Abstract:
Provided herein are copolymers and copolymer compositions that are both hydrophilic and oleophobic. The copolymers include structural units derived from a fluoroalkyl monomer and a zwitterionic monomer. It further relates to membranes formed by coating a porous substrate with the copolymeric compositions. The copolymeric coating imparts hydrophilicity and oleophobicity/oil-tolerance to the membranes. The uses of such membranes as microfiltration membrane or ultrafiltration membrane are also provided.
Abstract:
Provided herein are copolymers and copolymer compositions that are both hydrophilic and oleophobic. The copolymers include structural units derived from a fluoroalkyl monomer and a zwitterionic monomer. It further relates to membranes formed by coating a porous substrate with the copolymeric compositions. The copolymeric coating imparts hydrophilicity and oleophobicity/oil-tolerance to the membranes. The uses of such membranes as microfiltration membrane or ultrafiltration membrane are also provided.
Abstract:
According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a Hypothesis Generation Engine (HGE) to receive one or more property target values for a material; a memory for storing program instructions; an HGE processor, coupled to the memory, and in communication with the HGE, and operative to execute program instructions to: receive the one or more property target values for the material; analyze the one or more property target values as compared to one or more known values in a knowledge base; generate, based on the analysis, an initial set of hypothetical structures, wherein each hypothetical structure includes at least one property target value; execute a likelihood model for each candidate material to generate a likelihood probability for each hypothetical structure, wherein the likelihood probability is a measure of the likelihood that the hypothetical structure will have the target property value; convert each hypothetical structure into a natural language representation; execute an abduction kernel on the natural language representation with the at least one likelihood probability, to output at least one proposed structure that satisfies a likelihood threshold for having the property target value; and receive the output of the executed abduction kernel at a testing module to determine whether the output satisfies the property target values. Numerous other aspects are provided.