Fabrication of Hollow Mesoporous Nanosized ZSM‐5 Catalyst with Superior Methanol‐to‐Hydrocarbons Performance by Controllable Desilication
Bibliographic record
Abstract
Abstract This work aims to improve the catalyst lifetime of ZSM‐5 for the methanol‐to‐hydrocarbons conversion by tuning the pore structure and external surface area on nano‐ZSM‐5 (100 nm). Several differently pore‐structured samples were synthesized based on the controllable desilication of nano‐ZSM‐5 by prolonging the alkali‐treatment time and introducing tetrapropylammonium hydroxide (TPAOH) into NaOH solution. 2 h NaOH treatment produced mesoporous ZSM‐5 with well‐maintained interior, and the catalytic lifetime was prolonged from 60 to 127 h along with increased isoparaffins selectivity owing to improved diffusion and accessibility of internal weak acid sites. If prolonging the treating time to 15 h, nano‐ZSM‐5 with initially formed hollow structure (HI‐Z5) was achieved, which shows relatively short lifetime of 85 h and high aromatics selectivity. After a secondary treatment of HI‐Z5 with fresh NaOH solution for 5 h, the formed thin shell and clean interior significantly improved diffusion, and the lifetime increased from 85 to 115 h. The aromatics selectivity was also increased by the enhanced acidity. Mesopores were introduced into the shell of HI‐Z5 after treating it with a freshly mixed solution of NaOH and TPAOH. The external surface area exhibited a 94 % increase, resulting in a high isoparaffins selectivity and a long lifetime of 149 h.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".