4th Annual Edward Brush Green Chemistry Awards Symposium – Part 2

To enable sustainable chemical design, there is a need for the capability to predict the degradation potential of proposed structures not yet produced and for which experimental data are unavailable. Hydrolysis is a key process impacting contaminant fate, especially in aqueous and biological systems. This work develops WaterDRoP (Water Degradation Rate of Pollutants), a machine learning model to predict the rate of hydrolysis from chemical structure in environmentally relevant settings (pH 7 and 25 degrees C). The two-stage model classifies a compound as stable (half-life > 1 year) or unstable (half-life < 1 year) and estimates the numeric half-life of unstable compounds. Each stage is a pretrained neural network fine-tuned using 808 experimental hydrolysis rates collected from reports and databases. WaterDRoP compares favorably to existing models for hydrolysis rate prediction (EPI Suite, Hydrolysis QSAR, QSAR Toolbox) in terms of applicability, stability classification (F1 score), and rate prediction of unstable compounds (RMSE, MAE, R²). Atom-level attribution scores obtained through Shapley Additive Explanations (SHAP) analysis, illustrating the substructures identified by the model as most relevant for anticipating hydrolysis, were compared against proposed hydrolysis mechanisms from the literature. This in silico hydrolysis rate estimation tool and curated training dataset are made openly available.

Eutrophication caused by excess inorganic phosphate (Pi) severely reduces water quality, threatens aquatic ecosystems, and poses serious environmental and economic challenges. This work presents a sustainable and integrated approach to both monitoring and tackling Pi pollution in water. First, a portable, sensitive, and cost-effective Pi sensor is developed, addressing challenges such as reliance on expensive lab equipment, trained personnel, complex reagent synthesis, time-consuming procedures, and limited reagent shelf life. The sensor combines 3D-printed platforms with fluorophore design and utilizes a smartphone integrated with machine learning as a portable detection tool. Second, Pi removal is addressed through the development of a sustainable adsorbent fabricated by upcycling waste printed papers. This adsorbent presents a scalable, cost-effective, and environmentally friendly solution with high adsorption efficiency. Together, this combined sensing and remediation strategy offers a green and practical solution for managing Pi pollution and mitigating eutrophication in water systems.

Spent coffee grounds (SCG), an abundant lignocellulosic biomass waste, offer potential for sustainable aviation fuels (SAF) production due to their major components of carbohydrates (50%), lignin (30%), and lipids (8%), with efficient fractionation being the main challenge. This study presents an alcohol-based solvolysis strategy for deconstructing SCG into SAF-relevant fractions while enabling the use of ethanol and glycerol as endogenous co-solvents. Methanol solvolysis achieved high retentions of glucan (99.5% at 180 °C and 98.0% at 200 °C), mannan (99.0% at 180 °C and 79.0% at 200 °C), and galactan (93.0% at 180 °C and 78.0% at 200 °C), with delipidification (>95.0%) and delignification (>52.0%). The ethanol/glycerol (1:1 v/v) cosolvent at 200 °C demonstrated comparable glucan and mannan retentions of 88.0% and 76.0%, with delignification (67.0%) and delipidification (93.0%). All tested solvent systems yielded a combined 61-66 wt% of SAF-convertible lipid, lignin, and carbohydrate fractions, each suitable for direct integration into established catalytic upgrading pathways. Despite the comparable fractionation efficiency, the endogenous solvent systems reduced autogenous pressure to 15.6 bar, 59% lower than neat methanol solvolysis, enabling safer, more energy-efficient biorefinery operation. Techno economic analysis shows superior performance for cosolvent systems, with ethanol/glycerol achieving a minimum selling price (MSP) of $1.43/kg and methanol/glycerol achieving $1.64/kg, which are 30% and 20% lower than methanol solvolysis ($2.05/kg) while delivering enhanced solvent circularity and operational safety.

The transition toward a more sustainable chemical enterprise requires not only technological innovation but also a transformation in how chemistry is taught, practiced, and experienced by learners across educational levels. In this presentation, I will describe a multi-layered model for integrating sustainability into chemical education that combines research-driven learning, student leadership, and community-engaged outreach. Central to this effort is the UConn Green Chemistry Initiative (GCI), a student-led program that has developed peer-to-peer mentorship, undergraduate green chemistry courses, and campus-wide programming that aligns chemical education with the United Nations Sustainable Development Goals (SDGs).
Complementing these curricular and co-curricular efforts, our research group has developed a high-school outreach program centered on the sustainable continuous-flow synthesis of artemisinin using natural photocatalysts. Through a Remote Research Network (RRN), students can participate in authentic, real-time experimentation even when they lack access to specialized instrumentation, thereby democratizing access to modern chemical technologies. Participants learn not only core chemical concepts but also systems thinking, lifecycle considerations, and the societal relevance of sustainable manufacturing.
This integrated approach has generated measurable educational impact: high-school participants have gone on to pursue college degrees and careers in sustainable science, while undergraduate leaders from GCI have translated their training into professional roles — including in UConn’s Office of Sustainability — where they now apply chemical knowledge to institutional sustainability initiatives. Graduate and undergraduate mentees have similarly incorporated green chemistry principles into their research and teaching practices.