Poster

P5.10 – Automated Optimization of Flow-Focusing Parameters for High Thermal Conductivity of Nanocellulose Filaments

Jia Xin Peng

The University of Tokyo

Co-author(s):
Kazuho Daicho, The University of Tokyo
Jimpei Chida, The University of Tokyo
Eustache Westphal, University of California, Davis
Yaerim Lee, The University of Tokyo
Junichiro Shiomi, The University of Tokyo

Achieving a circular economy necessitates the transition to sustainable, green materials. Cellulose-based materials, long utilized in papermaking and textiles, hold great potential owing to their exceptional mechanical strength, lightweight nature, and biocompatibility. Cellulose’s properties can be finely tuned by controlling its structure at the micro- and nanoscale, thereby enabling superior performance across diverse applications.Cellulose filaments can be fabricated from cellulose nanofibrils (CNFs) using flow-focusing techniques [1], which have demonstrated efficacy in improving the uniformity of CNF packing in filaments as well as enhancing thermal conductivity [2]. The flow-focusing process can be tailored by modifying the design of the microfluidic device, gelation and stretching conditions, and applying gradients of pH, salt concentration, and flow within channels. However, CNFs sourced from different origins—such as wood pulp, tunicate, or bacterial cellulose—exhibit significant variations in their morphological, rheological, and chemical properties, necessitating the optimization of process parameters for enhanced thermal conductivity.In this study, an autonomous system was developed to optimize the flow-focusing process parameters for spinning cellulose nanofibers and characterizing their properties. Programmable syringe pumps were employed to precisely control flow rates. The alignment of cellulose during the flow-focusing process was evaluated using birefringence measurements, while the wet filament diameter was monitored using high-magnification imaging. Wet filament diameter was found to be inversely correlated with thermal conductivity as a smaller diameter results from better alignment and higher packing. Bayesian optimization was employed to identify optimal process parameters for different CNF suspensions.

References:[1] Karl M. O. Håkansson, et al. Nature Communications, 5, 4018 (2014) [2] Guantong Wang, et al., Nano Lett., 22, 21, 8406–8412 (2022)

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