Primary structure analysis of natural nanofibers by the pixel-resolved optical retardation distribution measurements

2022 Fiscal Year Final Research Report

• Project/Area Number: 21K19146
• Research Category: Grant-in-Aid for Challenging Research (Exploratory)
• Allocation Type: Multi-year Fund
• Review Section: Medium-sized Section 40:Forestry and forest products science, applied aquatic science, and related fields
• Research Institution: Tokyo University of Science (2022); Osaka University (2021)
• Principal Investigator: Uetani Kojiro 東京理科大学, 工学部工業化学科, 講師 (20733306)
• Co-Investigator(Kenkyū-buntansha): 宇都 卓也 宮崎大学, 工学部, 准教授 (60749084) ; 古賀 大尚 大阪大学, 産業科学研究所, 准教授 (30634539)
• Project Period (FY): 2021-07-09 – 2023-03-31
• Keywords: ナノ繊維 / 光学位相差 / 構造解析

Outline of Final Research Achievements

The objective of this study was to extend the analytical technique of pixel-resolved optical retardation distribution used to characterize wood pulp defibers on the millimeter to microscale to the nanoscale and to evaluate and demonstrate its applicability to the structural analysis of highly nanofibrillated cellulose nanofibers (CNFs). CNFs of different thickness and morphology were individually extracted from various cellulose-synthesizing organisms, and their microscopic structures, such as fiber width and crystallite size, were quantitatively evaluated by a transmission electron microscopy and X-ray diffraction measurements. The optical retardation distribution of each CNF suspension was analyzed and compared with the measured fiber widths. Then, the optical retardation of the CNF suspension was found to be highly correlated not only to the fiber width alone, but also to a composite parameter including the average curvature of the fibers.

Free Research Field

木質科学

Academic Significance and Societal Importance of the Research Achievements

セルロースナノファイバー（CNF）の繊維幅や長さ、形態などの一次構造は、高分子で言う分子量やコンフォメーションに相当する最も基礎的な材料学的情報と言える。CNFの寸法は、製造方法に応じて広い分布やばらつきを持ち、高度に解繊されると高い形態異方性によって一次構造とその分布を定量することが技術的に困難であった。本研究の手法は、従来同時解析が困難であった繊維幅と繊維形態の複合パラメータを検出するため、新しいCNF構造を定義可能となり、より精密な材料開発と機能解明に有用であると考えられる。