Direct CO<sub>2</sub> Capture by Alkali‐Dissolved Cellulose and Sequestration in Building Materials and Artificial Reef Structures
Bibliographic record
Abstract
Abstract Current carbon capture and utilization (CCU) technologies require high energy input and costly catalysts. Here, an effective pathway is offered that addresses climate action by atmospheric CO2 sequestration. Industrially relevant highly reactive alkali cellulose solutions are used as CO2 absorption media. The latter lead to mineralized cellulose materials (MCM) at a tailorable cellulose‐to‐mineral ratio, forming organic‐inorganic viscous systems (viscosity from 102 to 107 mPa s and storage modulus from 10 to 105 Pa). CO2 absorption and conversion into calcium carbonate and associated minerals translate to maximum absorption of 6.5 gCO2 gcellulose−1, tracking inversely with cellulose loading. Cellulose lean gels are easily converted into dry powders, shown as a functional component of ceramic glazes and cementitious composites. Meanwhile, cellulose‐rich gels are moldable and extrudable, yielding stone‐like structures tested as artificial substrates for coral reef restoration. Life Cycle Assessment (LCA) suggests new CCU opportunities for building materials, as demonstrated in underwater deployment for coral reef ecosystem restoration.
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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) | 0.001 | 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".