Silicate-Based Electro-Conductive Inks for Printing Soft Electronics and Tissue Engineering
Bibliographic record
Abstract
Hydrogel-based bio-inks have been extensively used for developing three-dimensional (3D) printed biomaterials for biomedical applications. However, poor mechanical performance and the inability to conduct electricity limit their application as wearable sensors. In this work, we formulate a novel, 3D printable electro-conductive hydrogel consisting of silicate nanosheets (Laponite), graphene oxide, and alginate. The result generated a stretchable, soft, but durable electro-conductive material suitable for utilization as a novel electro-conductive bio-ink for the extrusion printing of different biomedical platforms, including flexible electronics, tissue engineering, and drug delivery. A series of tensile tests were performed on the material, indicating excellent stability under significant stretching and bending without any conductive or mechanical failures. Rheological characterization revealed that the addition of Laponite enhanced the hydrogel's mechanical properties, including stiffness, shear-thinning, and stretchability. We also illustrate the reproducibility and flexibility of our fabrication process by extrusion printing various patterns with different fiber diameters. Developing an electro-conductive bio-ink with favorable mechanical and electrical properties offers a new platform for advanced tissue engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".