Nanocatalytic chemohydrodynamic instability: Deposition effects
Bibliographic record
Abstract
Due to the high surface area to volume ratio of nanoparticles, nanocatalytic reactive flows are widely utilized in various applications, such as water purification, fuel cell, energy storage, and biodiesel production. The implementation of nanocatalysts in porous media flow, such as oil recovery and contaminant transport in soil, can trigger or modify the interfacial instabilities called viscous fingering. These instabilities grow at the interface of the fluids when a less viscous fluid displaces a high viscous one in porous media. Here the flow dynamics and the total amount of chemical product are investigated when two reactive miscible fluids meet in a porous medium while undergoing A+B+n → C+n reaction. Nanocatalysts (n) are dispersed in the displacing fluid and deposited gradually with time. Four generic regimes are observed over time as a result of the particle deposition: (1) the initial diffusive regime, where the flow is stable with decreasing production rate, (2) the mixing-dominant fingering regime, where the flow is unstable and the production rate generally increases, (3) the transition regime, where the production rate generally decreases regardless of whether the system is stable or unstable, and (4) the final zero-production regime, where the product diffuses and fades away in the channel. Although the general trend shows a decreasing reaction rate with nanocatalysts deposition, there is a period in which the production rate increases due to the moderate deposition rates. Such an increase of production, however, is not observed in two groups: first, those systems in which the nanocatalysts do not change the viscosity of the base fluid and, second, a subgroup of the systems that are stable before and after the reaction in the absence of deposition.
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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) | 0.000 | 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 | 0.001 |
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".