Oxidation kinetics of 2‐methyl‐1,3‐propanediol to methacrylic acid in a fluidized bed reactor
Bibliographic record
Abstract
Abstract Methacrylic acid (MAA) is a specialty monomer for poly methyl methacrylate (PMMA). Partial oxidation of 2‐methyl‐1,3‐propanediol (2MDPO) to MAA is an alternative to commercial technology with fewer process steps, less cost and toxic feedstocks, and longer catalyst lifetime. Here, we evaluated the effect of 2MPDO and oxygen concentrations, reaction temperature, and contact time on product selectivity and 2MPDO conversion over VOCu0.5/Cs(NH4)2PMo12O40. Higher temperature increases selectivity and conversion; MAA selectivity reached 46% at 69% 2MPDO conversion. Higher 2MPDO to O2 ratio favours higher MAA selectivity due to methacrolein (intermediate) oxidation to MAA (a higher ratio favours consecutive oxidation) or lower combustion of MAA to COx. Shorter contact times decrease MAA selectivity. The Mars van Krevelen model characterizes the experimental data better than both the Langmuir‐Hinshelwood or Eley‐Rideal models; The reaction sequence involves both direct and indirect reactions in which 2MPDO reacts to methacrolein (MAC) as an intermediate and then it oxidizes to MAA (indirect) and/or 2MPDO directly oxidizes to MAA but the indirect reaction rate to MAA is 50 times faster than the direct reaction rate. The reaction is first order with respect 2MPDO and oxygen, and the rate‐limiting step is 2MPDO activation on oxidized sites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".