Bibliographic record
Abstract
With societal, industrial, and clinical progress continuously reaching greater heights, the crucial need for enhanced materials with versatile properties is ever increasing to help this growth. As such, polymer nanocomposites (PNCs) have been heavily researched, becoming redefined as their own class of materials, focusing on fundamental structure/property relationships, manufacturing techniques, and commercial applications, due to their remarkable properties and application versatility. Through countless nanoparticle and polymer matrix variations, PNCs can be finely-tuned to exhibit a multitude of unique characteristics. Utilizing this concept, the research laid out in the combined chapters of this dissertation sought to produce various PNCs embedded with cellulose nanocrystals/nanofibrils (CNCs/CNFs) and magnetic nanoparticles (MNPs) to obtain uniquely tunable properties to further progress the biomaterials field for biomedical applications. Initially, CNCs were extracted from the otherwise useless agricultural waste product of spent coffee grounds through phosphoric acid hydrolysis, and analyzed using multiple physical and chemical characterization techniques. In particular, a few crucial properties determined were aspect ratio of 12 ± 3, crystallinity of 74.2%, surface charge density of 48.4 ± 6.2 mmol/kg cellulose, and the ability to successfully reinforce PNCs, comparing well to other literature data and common commercial CNCs. Following extraction, CNCs/CNFs, as well as MNPs, were incorporated into various polymer matrices, including poly(ethylene glycol) diacrylate, sodium alginate, gelatin, and polyurethane, among others. Through solution casting and 3D bioprinting fabrication methods, as well as composition manipulation, CNCs/CNFs were able to reach ideal percolating networks within the PNCs for maximum mechanical reinforcement with minimal hindrance of the polymer matrix’s natural properties. The various PNC hydrogel scaffolds successfully demonstrated tunability of their nanostructural, mechanical, hydration, and biodegradation properties, utilizing the benefits of manipulated composition, crosslinking density, and nanofiller orientation to increase versatility for tissue engineering constructs. Additionally, MNP incorporation was shown to successfully produce inductive heating responses to promote topographical shape memory effects, while invoking minute thermal dissipation into surrounding environments to reduce thermal shock to seeded biological components. The success of this work makes strides to overcome a few crucial disadvantages of current PNC biomaterial hydrogels, specifically their inability to regenerate biomimetic native tissues during wound healing.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".