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Updated: Aug 5, 2026

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Scalable Syntheses of Graphene Oxide and Reduced Graphene Oxide using Cascade Design Oxidation and Highly Basic Reduction Reactions
Published on: July 3, 2025
From Atomic Channels to Deployable Membranes: A Design-Oriented Framework for Graphene Oxide Transport,
Awad Alzebair1, Didem Aydin2, İlkay Hilal Gübbük2
1Department of Biochemistry, Faculty of Sciences, Selcuk University, 42030 Konya, Türkiye.
Membranes
|July 27, 2026
Summary
Graphene oxide membranes offer improved selectivity over polymers. This review unifies transport models, corrects computational overestimations, and proposes a framework for designing next-generation graphene oxide membranes.
Area of Science:
- Materials Science and Engineering
- Chemical Engineering
- Computational Materials Science
Background:
- Graphene oxide (GO) membranes show promise for overcoming the permeability-selectivity limitations of conventional polymer membranes.
- Current challenges include incomplete mechanistic understanding and a lack of scalable, defect-controlled fabrication processes, hindering practical application.
- A comprehensive design framework is needed to integrate transport mechanisms, computational modeling, fabrication, and translational constraints.
Purpose of the Study:
- To synthesize and integrate transport mechanisms, computational modeling, fabrication, and translational constraints for graphene-based membrane architectures.
- To establish a comprehensive, design-oriented framework for advancing GO membrane technology.
- To address key challenges hindering the practical deployment of GO membranes.
Main Methods:
- Review and synthesis of existing literature on GO membrane transport mechanisms and fabrication.
- Quantitative parity analysis of molecular dynamics simulations against experimental data.
- Benchmarking of GO membrane performance across various applications against commercial standards.
- Proposal of a standardized hydraulic stability protocol.
Main Results:
- Evidence supports a three-regime transport model (viscous flow, hopping, solution-diffusion), with selectivity tunable via C/O ratio and interlayer chemistry.
- Classical molecular dynamics overestimates GO water permeance by 3-8x; machine learning force fields reduce this to 1.5-2x.
- Nuclear quantum effects are critical for proton transport but unresolved for water permeance in GO laminates.
- Performance benchmarking reveals heterogeneity in testing conditions; a 500-h hydraulic stability protocol is proposed.
Conclusions:
- A unified framework for GO membrane design is presented, integrating transport phenomena, computational insights, and fabrication considerations.
- The review provides a structured basis for developing next-generation GO membranes by addressing key mechanistic and computational discrepancies.
- Standardized testing protocols are crucial for reliable cross-laboratory comparisons and technology advancement.
Keywords:
antifoulinggraphene oxide membranesinterlayer spacingion sievingmachine-learning force fieldsscalable fabricationtransport mechanismsMore Related Videos
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