Analysis on the catalytic performance of catalysts and cost-effectiveness and selectivity of methanol carbonylation
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
Methanol is an essential raw chemical that is also a source of environmentally friendly energy for use in automobiles and other uses. It is one of the most important organic raw materials that is used in the manufacturing of a wide variety of organic compounds, such as chloromethane, methylamine, and dimethyl sulfate, among many others. It is one of the raw materials used in the production of dimethyl terephthalate, methyl methacrylate, and methyl acrylate, in addition to being a raw material for pesticides (insecticides, acaricides), pharmaceuticals (sulphonamides, co-trimoxazole, etc.), and other chemicals (such as sulfonamides and co-trimoxazole, for example). This paper investigates the catalytic performance of various catalysts for methanol carbonylation through a method of literature review. The purpose of the study is to determine which catalyst offers the most value for the price. In addition to that, the research investigates the efficiency in terms of cost and the selectivity of the methanol carbonylation process.
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 | 0.000 | 0.001 |
| 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