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We report a transformative epoxy system with a microalgae-derived bio-binder from hydrothermal liquefaction processing (HTL). The obtained bio-binder not only served as a curing agent for conventional epoxy resin (e.g., EPON 862), but also acted as a modifying agent to enhance the thermal and mechanical properties of the conventional epoxy resin. This game-changing epoxy/bio-binder system outperformed the conventional epoxy/hardener system in thermal stability and mechanical properties. Compared to the commercial EPON 862/EPIKURE W epoxy product, our epoxy/bio-binder system (35 wt.% bio-binder addition with respect to the epoxy) increased the temperature of 60% weight loss from 394 °C to 428 °C and the temperature of maximum decomposition rate from 382 °C to 413 °C, while the tensile, flexural, and impact performance of the cured epoxy improved in all cases by up to 64%. Our research could significantly impact the USD 38.2 billion global market of the epoxy-related industry by not only providing better thermal and mechanical performance of epoxy-based composite materials, but also simultaneously reducing the carbon footprint from the epoxy industry and relieving waste epoxy pollution. 

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We explore the properties of interferometric data from high-redshift 21 cm measurements using the Murchison Widefield Array (MWA). These data contain the redshifted 21 cm signal, contamination from continuum foreground sources, and radiometric noise. The 21 cm signal from the Epoch of Reionization (EoR) is expected to be highly Gaussian, which motivates the use of the power spectrum as an effective statistical tool for extracting astrophysical information. We find that foreground contamination introduces non-Gaussianity into the distribution of measurements and then use this information to separate Gaussian from the non-Gaussian signal. We present improved upper limits on the 21 cm EoR power spectrum from the MWA using a Gaussian component of the data, based on the existing analysis from C. D. Nunhokee et al. 2025. This is extracted as the best-fitting Gaussian to the measured data. Our best 2σ (thermal+sample variance) limit for 268 hr of data improves from (30.2 mK)2 to (23.0 mK)2 at z = 6.5 for the East–West polarization, and from (39.2 mK)2 to (21.7 mK)2 = 470 mK2 in North–South. The best limits at z = 6.8 (z = 7.0) improve to P < (25.9 mK)2 (P < (32.0 mK)2) and k = 0.18h Mpc‑1 (k = 0.21h Mpc‑1). Results are compared with realistic simulations, which indicate that leakage from foreground contamination is a source of the non-Gaussian behavior. 

This paper reports on the lessons learned from developing and deploying campus-wide large language model (LLM) services at Purdue University for generative AI (GenAI) applications in education and research. We present a frame- work for identifying an LLM solution suite and identify key considerations related to developing custom solutions. While the GenAI ecosystem continues to evolve, the framework is intended to provide a tool- and organization-agnostic approach to guide leaders in conversations and strategy for future work and collaboration in this emerging field.