Layer‐by‐Layer Assembly of 3D Tissue Constructs with Functionalized Graphene
Bibliographic record
Abstract
Carbon-based nanomaterials have been considered as promising candidates to mimic certain structure and function of native extracellular matrix materials for tissue engineering. Significant progress has been made in fabricating carbon nanoparticle-incorporated cell culture substrates, but limited studies have been reported on the development of three-dimensional (3D) tissue constructs using these nanomaterials. Here, we present a novel approach to engineer 3D multi-layered constructs using layer-by-layer (LbL) assembly of cells separated with self-assembled graphene oxide (GO)-based thin films. The GO-based structures are shown to serve as cell adhesive sheets that effectively facilitate the formation of multi-layer cell constructs with interlayer connectivity. By controlling the amount of GO deposited in forming the thin films, the thickness of the multi-layer tissue constructs could be tuned with high cell viability. Specifically, this approach could be useful for creating dense and tightly connected cardiac tissues through the co-culture of cardiomyocytes and other cell types. In this work, we demonstrated the fabrication of stand-alone multi-layer cardiac tissues with strong spontaneous beating behavior and programmable pumping properties. Therefore, this LbL-based cell construct fabrication approach, utilizing GO thin films formed directly on cell surfaces, has great potential in engineering 3D tissue structures with improved organization, electrophysiological function, and mechanical integrity.
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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) | 0.001 | 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".