Abstract
<jats:p>Poly(ethylene glycol) diacrylate (PEGDA) is a widely used hydrogel across applications from biomedical to soft electronics due to its biocompatibility and tunable mechanical properties. However, this tunability also makes PEGDA highly sensitive to formulation, processing, handling, and testing, all of which lead to large scatter in reported mechanical properties. This work identifies factors governing these heterogeneities through a comprehensive structure-process-property analysis, with the aim of establishing a standardized reporting framework to support data-driven design strategies. The theoretical basis for heterogeneity was highlighted as arising from its chemistry and reaction kinetics, as reflected in its bottlebrush architecture, distinct from that of standard point-junction hydrogels. Manufacturing methods (bulk casting, additive manufacturing, and two-photon laser printing) were evaluated to identify critical process parameters influencing network formation. These included commonly understood factors such as PEGDA chemistry and choice of photoinitiator, and less-reported parameters such as oxygen exposure, choice of mold, and post-curing processes, that can significantly impact the outcome. Mechanical variability across the testing scale arises from these inherent heterogeneities, with additional factors, such as the testing scale (macroscale tension and compression to nano- and microscale indentation) and sample state, further influencing the outcome. Based on the above, the work proposed a unifying framework organized under five categories required for reporting and analyzing PEGDA outcomes: formulation and composition, initiator information, curing parameters, post-cure handling, and testing conditions. Such standardization, reported in a suitable database format, is demonstrated, including its application in workflows that use large language models (LLMs) for querying. Future work can incorporate the framework in training more advanced machine learning (ML) models for a data-driven design. Overall, the study establishes a data-driven strategy for PEGDA and other hydrogels for advancing research across different applications.</jats:p>