Carbon Dioxide Adsorption by Metal Organic Frameworks (Synthesis, Testing and Modeling)
Bibliographic record
Abstract
It is essential to capture carbon dioxide from flue gas because it is considered one of the main causes of global warming. Several materials and various methods have been reported for the CO2 capturing including adsorption onto zeolites, porous membranes, and absorption in amine solutions. All such methods require high energy input and high cost. New classes of porous materials called Metal Organic Frameworks (MOFs) exhibit excellent performance in capturing carbon dioxide from a gas mixture.\nIn the course of the current research, a novel MOF synthesis method using combined microwave and ultrasound, and microwave only was introduced and successfully applied to synthesize two different MOFs named IRMOF-1 and CPM-5. The scope of the research focuses on: 1) synthesis of two different MOFs (e.g. IRMOF-1 and CPM-5) using innovative non-traditional methods including microwave and ultrasound irradiation, and employing the optimization of three synthesis conditions: synthesis temperature, time and solvent ratio, 2) testing the MOFs for carbon dioxide adsorption to obtain the adsorption properties such as adsorption equilibrium isotherm, CO2 diffusivity coefficient, adsorption kinetics and isosteric heat of adsorption, 3) testing of the best MOF for CO2 adsorption using fixed bed adsorption micro-reactor column configuration at different experimental conditions such as adsorption temperature, feed concentration and feed flowrate, 4) modeling of the breakthrough curve using COMSOL simulation and comparing it with the experimental breakthrough curves.\nThe microwave irradiations drastically reduce the synthesis time of CPM-5 samples from 5 days using a traditional method (e.g. conventional oven) to 10 min. The outcome of the research indicated that the IRMOF-1 and CPM-5 samples synthesized using the novel synthesis methods exhibit unique properties compared to traditional synthesis method. The improved properties of the final product such as: lower particle size and narrower size distribution, more constructed crystallites, high surface area, high CO2 adsorption isotherm capacity (e.g. 2.3 mmol CO2/ g) , high selectivity factor of CO2 over N2 ( e.g 16.1 at 298 K), low isosteric heat of adsorption, and a high CO2 dynamic adsorption capacity (e.g. 11.9 wt. % at 298 K), were noted. As a result the microwave synthesized CPM-5 samples can be considered as an attractive adsorbent for the separation of CO2 from flue gas.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".