Direct Ink Writing of Conductive Hydrogels
Bibliographic record
Abstract
Abstract Direct ink writing (DIW) is an additive manufacturing technique that has garnered notable interest due to its precise and consistent printing of a wide range of materials, such as viscoelastic hydrogels, pastes, and complex composites, by adjusting the ink's rheology. This material flexibility combined with the ability to print at room temperature makes DIW ideal for diverse applications and is scalable from small to industrial levels. In recent years, DIW of conductive hydrogels has gained significant attention across various fields, ranging from biomedical scaffolds to flexible electronics. Conductive hydrogels are a category of hydrogels which exhibit conductivity in their wet and/or dry state. Precursors like conductive polymers, metallic nanoparticles, and carbon‐based materials can be used to induce electronic and/or ionic conductivity in hydrogels. This review presents a comprehensive overview of conductive hydrogels demonstrating printability using the DIW technique. The fundamentals of DIW and conductive precursors are presented. Following, the different pathways for reaching optimal conductive hydrogel properties, including mechanical, conductive, and rheological, with a focus on ink synthesis and printability are introduced. Finally, emerging applications of DIW of conductive hydrogels in flexible electronics and medicine are highlighted, and the anticipated challenges for advancement of printable conductive hydrogels using DIW are discussed.
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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) | 0.003 | 0.001 |
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".