasting (SB) with 50 µm Al2O3 at 70 psi for 10s, (2) hydrofluoric acid etching (HF) with 5% hydrofluoric acid, according to the manufacturer instructions, (3) and a combination of sand-blasting and hydrofluoric acid (SB + HF). All specimens were cleaned with ethanol for 2 min and placed in an ultrasonic unit with distilled water for 15 min. The microstructure was analyzed by scanning electron microscopy (SEM). The surface roughness and topography were evaluated with atomic force microscopy in tapping mode (AFM). Statistical analysis was done using two-way ANOVA and Tukey tests (α = 5%). Results: All surface treatments had a significant effect on LDGC surface roughness compared to the untreated surface (p < 0.05). The sand-blasting treatment had a significantly higher mean surface roughness value for Vita Suprinity and Celtra Duo compared to other surface treatments (p < 0.05). However, there was no significant difference for surface roughness between sand-blasting and sand-blasting + etching for e.max CAD and n!ce. The hydrofluoric acid produced less surface roughness compared to other surface treatments but was able to change the surface structure. (5) Conclusions: The sand-blasting + etching treatment could be a sufficient method to produce surface roughness for all LSGC types.

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.

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 score0.100
Threshold uncertainty score0.345

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 studies0.0000.001
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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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