Fabrication and Properties of the Montmorillonite/Nanobioglass Hybrid Reinforcement from Agroindustrial Waste for Bone Regeneration
Bibliographic record
Abstract
Nowadays, bone systems have a series of consequences that compromise the quality of life mainly due to wear and decreased bioactivity, generally in elderly people and children. In this context, the combination of montmorillonite (MMT-NPs) in a vitreous system such as nanobioglass facilitates the adsorption of biomolecules on the surface and within the interlamellar spaces, enabling the entry of ions by a cation exchange process focusing on increasing the rate of bone formation. This work aims to synthesize and characterize an eco-friendly hybrid reinforcement containing MMT-NPs with nanobioglass doped with magnesium nanoparticles (MgNPs-BV). In this way, MMT-NPs@MgNPs-BV was synthesized by the impregnation method, where an experimental design was used to verify the synthesis conditions. The ideal condition by experimental design was carried out in terms of the characterization and biological activity, where we demonstrated MMT-NPs of 30% w w –1, MgNPs-BV of 6% w w –1, and a calcination temperature of 1273.15 K with a cell viability around 66.87%, an average crystallite diameter of 12.5 nm, and a contact angle of 17.7°. The characterizations confirmed the impregnation method with an average particle size of 51.4 ± 13.1 nm. The mechanical tests showed a hardness of 2.6 GPa with an apparent porosity of 22.2%, similar to human bone. MMT-NPs@MgNPs-BV showed a cell proliferation of around 96% in osteoblastic cells (OFCOL II), with the formation of the apatite phase containing a relation of Ca/P of around 1.63, a biodegradability of 82%, and rapid release of ions with a Ca/P ratio of 1.42. Therefore, the eco-friendly hybrid reinforcement with MMT-NPs and MgNPs-BV shows potential for application with a matrix for biocompatible nanocomposites for bone regeneration.
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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".