Optimized diffusion–convection compromise for reversible CO <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msub> </mml:math> capture on hydroxylated organo-montmorillonite
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) was reversibly captured at room temperature and normal pressure by a low cost hybrid absorbent (NaMt-H30) obtained through Na-montmorillonite intercalation with Boltorn dendrimer H30 (1 wt%). Measurements through thermal programmed desorption between 20 °C and 200 °C showed variations of the retention capacities of CO 2 (CRC) and water (WRC) according to the nitrogen stream throughput, contact time and injected CO 2 amount for adsorbent saturation. CRC and WRC modeling as functions of these parameters was achieved using a 3 3 factorial design involving 27 attempts. Model analysis revealed that the retention of CO 2 and water molecules depend not only on the individual effects of each parameters but also on their interactions. High nitrogen throughput and low amount of impregnation resulted in detrimental effects on both CO 2 and moisture retention. This was explained in terms of diffusion and mass loss during impregnation through forced convection. An optimum compromise between these factors in correlation with the contribution of moisture content turned out to be an essential requirement for achieving highest CRC levels that express the effective material affinity towards carbon dioxide. These findings provide a useful tool for rigorously optimizing the reversible capture of CO 2 by hydroxylated adsorbents.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".