Activated Carbons for Bone Cell Growth: Structural Properties and Biological Interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it