Highly Concentrated Nitrogen‐Doped Carbon Nanotubes in Alginate–Gelatin 3D Hydrogels Enable in Vitro Breast Cancer Spheroid Formation
Bibliographic record
Abstract
Carbon nanotubes’ (CNTs) physicochemical and mechanical properties make them ideal reinforcement materials for hydrogels, but distributing CNTs homogeneously in hydrogels remains a challenge. Chemical modifications to CNTs are used to facilitate nanomaterial dispersion, thus improving hydrogels’ physicochemical properties. Among CNTs, nitrogen‐doped CNTs (CNx) possess both great dispersibility in solution and biocompatibility properties. By formulating a method to incorporate CNx within alginate (i.e., covalently grafting alginate to the CNx surface versus noncovalently adsorbing alginate to the CNx surface) creates extrudable materials with tunable physical, chemical, and thermal properties. Herein, three new composites of alginate‐CNx are created. The results indicate that all composites present different physicochemical and thermal properties, suggesting that alginate is reorganized according to their degree of oxidation. These composites show cytocompatibility with MDA‐MB‐231 and regulation over the size of spheroids formed within the matrix. CNx within the matrix negatively affects MCF‐7 cells viability, spheroid formation rate, and the quantity of spheroids developed during culture. These materials provide a useful 3D hydrogel that can be used to develop in vitro models to understand the role of microenvironmental factors such as stiffness or surface roughness on the development of spheroids and their subsequent phenotypic behavior.
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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".