Controlling the Water Diffusion Inside Smart 4D‐Printed HBC by Functional Channels
Bibliographic record
Abstract
Abstract Hydromorph Biocomposites (HBCs) are self‐shaping materials whose motions are actuated by moisture‐induced swelling of natural fibers and designed through a multilayered bioinspired material architecture. Their reactivity, i.e., kinetic of actuation, is relatively slow and is currently limited by the moisture transport. Porosities are commonly assumed to be a defect in composite materials that results in reduced mechanical performance. However, in biological structures porosities within the tissue architecture provide essential functions (lightness, moisture transport, and actuation). Inspired by biological mesostructures, a 3D‐printing process is applied to precisely define the architecture of an HBC that incorporates functional porosities, i.e., channels. The purpose is to improve the moisture‐induced actuation reactivity without compromising any other functional performance. First, 3D‐printed wood fiber‐reinforced biocomposites are designed with various channel content (from 0% to 10%), size (from 0.5 to 3.0 mm), and distribution patterns across the samples. Immersed in water, these novel HBCs with tailored channel structures fasten the water transport by triggering capillary transport. The presented results demonstrate that implementing functional channels in the mesostructure of 4D‐printed HBC bilayers makes it possible to achieve an eight‐fold improvement in the speed of shape‐change transformation while enabling new capabilities to functionally tune the morphing performance of HBCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 teacher head, 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".