Closed form solutions of convection‐diffusion mechanisms in two dimensions for <scp> H <sub>2</sub> </scp> separation from ( <scp> H <sub>2</sub> </scp> / <scp> CO <sub>2</sub> </scp> ) mixture at room temperature
Bibliographic record
Abstract
Abstract Studies pertinent to the hydrogen based clean energy systems have seen a significant surge in this decade in order to meet the ever‐increasing demand for energy in an environmentally sustainable way. In order to harness hydrogen from available sources, development and characterization of zeolite membranes with high hydrogen selectivity is of pivotal importance. In this study, a sonication based hydrothermal technique is used for the synthesis of Deca‐Dodecasil‐rhombohedral (DDR) zeolite membrane. Permeation tests of single gas and mixture of gases were conducted by using an in‐house designed permeation cell. For the single gas permeation test, permeate flux was calculated using soap bubble flow meter under varying feed pressure (98‐392 kPa) at 303 K. For the mixed gas permeation test, separation selectivity was measured by using gas chromatography. A mathematical model was developed in order to predict the total volume flux of hydrogen. A simple, two‐dimensional parameter model was developed to simulate the steady state permeate flux, permeate and retentate streams, and the permeate concentrations for both H 2 and CO 2 . Fourier transform was found to give the best prediction for the axial diffusion coefficient, as well as the concentration distribution of both the components, (H 2 and CO 2 ). A good agreement was found between the observed and the calculated values for total volumetric permeate flux .The deviation between experimentally obtained results with the predicted values based on the analytical model was found to be as low as ±7%, ensuring adequate reliability of the proposed analytical model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".