Tough Supramolecular Hydrogels Crafted via Lignin‐Induced Self‐Assembly
Bibliographic record
Abstract
Abstract Supramolecular hydrogels are typically assembled through weak non‐covalent interactions, posing a significant challenge in achieving ultra strength. Developing a higher strength based on molecular/nanoscale engineering concepts is a potential improvement strategy. Herein, a super‐tough supramolecular hydrogel is assembled by gradually diffusing lignosulfonate sodium (LS) into a polyvinyl alcohol (PVA) solution. Both simulations and analytical results indicate that the assembly and subsequent enhancement of the crosslinked network are primarily attributed to LS‐induced formation and gradual densification of strong crystalline domains within the hydrogel. The optimized hydrogel exhibits impressive mechanical properties with tensile strength of ≈20 MPa, Young's modulus of ≈14 MPa, and toughness of ≈50 MJ m⁻ 3 , making it the strongest lignin‐PVA/polymer hydrogel known so far. Moreover, LS provides the supramolecular hydrogel with excellent low‐temperature stability (<‐60 °C), antibacterial, and UV‐blocking capability (≈100%). Interestingly, the diffusion ability of LS is demonstrated for self‐restructuring damaged supramolecular hydrogel, achieving 3D patterning on hydrogel surfaces, and enhancing the local strength of the freeze‐thaw PVA hydrogel. The goal is to foster a versatile hydrogel platform by combining eco‐friendly LS with biocompatible PVA, paving the way for innovation and interdisciplinarity in biomedicine, engineering materials, and forestry science.
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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) | 0.001 | 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".