Leaf-vein-mimetic fins enhance Fischer-Tropsch long-chain selectivity based on particle-resolved analysis

• A leaf-vein-mimic internal is proposed to enhance Fischer-Tropsch reactions. • A particle-resolved model of reaction kinetics, heat/mass transfer is developed. • Hot spots are suppressed with the maximum temperature rise reduced by over 60% • The C 5+ yield is enhanced by about 7% due to improved temperature uniformity. Fischer-Tropsch synthesis (FTS) plays a pivotal role in the sustainable production of liquid hydrocarbon fuels from syngas derived from biomass or catalytic CO 2 reduction. Fixed-bed reactors, although being widely used in FTS, often suffer from the presence of hot spots that accelerate catalyst deactivation and reduce selectivity towards high-value long-chain liquid fuels. Inspired by the hierarchical distribution network structure of leaf veins, this work introduces novel leaf-vein-mimetic (LVM) fins to enhance FTS long-chain selectivity. A particle-resolved FTS computation model integrating particle reaction kinetics, heat/mass transfer processes, and explicit particle-fin physical interactions is developed. Optimized configurations show a reduction of over 60% in the maximum temperature rise (at δ 0 = 0.5) and an increase of nearly 7% in C 5+ yield. The underlying mechanism is attributed to significantly improved radial temperature uniformity and the suppression of hot spots enabled by the superior heat dissipation capability of LVM fins. This work offers novel routes for producing high-value long-chain liquid hydrocarbon fuels from syngas via fixed-bed reactors.

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Publication Details

Journal
Fuel
Published
2026-02-13
DOI
https://doi.org/10.1016/j.fuel.2026.138746
Primary Topic
Catalysts for Methane Reforming
Type
article
Field-Weighted Citation Impact
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article

Leaf-vein-mimetic fins enhance Fischer-Tropsch long-chain selectivity based on particle-resolved analysis

Haichen Yao, Mingxi Jiang, Qiyan Li, Shaoyang Li et al.
Fuel
Catalysts for Methane Reforming
article

Leaf-vein-mimetic fins enhance Fischer-Tropsch long-chain selectivity based on particle-resolved analysis

Haichen Yao, Mingxi Jiang, Qiyan Li, Shaoyang Li, Tao Wang, Jianing Fan, Xianglei Liu
article en

Abstract

• A leaf-vein-mimic internal is proposed to enhance Fischer-Tropsch reactions. • A particle-resolved model of reaction kinetics, heat/mass transfer is developed. • Hot spots are suppressed with the maximum temperature rise reduced by over 60% • The C 5+ yield is enhanced by about 7% due to improved temperature uniformity. Fischer-Tropsch synthesis (FTS) plays a pivotal role in the sustainable production of liquid hydrocarbon fuels from syngas derived from biomass or catalytic CO 2 reduction. Fixed-bed reactors, although being widely used in FTS, often suffer from the presence of hot spots that accelerate catalyst deactivation and reduce selectivity towards high-value long-chain liquid fuels. Inspired by the hierarchical distribution network structure of leaf veins, this work introduces novel leaf-vein-mimetic (LVM) fins to enhance FTS long-chain selectivity. A particle-resolved FTS computation model integrating particle reaction kinetics, heat/mass transfer processes, and explicit particle-fin physical interactions is developed. Optimized configurations show a reduction of over 60% in the maximum temperature rise (at δ 0 = 0.5) and an increase of nearly 7% in C 5+ yield. The underlying mechanism is attributed to significantly improved radial temperature uniformity and the suppression of hot spots enabled by the superior heat dissipation capability of LVM fins. This work offers novel routes for producing high-value long-chain liquid hydrocarbon fuels from syngas via fixed-bed reactors.

FuelVol. 418
China Guodian Corporation (China) (CN), Ministry of Industry and Information Technology (CN), Nanjing University of Aeronautics and Astronautics (CN)
Responsible consumption and production
Openalex Percentile: Top 57%
Catalysts for Methane Reforming
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Leaf-vein-mimetic fins enhance Fischer-Tropsch long-chain selectivity based on particle-resolved analysis — Haichen Yao, Mingxi Jiang, et al. · Fuel (2026) | TGRS Research Map | TGRS