Mass Transfer at a Vertical Oscillating Screen Stack in Relation to Catalytic and Electrochemical Reactor Design
Bibliographic record
Abstract
The aim of the present work is to improve the performance of the parallel plate catalytic and electrochemical reactor in conducting diffusion controlled reactions by using parallel oscillating vertical arrays of closely packed screens. To this end, rates of mass transfer at oscillating vertical single screen and arrays of closely packed screens were studied by measuring the limiting current of the cathodic deposition of copper from acidified copper sulfate. Variables studied were amplitude and frequency of vibration, screen mesh number, and number of screens per stack. The mass transfer data at a single vibrating screen were correlated by the equation: Sh = 0.73Sc 0.33 Re v 0.38 . Increasing mesh number of the screen was found to increase the mass transfer coefficient, while increasing the number of screens per stack decreased the mass transfer coefficient below single screen value. The enhancement ratio between oscillating screen array and oscillating vertical plate (the volumetric mass transfer coefficient ratio) ranged from 11.7 to 27.87 depending on the operating conditions. Electrical energy consumption of the present reactor was found to be lower than the value reported in the literature for electrowinning of copper using traditional parallel plate reactor. In view of this, the possibility of using oscillating vertical screen stack in building high space-time yield modular parallel plate electrochemical reactor suitable for wastewater treatment, electroorganic synthesis, and electrochemical energy storage via redox cells was noted. Also, the possibility of using the present geometry in building catalytic reactors suitable for conducting diffusion controlled liquid–solid reaction was highlighted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".