Using Functionalized Micron-Sized Glass Fibres for the Synergistic Effect of Glass Ionomer on Luting Material
Bibliographic record
Abstract
This laboratory experiment was conducted with the objective of augmenting the mechanical properties of glass ionomer cement (GIC) via altering the composition of GIC luting powder through the introduction of micron-sized silanized glass fibres (GFs). Experimental GICs were prepared through the addition of two concentrations of GFs (0.5% and 1.0% by weight) to the powder of commercially available GIC luting materials. The effect of GF in set GIC was internally evaluated using micro-CT while the mechanical attributes such as nano hardness (nH), elastic modulus (EM), compressive strength (CS), and diametral tensile strength (DTS) were gauged. Additionally, the physical properties such as water solubility and sorption, contact angle (CA), and film thickness were evaluated. Reinforced Ketac Cem Radiopaque (KCR) GIC with 0.5 wt.% GF achieved improved nH, EM, CS, and DTS without affecting the film thickness, CA or internal porosity of the set GIC cement. In contrast, both GF-GIC formulations of Medicem (MC) GIC showed the detrimental effect of the GF incorporation. Reinforcing KCR GIC with 0.5 wt.% silanized GFs could improve the physical and mechanical attributes of luting material. Silanized GF, with optimal concentration within the GIC powder, can be used as a functional additive in KCR GIC with promising results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".