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How to Compare the Ion Selectivity of Smart Nanopores/Membranes
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Ion selectivity is a fundamental feature for designing advanced nanopores/channel systems, for example, biosensors or selectively permeable membranes. Comparison between different studies is a way to find and design the nanopore/membrane with pronounced selectivity. However, there is a huge hurdle in comparing the ion selectivities between studies, resulting from different equations from diverse scopes of science. Here, the authors from
“
Addressing challenges in ion-selectivity characterization in nanopores
”
emphasized the misinterpretation of the traditionally used Nernst and Goldman–Hodgkin–Katz equations in the previous literature and suggested the use of uniform criteria to overcome this ambiguity. We highlight the potential future applications of using uniform criteria in describing ion selectivity, which is beneficial to developing a massive AI-based databank. This databank would be advantageous for predicting and designing the ion selectivity of nanopores/nanomaterials in question for various applications in biological and material sciences.
American Association for the Advancement of Science (AAAS)
Title: How to Compare the Ion Selectivity of Smart Nanopores/Membranes
Description:
Ion selectivity is a fundamental feature for designing advanced nanopores/channel systems, for example, biosensors or selectively permeable membranes.
Comparison between different studies is a way to find and design the nanopore/membrane with pronounced selectivity.
However, there is a huge hurdle in comparing the ion selectivities between studies, resulting from different equations from diverse scopes of science.
Here, the authors from
“
Addressing challenges in ion-selectivity characterization in nanopores
”
emphasized the misinterpretation of the traditionally used Nernst and Goldman–Hodgkin–Katz equations in the previous literature and suggested the use of uniform criteria to overcome this ambiguity.
We highlight the potential future applications of using uniform criteria in describing ion selectivity, which is beneficial to developing a massive AI-based databank.
This databank would be advantageous for predicting and designing the ion selectivity of nanopores/nanomaterials in question for various applications in biological and material sciences.
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