Biopolymer Composites Material Extrusion and their Applications: A Review
Bibliographic record
Abstract
Advances in additive manufacturing are leading to the emergence of new printable applications, including sensors for healthcare monitoring and bioengineering scaffolds. Research is driven by designing new printable inks including composites that can be extruded and respond to changes in their surroundings and patterning these materials on the microscale. In modern printing techniques, an emerging modified three‐dimensional (3D) printing method: materials extrusion has been utilized for customizable electronics because of its high compatibility with various inks, low cost, and versatility to different levels of complexity. Material extrusion enables not only the printing of 2D and 3D architecture of the electrode structure but also the bioprinting of structures such as conductive scaffolds. In this review, fundamental insights into rational printable ink formulation including colloidal suspensions, gels, polymer melts, composites, printing criteria, processes, and applications toward printable electronics using composites composed of nanomaterials and biopolymers are fully discussed. New manufacturing insights on how to further improve the resolution and simplify the printing process of responsive materials are discussed, which have not been seen in currently published representative reviews. It is envisioned that this review provides high scientific merits to readers working in wearable devices, biological smart materials, and flexible nanoelectronics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".