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 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) | 0.000 | 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 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".