journal article·2026-03Open access

High‐Selective and Ultrafast Furfural Removal From Simulated Biomass Hydrolysates via a Sustainable MOF‐Based Strategy

Hao Zhao, Hui Zhang, Xingjie Wang, Xiaobao Yang, Feng Peng, Wei Qi, Junli Ren

Small· Volume 22· Issue 15

Abstract

ABSTRACT The microbial fermentation efficiency of biomass‐derived sugars into biofuels and bioplastics is severely hindered by inhibitory compounds such as furfural and 5‐hydroxymethylfurfural generated during biomass hydrolysis, highlighting the urgent need for their selective and efficient removal. Herein, we report the design and application of a metal–organic framework (MOF), MIL‐53‐TDC, featuring a well‐defined one‐dimensional pore structure that enables rapid and selective molecular sieving of furfural from pentose sugars. Comprehensive static and dynamic adsorption studies reveal an intriguing two‐step adsorption mechanism. While size exclusion governs the selectivity, hydrogen bonding interactions between furfural and the pore walls facilitate accelerated mass transport. This synergy enables high‐performance furfural capture with negligible pentose adsorption. MIL‐53‐TDC achieves a maximum furfural adsorption capacity of 424.6 mg g − 1 (98% removal) and ultrafast kinetics (∼90% uptake within 5 min). In dynamic breakthrough tests using simulated hydrolysates, the material exhibits complete furfural selectivity and a capacity of 202.9 mg g − 1 . Both experimental data and calculations confirm the dominant role of hydrogen bonding in furfural adsorption, contributing to facile regeneration and performance, significantly surpassing previously reported adsorbents. This work establishes MIL‐53‐TDC as a robust and regenerable adsorbent for hydrolysate purification, overcoming a key bottleneck in lignocellulosic bioconversion and advancing the sustainable production of biofuels and bioplastics.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Catalysis for Biomass Conversion · Biofuel production and bioconversion · Food Chemistry and Fat Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Advanced Papermaking and Paper‐based Materials School of Light Industry and Engineering South China University of Technology Guangzhou China

School of Physics and Optoelectronics South China University of Technology Guangzhou China

Beijing Key Laboratory of Lignocellulosic Chemistry, College of Materials Science and Technology Beijing Forestry University Beijing China

Guangzhou Institute of Energy Conversion Chinese Academy of Sciences Guangzhou China

Funding

National Natural Science Foundation of China · Guangdong Provincial Pearl River Talents Program · Fundamental Research Funds for the Central Universities

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