Sol-Gel Derived Gelatin–Bioactive Glass Nanocomposite Biomaterials Incorporating Calcium Chloride and Calcium Ethoxide
Bibliographic record
Abstract
Calcium-containing organic–inorganic nanocomposites play an essential role in developing bioactive bone biomaterials. Ideally, bone substitute materials should mimic the organic–inorganic composition of bone. In this study, the roles of calcium chloride (CaCl2) and calcium ethoxide (Ca(OEt)2) were evaluated for the development of sol-gel-derived organic–inorganic biomaterials composed of gelatin, bioactive glass (BG) and multiwall carbon nanotubes (MWCNTs) to create nanocomposites that mimic the elemental composition of bone. Nanocomposites composed of either CaCl2 or Ca(OEt)2 were chemically different but presented uniform elemental distribution. The role of calcium sources in the matrix of the nanocomposites played a major role in the swelling and degradation properties of biomaterials as a function of time, as well as the resulting porous properties of the nanocomposites. Regardless of the calcium source type, biomineralization in simulated body fluid and favorable cell attachment were promoted on the nanocomposites. 10T1/2 cell viability studies using standard media (DMEM with 5% FBS) and conditioned media showed that Ca(OEt)2-based nanocomposites seemed more favorable biomaterials. Collectively, our study demonstrated that CaCl2 and Ca(OEt)2 could be used to prepare sol-gel-derived gelatin–BG–MWCNT nanocomposites, which have the potential to function as bone 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".