Monoethanolamine assisted CO2 hydrogenation to methanol – A computational study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• The traditional industrial carbon capture using MEA process was refined. • The hydrogenation of one of the capture products using MEA has been investigated. • Uncatalyzed and catalyzed-like hydrogenation of CO 2 to methanol has been studied. • The catalyzed-like mechanism is 7.5 more efficient than the uncatalyzed. Carbon dioxide emission to the atmosphere has to be reduced which can be done by utilizing CO 2 in the synthesis of added value products. At the same time this will lead to a process which can help to store renewable energy. The use of hydrogen produced from the electrolysis of water in carbon dioxide reduction to methanol could be an efficient way to store energy and to convert CO 2 into an added value product. The synthesis of methanol from CO 2 is usually performed catalytically in gas phase. Many scientists nowadays expressed interest in testing the feasibility of the reaction also in aqueous phase. In this direction, monoethanolamine (MEA) can be used as a solvent for capturing and trapping carbon dioxide in an aqueous phase. In this work, the hydrogenation of (2-hydroxyethyl) carbamic acid (HO-(CH 2 ) 2 -NH-COOH) which is one of the produced species during the capture process has been investigated by using computational tools. The uncatalyzed and catalyzed-like hydrogenation mechanisms leading to methanol (and MEA+water) as a product has been studied by using high level ab initio calculations in aqueous phase. The mechanisms have been described at the molecular level to provide a deeper understanding of the processes. The calculations indicate that the highest barrier height in the catalyzed-like process is only 114.67 kJ/mol, which is 227.71 kJ/mol lower than the corresponding step in the uncatalyzed mechanism. Furthermore, the energy storage efficiency of the catalyzed-like process is 96.68 %, which is 7.5 times more efficient than the uncatalyzed mechanism.
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.
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 | 0.002 |
| 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 it