Selective hydrogenation of dimethyl terephthalate to dimethyl 1,4‐cyclohexanedicarboxylate over zeolite‐supported Ru catalysts
Bibliographic record
Abstract
Abstract Dimethyl 1,4‐cyclohexanedicarboxylate (DMCD) is an important chemical product, which is widely used in the synthesis of polyester resins, polyamides, and plasticizers. It is generally prepared via dimethyl terephthalate (DMT) selective hydrogenation. Herein, the zeolite (ZSM‐5‐25/50/100, ZSM‐35, MOR, and β)‐supported Ru catalysts were prepared and employed for DMT hydrogenation. Compared to the other zeolites, MOR‐supported Ru showed the best performance (DMT conversion: 100%, DMCD selectivity: 81.67% at the conditions of 180°C, 4 MPa H 2 , 3 h, 2‐propanol as solvent, and m(DMT):m(Cat.) = 5:1). After optimizing the reaction conditions, DMT conversion and DMCD selectivity finally reached 100% and 95.09%, respectively, at 140°C, 6 MPa H 2 , 4 h, ethyl acetate as solvent, and m(DMT):m(Cat.) = 2.5:1. Furthermore, Ru/MOR exhibited good reusability with no significant decrease after 5 cycles. The employed catalysts were comprehensively characterized by X‐ray diffraction (XRD), N 2 ‐physisorption, transmission electron microscopy (TEM), H 2 ‐TPR, NH 3 temperature‐programmed desorption (NH 3 ‐TPD), X‐ray photoelectron spectroscopy (XPS), inductively coupled plasma atomic emission spectrometry (ICP‐OES), and CO pulse experiment. The results indicated that Ru/MOR had higher Ru dispersion and stronger metal–support interaction. Moreover, Ru/MOR exhibited greater specific surface area and larger pore size, enhancing the adsorption efficiency of the reactants.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".