Experimental study and CFD simulation of VOC adsorption on 3D-printed zeolite honeycombs
Bibliographic record
Abstract
Three-dimensional additive manufacturing is an innovative method for creating customized geometries and rapid prototyping of honeycomb adsorbents. In this study, a combination of additive manufacturing and computational fluid dynamic simulation was used as a rapid prototyping approach to analyze volatile organic compound (VOC) adsorption behavior, which could be used prior to mass production of an adsorbent. Zeolite honeycomb adsorbent samples were prepared by direct ink writing and a three-dimensional mass and momentum model was used to predict the dynamic adsorption of VOCs on the prepared honeycombs. The simulation results were then compared to experimental measurements with the samples to assess the model’s accuracy and performance. Dynamic adsorption simulations predicted concentration breakthrough profiles for isopropanol (IPA) and 1,2,4-trimethylbenzene (TMB) with a mean absolute relative error below 7 % and 5 % breakthrough times within 9 % accuracy. The change from a triangular to a square channel shape resulted in a notable improvement in the removal efficiency (∼6%). This study demonstrates that combining simulation with direct ink writing is an effective approach for rapid prototyping of structured adsorbents. This methodology enables the rapid optimization and customization of adsorbents for targeted applications, such as VOC adsorption in automotive industry painting booths.
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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 | 0.000 |
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 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".