Palacio Lozano Diana Catalina, Lester Daniel W, Town J S, McKenna Amy M, Wills Martin
Department of Chemistry, University of Warwick, Coventry CV4 7AL, U.K.
Polymer Characterisation Research Technology Platform, University of Warwick, Coventry CV4 7AL, U.K.
Energy Fuels. 2024 Aug 20;38(17):16473-16489. doi: 10.1021/acs.energyfuels.4c02605. eCollection 2024 Sep 5.
Bio-oils contain a substantial number of highly oxygenated hydrocarbons, which often exhibit low thermal stability during storage, handling, and refining. The primary objectives of this study are to characterize the hydroxyl group in bio-oil fractions and to investigate the relationship between the type of hydroxyl group and accelerated aging behavior. A bio-oil was fractionated into five solubility-based fractions, classified in two main groups: water-soluble and water-insoluble fractions. These fractions were then subjected to chemoselective reactions to tag molecules containing hydroxyl groups and analyzed by negative-ion electrospray ionization 21 T Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The fractions were also subjected to accelerated aging experiments and characterized by FT-ICR MS and bulk viscosity measurements. Extracting insightful information from ultrahigh-resolution data to aid in predicting upgrading methodologies and instability behaviors of bio-oils is challenging due to the complexity of the data. To address this, an unsupervised learning technique, k-means clustering analysis, was used to semiquantify molecular compositions with a close Euclidean distance within the (/, /) chemical space. The combination of k-means analysis with findings from chemoselective reactions allowed the distinctive hydroxyl functionalities across the samples to be inferred. Our results indicate that the hexane-soluble fraction contained numerous molecules containing primary and secondary alcohols, while the water-soluble fraction displayed diverse groups of oxygenated compounds, clustered near to carbohydrate-like and pyrolytic humin-like materials. Despite its high oxygen content, the water-soluble fraction showed minimal changes in viscosity during aging. In contrast, a significant increase in viscosity was observed in the water-insoluble materials, specifically, the low- and high-molecular-weight lignin fractions (LMWL and HMWL, respectively). Among these two fractions, the HMWL exhibited the highest increase in viscosity after only 4 h of accelerated aging. Our results indicate that this aging behavior is attributed to an increased number of molecular compositions containing phenolic groups. Thus, the chemical compositions within the HMWL are the major contributors to the viscosity changes in the bio-oil under accelerated aging conditions. This highlights the crucial role of oxygen functionality in bio-oil aging, suggesting that a high oxygen content alone does not necessarily correlate with an increase of viscosity. Unlike other bio-oil categorization methods based on constrained molecule locations within the van Krevelen compositional space, k-means clustering can identify patterns within ultrahigh-resolution data inherent to the unique chemical fingerprint of each sample.
生物油含有大量高度氧化的碳氢化合物,在储存、处理和精炼过程中,这些化合物通常表现出较低的热稳定性。本研究的主要目的是对生物油馏分中的羟基进行表征,并研究羟基类型与加速老化行为之间的关系。将一种生物油分离成五个基于溶解度的馏分,分为两个主要类别:水溶性馏分和水不溶性馏分。然后让这些馏分进行化学选择性反应,以标记含羟基的分子,并通过负离子电喷雾电离傅里叶变换离子回旋共振质谱(FT-ICR MS)进行分析。这些馏分还进行了加速老化实验,并通过FT-ICR MS和体积粘度测量进行表征。由于数据的复杂性,从超高分辨率数据中提取有洞察力的信息以帮助预测生物油的升级方法和不稳定性行为具有挑战性。为了解决这个问题,使用了一种无监督学习技术——k均值聚类分析,在(/, /)化学空间内以接近欧几里得距离的方式对分子组成进行半定量。k均值分析与化学选择性反应结果的结合使得能够推断出不同样品中独特的羟基官能团。我们的结果表明,己烷可溶馏分包含许多含有伯醇和仲醇的分子,而水溶性馏分则显示出不同组的含氧化合物类别,聚集在类碳水化合物和热解腐殖质类物质附近。尽管水溶性馏分的氧含量很高,但在老化过程中其粘度变化最小。相比之下,在水不溶性物质中观察到粘度显著增加,特别是低分子量和高分子量木质素馏分(分别为LMWL和HMWL)。在这两个馏分中,高分子量木质素馏分在仅4小时的加速老化后粘度增加最高。我们的结果表明,这种老化行为归因于含有酚基分子组成数量增加。因此,高分子量木质素馏分中的化学成分是加速老化条件下生物油粘度变化的主要贡献者。这突出了氧官能团在生物油老化中的关键作用,表明仅高氧含量不一定与粘度增加相关。与其他基于范克雷维伦组成空间内受限分子位置的生物油分类方法不同,k均值聚类可以识别每个样品独特化学指纹所固有的超高分辨率数据中的模式。