Magnetic soft silicone elastomers with tunable mechanical properties for magnetically actuated devices
Bibliographic record
Abstract
Polydimethylsiloxane (PDMS)/iron oxide magnetic nanoparticle (NP) composites with tailored mechanical properties are prepared for use in magnetically actuated soft devices based on their controlled deformation by the application of an external magnetic field. This investigation reports the synthesis and functionalization of iron oxide NPs, the preparation of the PDMS/NP composites, the evaluation of NP dispersion using scanning electron microscopy (SEM) and optical microscopy, and the mechanical characterization of the composite films. Characterization includes rheological measurements as well as stress‐strain curves to obtain the Young modulus and elongation at break. SEM is used to probe individual NP dispersion, whereas optical microscopy provides rapid access to quantitative information about the size and distribution of particle aggregates. Results for nonfunctionalized (nf), oleic acid (OA)‐coated, and stearic acid (SA)‐coated iron oxide NPs and their blends are presented. PDMS elastomers containing both OA‐ and SA‐coated iron oxide NPs are found to have very low Young moduli with substantially higher resistance to failure than neat PDMS. For example, a formulation containing 2.5 wt% OA‐coated NPs and 2.5 wt% SA‐coated iron oxide NPs has a modulus of 0.15 MPa (compared with 0.24 MPa for neat PDMS), while it can withstand an elongation of about 1.5 times its initial length compared with only 0.3 times for neat PDMS. As a comparison, the modulus of the most commonly used commercial PDMS elastomer (Sylgard 184) is an order of magnitude higher than that of the composites prepared here, whereas maximum elongation is similar for the two. The formulations developed in this work could be used in applications where high deformability is required with limited magnetic field strength and/or NP loading.
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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) | 0.002 | 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 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".