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Solid acid catalysts for light naphtha isomerization: reaction mechanism, structure-activity relationships, and
Yongqi Zhou1, Xiaofan Yang1, Chaochao Zhang1
1School of Chemistry and Chemical Engineering, Jiangsu University, No#301, Xuefu Road, Zhenjiang, Jiangsu Province 212013, P.R. China. jxliu0804@ujs.edu.cn.
None:
The isomerization of light naphtha (C5-C6) alkanes offers a promising route to boost gasoline octane ratings without increasing aromatic content, which drives the urgent need for eco-friendly, stable solid acid catalysts. Adopting a perspective of "synergistic regulation of acidic site accessibility and carbocation intermediate stability", this review systematically examines the structure-performance relationships of three catalyst classes. For zeolites, topological confinement governs product distribution: MFI-type ten-membered rings favor mono-branched isomers (selectivity >75%), while BEA-type twelve-membered rings promote highly branched high-octane products. Hierarchical porosity enhances diffusion, though its impact on shape selectivity remains debated. Heteroatom doping (e.g., Ga and B) improves target isomer selectivity by ∼10-15%. For solid superacids, crystal phase engineering is decisive: SO42-/ZrO2's superacidity relies on the metastable tetragonal phase, enabling 30-50% n-hexane conversion at ∼200 °C; WO3/ZrO2 achieves 40-60% conversion at 250-300 °C, with promoters like CuGa raising 50 h activity retention from ∼55% to ∼83%. Emerging materials (ILs, MOFs, and carbon-based catalysts) offer structural tunability but face stability and scalability challenges. The critical bottlenecks currently limiting further development include (i) lack of quantitative data on carbocation intermediates, (ii) unclear boundaries of hierarchical pore effects on shape selectivity, and (iii) unverified in situ synergy mechanisms of acid sites in composite systems. Future directions should focus on constructing frustrated Lewis pairs, developing industrially relevant characterization techniques, and applying machine learning for catalyst design.
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