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Sustainable sanitation and resource recovery technologies are needed to address rapid environmental and socioeconomic changes. Research prioritization is critical to expedite the development and deployment of such technologies across their vast system space (e.g., technology choices, design and operating decisions). In this study, we introduce QSDsan - an open-source tool written in Python (under the object-oriented programming paradigm) and developed for the quantitative sustainable design (QSD) of sanitation and resource recovery systems. As an integrated platform for system design, process modeling and simulation, techno-economic analysis (TEA), and life cycle assessment (LCA), QSDsan can be used to enumerate and investigate the opportunity space for emerging technologies under uncertainty, while considering contextual parameters that are critical to technology deployment. We illustrate the core capabilities of QSDsan through two distinct examples: (i) evaluation of a complete sanitation value chain that compares three alternative systems; and (ii) dynamic simulation of the wastewater treatment plant described in the benchmark simulation model no. 1 (BSM1). Through these examples, we show the utility of QSDsan to automate design, enable flexible process modeling, achieve rapid and reproducible simulations, and to perform advanced statistical analyses with integrated visualization. We strive to make QSDsan a community-led platform with online documentation, tutorials (explanatory notes, executable scripts, and video demonstrations), and a growing ecosystem of supporting packages (e.g., DMsan for decision-making). This platform can be freely accessed, used, and expanded by researchers, practitioners, and the public alike, ultimately contributing to the advancement of safe and affordable sanitation technologies around the globe.

In search of sustainable approaches to plastics production, many efforts have been made to engineer microbial conversions of renewable feedstock to short-chain (C2-C8) bifunctional polymer precursors (e.g., succinic acid, cadaverine, 1,4-butanediol). Less attention has been given to medium-chain (C12-C14) monomers such as ω-hydroxy fatty acids (ω-OHFAs) and α,ω-dicarboxylic acids (α,ω-DCAs), which are precursors to high performance polyesters and polyamides. Here we engineer a complete microbial conversion of glucose to C12 and C14 ω-OHFAs and α,ω-DCAs, with precise control of product chain length. Using an expanded bioinformatics approach, we screen a wide range of enzymes across phyla to identify combinations that yield complete conversion of intermediates to product α,ω-DCAs. Finally, through optimization of culture conditions, we enhance production titer of C12 α,ω-DCA to nearly 600 mg/L. Our results indicate potential for this microbial factory to enable commercially relevant, renewable production of C12 α,ω-DCA-a valuable precursor to the high-performance plastic, nylon-6,12.