Machine learning and metaheuristics in microfluidic transport characterization and optimization: <scp>CFD</scp> and experimental study integrated with predictive modelling
Bibliographic record
Abstract
Abstract This study presents a comprehensive numerical and experimental analysis on microfluidic cell lysis through computational fluid dynamics (CFD), data‐driven modelling, and multi‐objective optimization. The proposed intelligent framework integrates artificial intelligence and CFD for data generation and extraction, alongside machine learning analysis and experimental studies for transport phenomena characterization in the cell lysis process. The framework explores compound effects of various inflow Reynolds numbers and geometrical parameters, including obstacle configurations and microchannel thickness. It shows substantial effects on flow patterns and mixing in varied microfluidic designs. A surrogate model, developed via central composite design, exhibits high accuracy in assessing system functionality (). The height of the implemented baffles from its lower value to the upper bound resulted in more than 42% and 14% increase in the mixing index at low and high Reynolds numbers, respectively, with minimal impact on pressure drop. The framework introduces data‐driven modelling coupled with multi‐objective optimization by desirability function (DF), non‐dominated sorting genetic algorithm (NSGA‐II), and differential evolution (DE). In the optimization of microfluidic processes, machine learning algorithms outperform desirability‐based methods, and the DE algorithm surpasses the NSGA‐II. An optimum micromixing reducing the mixing length by over 50% and mixing index above 97% achieved, fabricated, and experimental investigations conducted to validate numerical process. Through the precise control of microfluidic variables and the exploitation of microtransfer phenomena, it is possible to enhance the efficiency and selectivity of cell lysis. This not only improves the accuracy of diagnostic information but also opens up new avenues for personalized medicine and therapeutic development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".