journal article·2020-11-13

Comb Architecture to Control the Selective Diffusivity of a Double Network Hydrogel

Ping Dong, Bradley J. Schott, A. Kristen Means, Melissa A. Grunlan

ACS Applied Polymer Materials· Volume 2· Issue 11
Publisher page

Abstract

Limiting the diffusion of small-sized substances (Dh < 5 nm) through hydrogels is particularly difficult to achieve. Herein, we demonstrate the utility of a comb architecture to systematically reduce the mesh size of a double network (DN) hydrogel to between ∼1 and 3 nm, without loss of hydration. Combs of varying charge, length, and concentration were introduced during the formation of the first network of the DN hydrogel based on thermoresponsive N-isopropylacrylamide and negatively charged 2-acrylamido-2-methylpropane sulfonic acid. The resulting hydrogels’ mesh sizes were characterized with a size-exclusion test. Negatively charged combs were the most effective in reducing mesh size, attributed to electrostatic repulsive forces with the DN. Due to a lack of interaction and attractive forces to the first network, respectively, neutral and particularly positively charged combs were less effective.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Hydrogels: synthesis, properties, applications · 3D Printing in Biomedical Research · Polymer Surface Interaction Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843-3120 United States

Department of Materials Science & Engineering, Texas A&M University, College Station, Texas 77843-3003 United States

Department of Chemistry, Texas A&M University, College Station, Texas 77843-3120 United States

Center for Remote Health Technologies Systems, Texas A&M University, College Station, Texas 77843-3120 United States

Funding

Robert J. Kleberg, Jr. and Helen C. Kleberg Foundation · Division of Engineering Education and Centers · NSF GRFP

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