Novel index for micromixing characterization and comparative analysis
Bibliographic record
Abstract
The most basic micromixer is a T- or Y-mixer, where two confluent streams mix due to transverse diffusion. To enhance micromixing, various modifications of T-mixers are reported such as heterogeneously charged walls, grooves on the channel base, geometric variations by introducing physical constrictions, etc. The performance of these reported designs is evaluated against the T-mixer in terms of the deviation from perfectly mixed state and mixing length (device length required to achieve perfect mixing). Although many studies have noticed the reduced flow rates for improved mixer designs, the residence time is not taken into consideration for micromixing performance evaluation. In this work, we propose a novel index, based on residence time, for micromixing characterization and comparative analysis. For any given mixer, the proposed index identifies the nondiffusive mixing enhancement with respect to the T-mixer. Various micromixers are evaluated using the proposed index to demonstrate the usefulness of the index. It is also shown that physical constriction mixer types are equivalent to T-mixers. The proposed index is found to be insightful and could be used as a benchmark for comparing different mixing strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".