Electro-osmosis-modulated biologically inspired flow of solid–liquid suspension in a channel with complex progressive wave: application of targeted drugging
Bibliographic record
Abstract
Electrokinetic microperistaltic pumps are important biomechanical devices that help target drug delivery to specific body parts. The current study focuses on the mathematical modelling and analysis of some important aspects of such flows in a channel with complex waves. It is considered that solid particles are uniformly distributed in the flow, and that these particles are non-conducting. Parameters such as the particle volume fraction coefficient, electro-osmotic parameter, and Helmholtz–Smoluchowski parameter are specifically focused on in this study. Equally sized spherical particles were uniformly floated in a non-Newtonian Powell–Eyring base fluid. The defined flow problem was modelled and analyzed analytically for the transport of a solid–liquid suspension. It is accepted that the flow is steady, non-turbulent, and propagating waves have a considerably longer wavelength compared to the amplitude. The conditions and assumptions lead to a model of the coupled partial differential equations of order two. The exact results of the homotopy perturbation method expansion method are obtained and shown accordingly. Predictions of the behavior of important parameters are displayed in the figures. The impact of several parameters was analyzed. The current study involved transporting or targeted drug delivery systems using peristaltic micropumps and magnetic fields in the pharmacological engineering of biofluids, such as blood.
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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) | 0.000 | 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 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".