Effect of Short Fiber Fillers on the Optical Properties of Composite Resins
Bibliographic record
Abstract
Objectives: The aim was to evaluate the effect of different fractions of fiber fillers on the translucency and color change of short fiber composite with various thicknesses.Methods: Fiber composite resin was prepared by mixing resin matrix with various weight fractions of short (3 mm in length) E-glass fiber fillers (0, 11.7, 21.0, 28.5, 34.7 wt%) and then silane treated particulate silica fillers were gradually added by using high speed mixing machine. Particulate filler composite resin without fibers was used as control. Composite resins disks of 10 mm in diameter and with various thicknesses (1.0, 2.0, 3.0, 4.0, and 5.0 mm) of each group were prepared (n=3). Translucency parameter (TP) and color change (?E) were calculated over a white and black background using spectrophotometer to determine the CIELAB values of each specimen. Data were statistically analyzed with analysis of variance (ANOVA).Results: ANOVA revealed that fraction of fiber fillers had a significant effect (P<0.05) on the translucency and color change values of the short fiber composite resin. Translucency values at various thicknesses of short fiber composite was significantly lower than particulate filler composite with same total fillers weight fractions.Significance: Inclusion of short glass fiber fillers reduced the translucency values of the composite resins. Thus, the masking ability of short fiber composite resin at various thicknesses was better than particulate filler composite. Color change was also altered with an increase of fractions of fiber fillers.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".