NF membranes sit between Reverse Osmosis (RO) and Ultrafiltration (UF) in terms of pore size. This allows them to remove a wider range of contaminants than UF but not as much as RO. However, current NF membranes aren't perfect at selectively removing certain contaminants while allowing desirable minerals to pass through. In optimizing NF membrane selectivity, could machine learning algorithms be used to design or predict ideal pore structures or surface functionalities for NF membranes?
@ Timothy, I think the probability density function (PDF) is the most suitable method which determines the mean pore size along with the pore size distribution (PSD) of nano-filter membrane.
@ Tarafdar thank you for your response. How about using a combined approach? Which will involve using the PDF along with data from other characterization techniques and incorporating them all into the ML model for a more comprehensive understanding of pore structure and its impact on selectivity.
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