Monoethanolamine assisted CO2 hydrogenation to methanol – A computational study
Bibliographic record
Abstract
Carbon dioxide emission to the atmosphere has to be reduced which can be done by utilizing CO2 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 CO2 into an added value product. The synthesis of methanol from CO2 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-(CH2)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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".