Lewatit 1065 VP OC for direct air capture; an analysis of adsorption characteristics using dynamic column breakthrough
Bibliographic record
Abstract
Direct air capture based on solid sorbents (S-DAC) is increasingly being recognized as an essential part of climate change mitigation strategies. This work investigates the adsorption characteristics for Lewatit 1065 OC VP which is considered a benchmark sorbent for S-DAC processes. A novel dynamic column breakthrough (DCB) instrument suited for DAC compositions is developed and described. Using both DCB experiments and volumetric measurements, we have identified a significant intra-batch variability in the CO 2 loading capacity. The observed difference in CO 2 capacity is found to depend entirely on the particle size from the manufacturer; thus, highlighting the importance of standardized sample selections when evaluating the properties of amine-functionalized materials. This ensures a solid database for process modeling and avoids misleading performance results of S-DAC processes. The mass transfer kinetics has been evaluated from both adsorption and desorption breakthrough experiments combined with modeling. The modeling effort includes a comparison between the commonly used linear driving force (LDF) model and a dual-site (DS) model to describe the CO 2 uptake. A significant dependence on macropore resistance is confirmed, though an additional transport resistance is required to fully describe the mass transfer kinetics. We hypothesize that the CO 2 -amine reaction is the key contributor to the added mass transfer resistance prompting the research on new amine-functionalized materials to focus on enhancing both the macropore resistance and reaction kinetics. • Novel instrument for dynamic column breakthrough at direct air capture compositions. • Intra-batch variability in CO 2 loading is confirmed for Lewatit 1065 VP OC. • Mass transfer kinetics depends on macropore resistance and reaction kinetics. • Dual-site model can describe adsorption and desorption rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".