Investigating Permselectivity in PVDF Mixed Matrix Membranes Using Experimental Optimization, Machine Learning Segmentation, and Statistical Forecasting.

This research examines the correlation between interfacial characteristics and membrane distillation (MD) performance of copper oxide (Cu) nanoparticle-decorated electrospun carbon nanofibers (CNFs) polyvinylidene fluoride (PVDF) mixed matrix membranes. The membranes were fabricated by a bottom-up phase inversion method to incorporate a range of concentrations of CNF and Cu + CNF particles in the polymer matrix to tune the porosity, crystallinity, and wettability of the membranes. The resultant membranes were tested for their application in desalination by comparing the water vapor transport and salt rejection rates in the presence of Cu and CNF. Our results demonstrated a 64% increase in water vapor flux and a salt rejection rate of over 99.8% with just 1 wt % loading of Cu + CNF in the PVDF matrix. This was attributed to enhanced chemical heterogeneity, porosity, hydrophobicity, and crystallinity that was confirmed by electron microscopy, tensiometry, and scattering techniques. 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 trained on MD experimental data obtained for various membranes to forecast the membrane performance over an extended duration.