Bibliographic record
Abstract
Carbon dioxide is the most abundantly emitted greenhouse gas for which several technologies are being developed and intensively studied for capture and storage, except retrofit of amine scrubbers, none is proven at the commercial scale for treating post-combustion stack gases. Amine scrubbing, membrane separation, wet and dry mineral carbonation, pressure, temperature and electrical swing adsorptions, have been thoroughly reviewed in the 2005 survey by the Intergovernmental Panel on Climate Change (IPCC). However, an innovative approach that has escaped the attention of the recent IPCC concerns using biocatalysts for carbon dioxide hydration to bicarbonate. This review critically evaluates the recent patent literature regarding the different scrubber configurations in use for supporting carbonic anhydrase (CA), an ultrafast zinc-bearing metalloenzyme which catalyzes CO2 hydration to bicarbonate. It describes two membrane contactors using free soluble CA, the first one releasing gaseous CO2 and the second one being used to produce precipitated calcium carbonate (PCC). It also describes two contactors using immobilized CA, namely counter-current and cross-co-current packed columns, and two other contactors using either free or particle-immobilized CA. The review also deals with the use of a cohort of enzymes mimicking metabolic pathways to capture CO2 and potentially produce useful organic compounds. Keywords: Carbon dioxide, capture, storage, recycling, enzymes, carbonic anhydrase, rubisco
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".