Patterning the Stiffness of Elastomeric Nanocomposites by Magnetophoretic Control of Cross-linking Impeder Distribution
Bibliographic record
Abstract
We report a novel method to pattern the stiffness of an elastomeric nanocomposite by selectively impeding the cross-linking reactions at desired locations while curing. This is accomplished by using a magnetic field to enforce a desired concentration distribution of colloidal magnetite nanoparticles (MNPs) in the liquid precursor of polydimethysiloxane (PDMS) elastomer. MNPs impede the cross-linking of PDMS; when they are dispersed in liquid PDMS, the cured elastomer exhibits lower stiffness in portions containing a higher nanoparticle concentration. Consequently, a desired stiffness pattern is produced by selecting the required magnetic field distribution a priori. Up to 200% variation in the reduced modulus is observed over a 2 mm length, and gradients of up to 12.6 MPa·mm−1 are obtained. This is a significant improvement over conventional nanocomposite systems where only small unidirectional variations can be achieved by varying nanoparticle concentration. The method has promising prospects in additive manufacturing; it can be integrated with existing systems thereby adding the capability to produce microscale heterogeneities in mechanical properties.
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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".