Chaotic Direct Ink Writing (ChDIW) of Hybrid Hydrogels: Implication for Fabrication of Micro‐ordered Multifunctional Cryogels
Bibliographic record
Abstract
The modern era demands multifunctional materials to support advanced technologies and tackle complex environmental issues caused by these innovations. Consequently, material hybridization has garnered significant attention as a strategy to design materials with prescribed multifunctional properties. Drawing inspiration from nature, a multi-scale material design approach is proposed to produce 3D-shaped hybrid materials by combining chaotic flows with direct ink writing (ChDIW). This approach enables the formation of predictable multilayered filaments with tunable microscale internal architectures using just a single printhead. By assigning different nanomaterials to each layer, 3D-printed hydrogels and cryogels with diverse functionalities, such as electrical conductivity and magnetism are successfully produced. Furthermore, control over the microscale pore morphology within each cryogel filament is achieved, resulting in a side-by-side dual-pore network sharing a large interfacial area. The ChDIW is compatible with different types of hydrogels as long as the rheological features of the printing materials are well-regulated. To showcase the potential of these multilayered cryogels, their electromagnetic interference shielding performance is evaluated, and they reveal an absorption-dominant mechanism with an excellent absorption coefficient of 0.71. This work opens new avenues in soft matter and cryogel engineering, demonstrating how simplicity can generate complexity.
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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) | 0.000 | 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".