biostudies-literatureLiu XDivision of Chemical, Bioengineering, Environmental, and Transport SystemsUniversity of Notre DameDivision of Civil, Mechanical and Manufacturing Innovation59216-59233https://www.ebi.ac.uk/biostudies/studies/S-EPMC11534006biostudies-literatureUnknown16(43)Multifunctional, nanostructured membranes hold immense promise for overcoming permeability-selectivity trade-offs and enhancing membrane durability in challenging molecule separations. Following the fabrication of copolymer membranes, additive manufacturing technologies can introduce reactive inks onto substrates to modify pore wall chemistries. However, large-scale implementation is hindered by a lack of systematic optimization. This study addresses this challenge by elucidating the membrane functionalization mechanisms and optimal manufacturing conditions using a copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) "click" reaction. Leveraging a data science toolkit (e.g., nonlinear regression, uncertainty quantification, identifiability analyses, model selection, and design of experimACS applied materials & interfacesOptimization of Reactive Ink Formulation for Controlled Additive Manufacturing of Copolymer Membrane Functionalization.PMC11534006CMMI-1932206CBET-1941596Liu XPhillip WADowling AWOuimet JAHoffman JRXu JfalseOptimization of Reactive Ink Formulation for Controlled Additive Manufacturing of Copolymer Membrane Functionalization.Multifunctional, nanostructured membranes hold immense promise for overcoming permeability-selectivity trade-offs and enhancing membrane durability in challenging molecule separations. Following the fabrication of copolymer membranes, additive manufacturing technologies can introduce reactive inks onto substrates to modify pore wall chemistries. However, large-scale implementation is hindered by a lack of systematic optimization. This study addresses this challenge by elucidating the membrane functionalization mechanisms and optimal manufacturing conditions using a copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) "click" reaction. Leveraging a data science toolkit (e.g., nonlinear regression, uncertainty quantification, identifiability analyses, model selection, and design of experim2024-01-01T00:00:00Z2024 Oct2026-06-04T13:06:48.904Z2026-05-09T03:11:32.013ZS-EPMC115340063942564010.1021/acsami.4c11749