Electroconductive Biohybrid Collagen/Pristine Graphene Composite Biomaterials with Enhanced Biological Activity
Bibliographic record
Abstract
Electroconductive substrates are emerging as promising functional materials for biomedical applications. Here, the development of biohybrids of collagen and pristine graphene that effectively harness both the biofunctionality of the protein component and the increased stiffness and enhanced electrical conductivity (matching native cardiac tissue) obtainable with pristine graphene is reported. As well as improving substrate physical properties, the addition of pristine graphene also enhances human cardiac fibroblast growth while simultaneously inhibiting bacterial attachment (Staphylococcus aureus). When embryonic-stem-cell-derived cardiomyocytes (ESC-CMs) are cultured on the substrates, biohybrids containing 32 wt% graphene significantly increase metabolic activity and cross-striated sarcomeric structures, indicative of the improved substrate suitability. By then applying electrical stimulation to these conductive biohybrid substrates, an enhancement of the alignment and maturation of the ESC-CMs is achieved. While this in vitro work has clearly shown the potential of these materials to be translated for cardiac applications, it is proposed that these graphene-based biohybrid platforms have potential for a myriad of other applications-particularly in electrically sensitive tissues, such as neural and neural and musculoskeletal tissues.
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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".