Hierarchical Stable Enzyme Microenvironments for High‐Temperature Stability in Amine Solvents
Bibliographic record
Abstract
Several methods have been proposed for capturing the CO2 emitted into the atmosphere by human activity. To date, mainly amine‐based absorption processes are currently among the more promising systems for post‐combustion CO2 capture. Tertiary amine solvents obviate the need for a high solvent regeneration temperature and fast absorption can be achieved with the use of carbonic anhydrase (CA), as an activator. In this study, the capacity of CA immobilization on nanoporous microparticles hierarchically structured to enhance their stability in tertiary amines is investigated. These microstructures allow for an efficient supply and presentation of substrate in the non‐aqueous solvent to the enzyme catalytic center and the particles' large size is attractive to make separation and reuse facile. These hierarchically structured particles conserve 70% of their initial activity after 30 d at 50 °C in amine solvent, whereas the free enzyme shows no activity after 1 h in the same conditions. In this work, we have overcome the technical hurdle linked to the recovery of the biocatalyst after operation thereby reducing costs of the system and importantly these micro‐bioparticles have shown a remarkable increase of the thermal stability of CA in an amine‐based CO2 sequestration solvent as determined by a para‐nitrophenyl acetate assay.
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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) | 0.000 | 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 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".