Activated Carbons for Bone Cell Growth: Structural Properties and Biological Interactions
Bibliographic record
Abstract
Having high porosity and biocompatibility, carbon-based materials are promising candidates for tissue engineering applications, particularly as substitutes for biological tissues. This study investigates the growth and viability of osteoblasts on four different activated carbon (AC) materials and correlates biological responses with their physicochemical and morphological properties. Two materials derived from non-renewable sources—AC1, a laboratory-synthesized carbon derived from anthracite, and AC3, a commercial activated carbon (Norit GCN 830) derived from coal—and two commercial activated carbons derived from renewable sources—peat, AC2 (Norit PK1-3), and wood, AC4 (ROX 0.8)—are studied. Results showed that AC1 exhibited the highest porosity (3072 m2/g), with higher phenolic and oxygen-containing surface groups but lower cell viability. In contrast, AC2, AC3, and AC4 displayed lower porosity compared to AC1 (755, 1040, and 1083 m2/g, respectively) and fewer surface phenolic groups but sustained osteoblast proliferation. Notably, AC4 demonstrated superior performance, characterized by regions of fibrous surface, pores in the meso- and microscale range (<50 nm), and enhanced cell viability and proliferation. AC2 also showed favorable results, ranking second for cell growth support. These findings suggest that biomass-derived ACs, particularly AC4 and AC2, provide favorable environments for osteoblast viability and proliferation. AC costs were estimated at 15 to 38 times lower than those for hydroxyapatite and bioceramics, which are widely used for bone cell growth. Thus, ACs made from renewable sources are promising candidates for tissue engineering applications, offering sustainable and effective alternatives for biomedical use.
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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".