In Vitro Oxidative Degradation of Hydroxyapatite Biopolymer Nanocomposites and the Resulting Consequences on Their Mechanical Performance
Bibliographic record
Abstract
Development of synthetic biomaterials for skeletal reconstruction has progressed rapidly, driven partly by demand to reduce dependency on allografts. One class of materials, biopolymer nanocomposites, has shown promise when combined with additive manufacturing for these applications. The driving goal for the development of 3D-printable biopolymer nanocomposites composed of methacrylated monomers (triglycerides and triethylene glycol) and hydroxyapatite (HA) is to produce structurally robust and degradable customizable grafts. These materials must be able to withstand the loading conditions found in vivo while allowing for degradation and remodeling processes. This study focused on the degradation potential of previously developed HA-containing biopolymer nanocomposites and the resulting consequences of degradation on their mechanical performance. One of the means to study a material's in vivo degradation performance is to assess its susceptibility to oxidative degradation, as oxidation is naturally occurring in cell metabolism, inflammatory responses, and osteoclast resorption. Two in vitro models of oxidative degradation were trialed: aqueous solutions of either hydrogen peroxide or neutral hypochlorous acid. Hypochlorous acid was shown to be a useful in vitro assessment for the degradation potential of biomaterials to different reactive oxygen species. The biopolymer nanocomposites were clearly susceptible to oxidative degradation, demonstrating significant changes in mass and surface morphology. Mechanical performance was reduced under these testing conditions. This was attributed to three main factors: swelling and water absorption effects, chemical modifications, and loss of structure. Overall, this study provides insights into the effects of oxidative degradation on biomaterial functionality and highlights the importance of exploring relevant physiological effects on mechanical properties when developing biomaterials.
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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 | 0.001 | 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".