Dual Microreactor Concept for Efficient Enzymatic Direct Air Capture and Formate Generation through CO<sub>2</sub> Reduction Combining Golden Hydrogen’s Potential
Bibliographic record
Abstract
Enzyme-mediated direct CO 2 hydrogenation with upstream enzyme-mediated direct air capture (DAC) in interconnected multiphase fixed-bed microreactors (FBMRs) has been envisioned for the first time for the bioconversion of atmospheric CO 2 . Both processes are catalyzed by the thermostable hydrogen-dependent CO 2 reductase (HDCR) enzyme from the thermophilic acetogenic bacterium Thermoanaerobacter kivui and the human carbonic anhydrase II (hCA II) enzyme immobilized on the surface of solid particles, respectively. The performance of the integrated enzymatic processes was evaluated using 3D models linking Euler–Euler equations of multiphase flow and mass transport equations in liquid and gas phases and diffusion/enzymatic reaction models within hCA II and HDCR enzyme layers. When coupled to an enzymatic DAC FBMR that extracts on-road CO 2 under traffic congestion or CO 2 from the atmosphere, the direct CO 2 hydrogenation FBMR charged with the same enzyme loading and operating at 70 °C can reduce more CO 2 than the DAC FBMR can remove due to the enhanced interphase mass transfer. The coupled multiphase FBMRs with immobilized hCA II/HDCR enzymes can also operate with higher CO 2 concentrations (CO 2 emissions from residential, commercial, and public services buildings), but the enzyme-mediated hydrogenation process (with large HDCR enzyme loadings) is controlled by mass transfer and reverse formate oxidation. FBMRs, which can potentially be installed on heavy-duty and marine vehicles, must be designed and operated under conditions that ensure that the benefits of high CO 2 capture and reduction rates outweigh the cost of energy requirements. Formate must be removed from the reaction system to ensure liquid recirculation and no formate inhibition of the CO 2 reduction.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".