Intensified CO<sub>2</sub> Capture in Structured Microreactors with Carbonic Anhydrase Enzyme Immobilized on Open-Cell Foam Packings
Bibliographic record
Abstract
To mitigate climate change, it is of utmost importance to develop innovative, energy-efficient technologies to capture CO 2 . In this context, this study is envisioned as an exploration of opportunities by proposing an intensified CO 2 capture process in a novel immobilized enzyme structured packed-bed microreactor (IE-SPBμR) with human carbonic anhydrase II (hCA II) enzyme attached on open-cell foam packings, which constitutes a promising alternative for mass-transfer-limited reactions. Enzyme-mediated CO 2 uptake and fluid dynamics in IE-SPBμR were analyzed for different microreactor operating conditions and enzyme loadings, and using different buffer systems, via a 3D two-phase flow model coupled with governing equations of mass transport in gas/liquid phases and diffusion/reaction within the enzymatic catalyst layer. The results showed that IE-SPBμR generates a remarkable intensification of CO 2 uptake at low hCA II enzyme loadings due to enhanced mass transfer over large interfacial areas that allow greater use of the large hCA II enzyme turnover while, at the same time, maintaining low pressure losses. The performance of IE-SPBμR is significantly enhanced by increasing liquid flow rate, decreasing pore diameter of foam packing, operating with buffers with low dissociation constant of protonated form and widening packed bed height while preserving pressure drop at low levels. An important advantage is that the pressure drop in IE-SPBμR with selected open-cell foam packings is 1–2 orders of magnitude lower, compared to packed-bed microreactors of similar specific surface area, thus allowing the process volume to be easily scaled-up by increasing the height of packed bed. The significant process intensification highlights the attractiveness of IE-SPBμRs, connected in modular units, especially for capturing CO 2 from dispersed sources.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".