Characterization of swelling behavior of carbon nano-filler modified polydimethylsiloxane composites
Bibliographic record
Abstract
Polymers may absorb fluids from their surroundings via the natural phenomenon of swelling. Dimensional changes due to swelling can affect the function of polymer components, such as in the case of seals, microfluidic components and electromechanical sensors. An understanding of the swelling behavior of polymers and means for controlling it can improve the design of polymer components, for example, for the previously mentioned applications. Carbon-based fillers have risen in popularity to be used for the property enhancement of resulting polymer composites. The present investigation focuses on the effects of three carbon-based nano-fillers (graphene nano-platelets, carbon black, and graphene nano-scrolls) on the dimensional changes of polydimethylsiloxane composites due to swelling when immersed in certain organic solvents. For this study, a facile and expedient methodology comprised of optical measurements in conjunction with digital image analysis was developed as the primary experimental technique to quantify swelling dimensional changes of the prepared composites. Other experimental techniques assessed polymer cross-linking densities and elastic mechanical properties of the various materials. The study revealed that the addition of certain carbon-based nano-fillers increased the overall swelling of the composites. The extent of swelling further depended on the organic solvent in which the composites were immersed in. Experimental findings are contrasted with published models for swelling prediction, and the role of filler morphology on swelling behavior is discussed.
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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) | 0.001 | 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".