Identification of Metal–Organic Frameworks for near Practical Energy Limit CO <sub>2</sub> Capture from Wet Flue Gases: An Integrated Atomistic and Process Simulation Screening of Experimental MOFs
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Metal–organic framework (MOF) materials have attracted significant attention as solid sorbents for low energy CO 2 capture with adsorption-based gas separation processes. In this work, an integrated screening workflow combining a series of atomistic and process simulations was applied to identify promising MOFs for a 4-step pressure-vacuum swing adsorption (P/VSA) process at three different CO 2 flue gas compositions (6%, 15% and 35%). Starting from 55,818 unique experimentally characterized MOFs, ∼19k porous MOFs were investigated via atomistic grand canonical Monte Carlo (GCMC) simulations and machine learning model-based process optimizations to accelerate the screening of a large candidate database. Thousands of MOFs were identified for each of the CO 2 compositions tested that could achieve within 4% of the practical energy limit of dry CO 2 capture for the P/VSA process while still meeting the 95% CO 2 purity and 90% recovery constraints. From this pool, 3D MOFs without open metal sites were subjected to the multicomponent (CO 2 /N 2 /H 2 O) GCMC simulations at 40% relative humidity. Based on these simulations, hundreds of MOFs were identified at each CO 2 composition that could retain 90% of their CO 2 capture at this humidity while also adsorbing a minimal amount of water. A geometric analysis of these high performing materials revealed that narrow, straight 1D-channels were a common structural motif for low energy wet flue gas CO 2 capture with P/VSA.
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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 | 0.001 |
| 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".