Thermal Degradation and Bimolecular Decomposition of 2-Ethoxyethanol in Binary Ethanol and Isobutanol Solvent Mixtures: A Computational Mechanistic Study
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A thorough computational study of a thermal degradation mechanism of 2-ethoxyethanol (2-EE) in the gas phase has been implemented using G3MP2 and G3B3 methods. The stationary point geometries were optimized at the B3LYP functional utilizing the 6-31G(d) basis set. Intrinsic reaction coordinate analysis was performed to determine the transition states on the potential energy surfaces. Nineteen primary different reaction mechanisms, along with the kinetic and thermodynamic parameters, are demonstrated. Most of the thermal degradation mechanisms result in a concerted transition state step as an endothermic process. Among 11 degradation pathways of 2-ethoxyethanol, the formation of ethylene glycol and ethylene is kinetically significant with an activation energy of 269 kJ mol –1 at the G3B3 method. However, the kinetic and thermodynamic calculations indicate that ethanol and ethanal’s formation is the most plausible reaction with an activation barrier of 287 kJ mol –1 at the G3B3 method. For the bimolecular dissociation reaction of 2-ethoxyethanol with ethanol, the pathway that produces ether, H 2, and ethanol is more likely to occur with a lower activation energy of 221 kJ mol –1 at the G3B3 method. Thus, 2-EE has experienced a set of complex unimolecular and bimolecular reactions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".