O'Halloran Robyn C, Shapiro Alison J, Gupta Yagya, Guerard Jennifer J, Siple Dillon, Sadula Sunitha, Epps Thomas H, Vlachos Dionisios G, Levia Delphis F
Dept. of Civil, Construction, and Environmental Engineering, University of Delaware, Newark, Delaware 19716, United States.
Dept. of Chemical & Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.
ACS Sustain Chem Eng. 2025 Jun 9;13(24):9063-9073. doi: 10.1021/acssuschemeng.5c01598. eCollection 2025 Jun 23.
Lignin is a promising renewable feedstock to produce chemicals, fuels, and materials, yet a major challenge for lignocellulosic biorefineries is the significant variation in lignin content and structure. Traditional lignin characterization approaches require time-intensive, wet laboratory procedures, highlighting the need for rapid and reliable characterization methods to quantify lignin content and deconstruction products. This study presents a noninvasive, preharvest approach to determine lignin content, total phenolic monomer yield, and syringyl/guaiacyl (S/G) unit ratios in tree biomass from reductive catalytic fractionation (RCF) utilizing the optical properties of stemflow dissolved organic matter (DOM) as a proxy. A significant relationship between fluorescent signatures in stemflow DOM and constituent-specific composition (bark, twigs/branchlets, foliage) is identified, and stepwise multiple linear regression models showcase stemflow DOM component utilization to estimate lignin content, total phenolic monomer yield, and S/G ratio. Unlike traditional approaches, stemflow fluorescence can be quantified preharvest and pretransportation, enabling early lignin screening and prediction of deconstruction performance and product distribution. This stemflow fluorescence approach, harnessing the characterization of DOM in natural processes, is a higher-throughput, lower-cost screening method that could be a critical solution for biorefineries to overcome challenges due to biomass variability and facilitate feedstock screening, process optimization, and output product prediction.
木质素是一种很有前景的可再生原料,可用于生产化学品、燃料和材料,但木质纤维素生物精炼厂面临的一个主要挑战是木质素含量和结构存在显著差异。传统的木质素表征方法需要耗时的湿实验室程序,这凸显了对快速可靠的表征方法的需求,以量化木质素含量和解聚产物。本研究提出了一种非侵入性的收获前方法,利用茎流溶解有机物(DOM)的光学特性作为替代指标,来测定还原催化分馏(RCF)处理的树木生物质中的木质素含量、总酚单体产量以及紫丁香基/愈创木基(S/G)单元比率。研究发现茎流DOM中的荧光特征与特定成分组成(树皮、细枝/小枝、树叶)之间存在显著关系,逐步多元线性回归模型展示了利用茎流DOM成分来估算木质素含量、总酚单体产量和S/G比率。与传统方法不同,茎流荧光可以在收获前和运输前进行量化,从而实现早期木质素筛选以及对解构性能和产物分布的预测。这种利用自然过程中DOM表征的茎流荧光方法,是一种高通量、低成本的筛选方法,可能是生物精炼厂克服生物质变异性带来的挑战、促进原料筛选、工艺优化和产出产品预测的关键解决方案。