Study of Functionally Graded Temperature-Sensitive Hydrogel Micro-Valve Considering Fluid-Structure Interactions
Bibliographic record
Abstract
Hydrogels are the smart polymeric materials, which undergo large deformation when they are subjected to different physical and chemical stimuli in contact with fluids. These materials can be applied as sensors and actuators for instance in microfluidics in which the fluidsolid interactions have an important effect on its performance. On the other hand, the use of graded materials is also important considering their advantages. In this study, the behavior of a functionally graded temperature sensitive hydrogel micro-valve is investigated through considering the fluid-solid interactions. In this regard, the appropriate numerical tool for finite element modeling of a functionally graded hydrogel micro-valve has been developed that it has been implemented in both non fluid-solid interactions and fluid-solid interactions simulation. The homogeneous cases of the micro-valve have also been considered to distinguish the functionally graded temperature sensitive hydrogel micro-valve effect. The results indicate that the effect of fluid-solid interactions was important and have considerable impact on micro-valve operating parameters particularly its closing temperature and fluid flow rate. Thus, a comprehensive study on hydrogel-based micro-valve has been presented considering operating parameters such as inlet pressure and cross linking density of hydrogel.
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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".