Ultralow Thermal Conductivity Hierarchically Porous PVDF–rGO Membranes for High-Efficiency Thermal Desalination

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Authors: Waqas Alam, Md Ashiqur Rahman, Arnab Paul, Eman Alkhedher, Tian Li, David M. Warsinger
Cited by: 1
References: 67

Abstract

Reducing heat losses is essential for improving energy efficiency of thermally driven separation processes such as membrane distillation (MD). Membrane material enhancements offer huge potential in this regard. Conventional MD membranes typically suffer from relatively high thermal conductivity, which limits both water production rates and energy performance. In this work, we discuss the fabrication of polyvinylidene fluoride–reduced graphene oxide (PVDF–rGO) mixed matrix membrane using rGO microgranules that promote a hierarchical pore structure. The membrane’s combination of nanoscale and microscale pores, together with the phonon-scattering effects introduced by the dispersed rGO microgranules, result in an ultralow effective thermal conductivity of 0.04 W/mK which is among the lowest reported for MD membranes. The membrane, fabricated via phase inversion, exhibits high porosity (91%) and favorable morphology for vapor transport. Experimental testing in air gap MD (AGMD) achieved a permeate flux of 5.44 LMH at 80 °C, while comprehensive modeling of both AGMD and conductive gap MD (CGMD) configurations revealed that lowering thermal conductivity directly enhances not only flux but also energy efficiency, with CGMD showing particularly strong sensitivity. The gained output ratio (GOR) versus flux analysis further establishes low-conductivity membranes as a key design paradigm for advancing MD toward sustainable and scalable water treatment. By demonstrating a pathway to suppress conductive heat losses through low membrane thermal conductivity, this work establishes a new benchmark for membrane design in high-efficiency desalination technologies.

Fields of Study & Subjects

Membrane Separation Technologies Solar-Powered Water Purification Methods Adsorption and Cooling Systems

Publication details

Journal / source
SSRN Electronic Journal
Publisher
Elsevier BV
Publication date
1 January 2026
Document type
Preprint