A novel strategy for designing high-performance self-healing polysiloxane-polyurea composites enhanced by dopamine-grafted cellulose nanofibers and Zn2+
Bibliographic record
Abstract
Inspired by natural mussels, a novel dopamine-grafted cellulose nanofiber (DA-CNF) functional filler and Zn 2+ were incorporated into polysiloxane-polyurea to create advanced composites for Internet of Things applications. Through experimental characterization, molecular dynamics (MD) simulations and finite element (FE) analysis, we thoroughly investigated the mechanism by which DA-CNF and Zn 2+ improve the mechanical and self-healing properties of the polymer. The innovative synergistic effect of extra-added dynamic hydrogen bonds and metal ion coordination bonds between the filler and matrix simultaneously enhanced mechanical strength and self-healing efficiency, overcoming the traditional trade-off problem in conventional polymers. The results showed that the tensile strength and healing efficiency of DA-CNF/PU@Zn 2+ were 198.89 % and 104.77 % of the value of the control sample, respectively. This performance significantly surpasses that of previously reported self-healing polydimethylsiloxane-based materials. In the EMI shielding tests for Internet of Things applications, the conductive composite film fabricated with DA-CNF/PU@Zn 2+ and silver nanowires (AgNWs) effectively addresses the issues of resource waste and device stability. These findings offer a new strategy for designing high-performance self-healing composite materials with significant potential for applications in electronics, aerospace, automotive and wearable devices.
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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) | 0.001 | 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".