Conductive supramolecular acrylate hydrogels enabled by quaternized chitosan ionic crosslinking for high-fidelity 3D printing
Bibliographic record
Abstract
While 3D printing has enabled the fabrication of hydrogels with complex structures, high fidelity techniques (vat polymerization) that enable precisely engineered design of hydrogels require stiff structures to withstand the forces of printing. This is a pressing research gap in hydrogel vat-polymerization 3D printing. To address this limitation, a novel ionic crosslinker consisting of quaternized chitosan complexed with 3-sulfopropyl acrylate was used to form supramolecular 2-hydroxyethyl acrylate organogel precursors. The Cyrene organogel enhanced mechanical properties enabling the printing of high-fidelity structures; the final compliant hydrogels were then obtained through solvent exchange with water. This yielded high-fidelity 3D-printed conductive supramolecular hydrogels with tensile properties of 288±29 kPa at 516±37 % elongation and compressive properties of 572±34 kPa at 65±4 % strain with uniform swelling (320–350 %). Nuclear magnetic resonance and conductivity measurements confirmed SPA-rich blocks within the hydrogel network and the solvent-dependent copolymer structure. Furthermore, through varying the anionic acrylate concentration, ultimate strain between 222 % and 516 % was achieved at a constant elastic modulus. Additionally, electrical properties were tunable with conductivity reaching 156 mS/m at 7 MH in ultrapure water. This work advances applications of quaternized chitosan as an ionic crosslinker in printable conductive hydrogels, opening new applications in medical and technological fields.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".