CO2 Adsorption Enhancement via Modification of Porous Materials
Bibliographic record
Abstract
CO2 separation from flue gas streams is an essential issue as it is the main anthropogenic greenhouse gas contributing to global warming. Commercially used methods for CO2 capture includes absorption in liquid amine solutions which has its own drawbacks such as pipeline corrosion, oxidative degradation of amines, foaming in the gas-liquid interface, high energy requirement for regeneration, and high maintenance costs. To overcome these problems, adsorption onto solid adsorbents is proposed as an alternative. Although microporous activated carbons and zeolites (e.g. 5A, 13X and Faujasite) have been previously utilized in CO2 capture, their relatively low adsorption capacity as well as sensitivity to humidity can limit their usage. One of the remedies to enhance adsorption capacity of solid adsorbent is anchoring amine via amine impregnation. Additionally, new classes of porous materials called Metal Organic Frameworks (MOFs) with exceptional capture performance hold promise in this regard. In the course of the current research, firstly activated carbons were synthesized from oil sands coke which is abundantly available in Alberta. Then the synthesized microporous activated carbons were impregnated with amines and the performance of amine impregnated activated carbons for CO2 adsorption was investigated to find suitable conditions for amine impregnation and CO2 adsorption. The application of MOF materials for CO2 capture was also studied and a new method was proposed to overcome problems associated with low MOF capture capacity at atmospheric pressure and temperature. Finally, a MOF–based microwave sensor was used to monitor the adsorption progress based on the change in dielectric properties of MOF materials. The first part of this study investigated the effect of different activation agents such as KOH and MgO on porosity and capture capacity of activated carbons prepared from oil sands coke. Research findings suggested that KOH activation results in a narrow pore size distribution in micropore range, while adding MgO template provides mesoporous activated carbons with almost twice CO2 adsorption capacity. Further investigations were completed to study the effect of amine impregnation on CO2 capture enhancement of activated carbons prepared from oil sands coke. The impact of different parameters such as impregnation ratio, amine type, humidity and adsorption temperature was evaluated. The results showed that secondary amine (Diethanolamine) provided more active sites with affinity for CO2 compared to tertiary (methyl diethaonlamine) and bulky amines (tetra ethylene pentamine). There is an optimum value in amine loading which results in maximum adsorption capacity, while exceeding this point causes a decrease in adsorption capacity possibly due to the blockage of accessible volume of pores. The optimum adsorption temperature for our research was discovered to be at 50 °C. Also, the adsorption capacity of tertiary amine (MDEA in our work) was found to be improved by almost 1.6 times in the presence of 20% water vapor in feed stream compared to dry condition. In the next step, a novel MOF material combined with carbon nanotube (CNT) membrane was introduced. This study focuses on: 1) synthesis of MOF (MOF-74) and MOF-CNT using solvothermal method, 2) enhancement of MOF-CNT interfacial interaction by plasma treatment, 3) testing the MOF and MOF-CNT for carbon dioxide adsorption. The generation of new functional groups on the surface of CNT through plasma treatment provided favorable chemical affinity and stability to bridge MOF crystals and produce near-defect free MOF-membranes. Additionally, single-component CO2 adsorption isotherms showed a large increase in CO2 uptake (maximum of 10.70 mmol CO2/g) for MOF-CNT-BP samples compared to the parent Mg-MOF-74 (maximum of 3.13 mmol CO2/g). Additionally, a new MOF material (MOF-199) was integrated with a microwave resonator sensor and its performance for CO2 monitoring was compared to a commercial zeolite 13X. During the adsorption, the dielectric properties of MOF changed which could be detected by a change in resonant frequency. Despite the lower adsorption capacity of MOF-199 for low CO2 concentrations (<45 %), it showed higher sensitivity than zeolite 13X, which can be related to the initial dielectric properties of the virgin adsorbent. The ability of the sensor to monitor low concentrations of CO2 amplifies its potential in applications where information is required on the adsorbent replacement time.
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 | 0.000 | 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".