Session: 16-01-01: Government Agency Student Poster Competition

Paper Number: 149695

149695 - Investigation and Optimization of Water Vapor Permselectivity of Copper-Decorated Carbon Nanofiber/polyvinylidene Fluoride Mixed Matrix Membranes for Enhanced Desalination Efficiency 

Addressing the global freshwater crisis, a significant challenge for humanity, this research presents a promising solution through the desalination of seawater. Given that around 97% of Earth is covered with water, desalination emerges as a viable solution to meet the growing demand for accessible clean water. This is especially relevant considering the United Nations projection that two-thirds of the global population will experience water shortages. As a part of the 6th United Nations Sustainable Development Goal, the focus of this research is a membrane-based thermal desalination process known as Membrane Distillation (MD) for producing clean drinking water. The key to enhancing the efficiency and sustainability of the MD process lies in replacing traditional membranes with high-performing ones. This research presents a comprehensive study on the fabrication and performance of mixed matrix polyvinylidene fluoride (PVDF) membranes. These membranes, enhanced with electrospun carbon nanofibers (CNF) and copper-decorated carbon nanofibers (Cu+CNF) for membrane distillation, were successfully fabricated through phase inversion. This process incorporated a range of concentrations of CNF and Cu+CNF particles to tune the membrane properties. Changes in properties such as pore size, porosity, thickness, hydrophobicity, chemical heterogeneity, crystallinity, and thermal stability were confirmed using various techniques. Copper nanoparticle decorated CNFs increased the mean pore size of the mixed matrix membranes by 37.1% for 1 wt % loading and increased the static water contact angle from 87°to 97°. The Flory–Huggin’s interaction parameter calculated based on the DSC peaks for CNF/PVDF was ∼0.119 and ∼0.093 for the Cu + CNF/PVDF membrane. Both the mixed matrix membranes exhibited low (close to zero) Flory–Huggin’s interactions parameters, indicating good thermodynamic compatibility and stability between polymer and nanofiller. The integration of nanomaterials into the PVDF matrix serves as nucleation sites, promoting the crystallization of PVDF. The degree of crystallinity of PVDF membrane was around 0.37%, and with the integration of 1 wt % CNF and Cu + CNF, the degree of crystallinity increased to 0.39 and 0.4%. The membranes were also stable up to 400 °C making them suitable for MD and high temperature applications. When applied to MD for desalination of a 3.5 wt% NaCl solution, the membranes achieved a significant increase in water vapor flux (64%) and a high salt rejection rate (99.8%) with just 1 wt% loading of Cu+CNF in the PVDF matrix. This improvement is attributed to the enhanced chemical heterogeneity, porosity, hydrophobicity, and crystallinity of the membranes. A machine learning segmentation model was trained on electron microscopy images to obtain the spatial distribution of pores in the membrane. An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) statistical time series model was used to forecast the long-term performance of the mixed matrix membranes based on real-time experimental MD data. After studying the mixed matrix membrane stability for 108 hours, it was found that the CNF/PVDF membranes exhibited the highest water vapor flux and salt rejection over 108 hours, whereas Cu+CNF incorporated membranes exhibited the highest vapor flux with low stability. This research significantly advances science and engineering by developing innovative solutions for water desalination, improving the efficiency of the MD process, and providing a comprehensive study of the fabrication and performance of enhanced membranes. It also demonstrates the application of machine learning and time series analysis in predicting membrane performance and stability, offering valuable insights for potential commercial applications.

Presenting Author: Saketh Merugu The University of Toledo

Presenting Author Biography: I am a third-year PhD candidate in Mechanical Engineering at the University of Toledo, specializing in the application of nanomaterials to enhance desalination and boiling processes. My expertise spans heat and mass transfer, transport processes, nanotechnology, and thermal and fluid mechanics. I hold a master’s degree in chemical engineering from Michigan Technological University, where I focused on biofuels and enzymatic hydrolysis. My research aims to innovate sustainable solutions in energy and water treatment.

Authors:

Saketh Merugu The University of Toledo
Anju R Gupta University of Toledo