A Novel Approach for the Utilization of Biocellulose Nanofibres in Polyurethane Nanocomposites for Potential Applications in Bone Tissue Implants
Bibliographic record
Abstract
With the increasing number of surgical bone grafts per year, the application of biomaterials in tissue engineering has become a popular issue. In the present work, the potential of biocellulose-nanofibre-reinforced polyurethane nanocomposites to act as bone scaffold implants is established. Investigating properties of polyurethane shows that this widely applied biomaterial group cannot fulfil all properties required for bone implants in a stand-alone fashion. Bone implants require a high Young's modulus and tensile strength but low strain which makes it difficult to find a suitable polyurethane since higher hard segment content will reduce tensile strength and lower hard segment content will reduce the Young's modulus. Other factors such as biodegradation also become important. A literature review on carbon nanotube and nanofibre composites with polyurethanes shows that nanofibrous reinforcement leads to favourable implant properties. Young's modulus and tensile strength increase dramatically. Other properties such as thermal conductivity and viscosity are also affected. These types of nanofibrous materials, however, are the subject of an ongoing debate about toxicity and their use in bone implants is questionable. Biocellulose nanofibres formed from bacteria (also called bacterial cellulose (BC)) possess favourable mechanical properties and are highly biocompatible. A survey on works done on BC nanofibres and their composites show that nanostructured biocomposites that contains the nanofibres reinforced in polymer composites result in changes that are comparable to those of carbon nanotubes in regards to bone scaffold applications. Showing improvement on biocompatibility and mechanical properties, biocellulose nanofibre reinforcement on polyurethanes possesses strong potential for bone implants and other tissue-engineering applications.
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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 | 0.000 | 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".