(161u) Application of Low-Field Nuclear Magnetic Resonance (LF-NMR) for the Mesh Structure Characterization of Poly(Ethylene Glycol) Derivative Hydrogels

Hydrogels are composed of hydrophilic polymeric structures and are widely used in biomedical engineering applications, such as drug delivery and tissue engineering. Valuable features that enable such versatility include a large swelling capacity and a biocompatible nature. Poly(ethylene glycol) or PEG hydrogels, have been receiving remarkable attention for their successful applications and promising future within the field. Notably, the complexity of the hydrogel network at microscopic scales, regulates properties of the system, such as permeability, elasticity, and strength. In this investigation, Low-Field Nuclear Magnetic Resonance (LF-NMR) relaxometry was applied to the characterization of the network structure of poly(ethylene glycol diacrylate) (PEGDA) and four-arm poly(ethylene glycol) (Tetra-PEG). The differences between PEGDA and Tetra-PEG were elucidated and compared to mechanical data collected in the lab and scattering data from literature.

The assessment of hydrogel pore volume and pore size distribution is often relegated to scanning electron microscopy (SEM) of dried or freeze-dried hydrogel samples, although it is well-known that drying conditions influence the final structure. Therefore, the porosity of these dried samples is, likely, not representative of the porosity in the native state. Furthermore, SEM and related techniques cannot probe length scales below approximately 50 nm with adequate resolution. Small angle X-ray scattering (SAXS) and small-angle neutron scattering (SANS) have been successfully applied to extract qualitative and quantitative details of hydrogels network, but access to the equipment is limited and not viable for routine characterization of gels. In contrast, NMR relaxometry is an experimentally simple method to obtain insightful information of pore structures by correlating relaxation rates with mesh sizes. In NMR experiments, a selective group of nuclei, e.g. ¹H, align with an externally applied magnetic field. This magnetization may be perturbed by a radio frequency pulse (90° or 180°), and the rates of equilibration in the longitudinal axis and transverse plane are characterized by the time constants T₁ and T₂, respectively. The interpretation of NMR relaxometry output is dependent upon pore surface / fluid interactions, which enhance relaxation rates, as well as, diffusivity of the imbibed fluid. With minimum sample preparation and relatively low analysis time, NMR can provide relevant data which may be correlated to the physical properties of hydrogels at different states.

In the present study, transverse NMR relaxation rates have been measured for crosslinked PEGDA gels at varying water concentration and molecular weight using a benchtop low-field NMR instrument, operated at 0.47 T and 20 MHz (Bruker, Germany). The Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence was applied to obtain the T₂ data. Predominantly, unimodal distributions of the relaxation times of pore water were observed upon the use of inverse Laplace transform, and the rates were found to be linearly proportional to weight percent of the polymer. In separate solute diffusion analyses, the fiber radius of PEG was determined and in conjuncture with the relaxation rate distribution for each system, the surface relaxivities were calculated. The mesh size distributions obtained via the network modeling are in good quantitative agreement with previously reported SAXS data of hydrogels with identical composition and molecular weight. The data set also matches the results obtained from mechanical analyses performed in the lab. These results demonstrate the potential of NMR as a tool to study mesh sizes and network homogeneities of hydrogels. Furthermore, in comparison with previous approaches described in literature, the model developed in this study has been optimized to reduce the number of assumptions and has shown to be adaptable to multiple hydrogel structures.