Cellulose Nanofibre Mesh for Use in Dental Materials
Bibliographic record
Abstract
The aim of this study was to produce a 3D mesh of defect free electrospun cellulose acetate nanofibres and to use this to produce a prototype composite resin containing nanofibre fillers. This might find use as an aesthetic orthodontic bracket material or composite veneer for restorative dentistry. In this laboratory based study cellulose acetate was dissolved in an acetone and dimethylacetamide solvent solution and electrospun. The spinning parameters were optimised and lithium chloride added to the solution to produce a self supporting nanofibre mesh. This mesh was then silane coated and infiltrated with either epoxy resin or an unfilled Bis-GMA resin. The flexural strength of the produced samples was measured and compared to that of unfilled resin samples. Using this method cellulose acetate nanofibres were successfully electrospun in the 286 nm range. However, resin infiltration of this mesh resulted in samples with a flexural strength less than that of the unfilled control samples. Air inclusion during preparation and incomplete wetting of the nanofibre mesh was thought to cause this reduction in flexural strength. Further work is required to reduce the air inclusions before the true effect of resin reinforcement with a 3D mesh of cellulose acetate nanofibres can be determined.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".