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The transition to a circular economy (CE) requires agents in circular supply chain (SC) networks to take a variety of different initiatives, many of which are dynamic in nature. We use a system dynamics (SD)-based approach to develop a generic framework for dynamic modeling of CE networks and propose a prototypical circular SC network by combining dynamic models for five actors: a manufacturer, consumer, material recovery facility (MRF), recycling facility, and the Earth. We apply this framework to the supply chain for Polyethylene Terephthalate (PET) plastic packaging by considering different scenarios over a 65-year time horizon in the US. We include both slow-down-the-loop initiatives (i.e., those that extend product use time through demand reduction or reuse) and close-the-loop initiatives (i.e., those that reintroduce product to the supply chain through recycling) by the consumer, as well as sorting and recycling capacity expansion. We find that, given the current recycling infrastructure in the U.S., slow-down-the-loop initiatives are more effective than close-the-loop initiatives for improving circularity and minimizing environmental impact. However, combining the two initiatives eliminates the need for capacity expansion and leads to the highest circularity in the shortest amount of time. 

Transition-metal ions regularly undergo charge transfer (CT) by directly interacting with electrodes, and this CT governs the performance of devices for numerous applications like energy storage and catalysis. These CT reactions are deemed inner sphere because they involve direct formation of a chemical bond between the electrode and the metal ion. Predicting inner-sphere CT kinetics on electrodes using simple physicochemical descriptors would aid the design of electrochemical systems with improved kinetics. Herein, we report that the average energy of the d electrons (i.e., d-band center) of a transition-metal electrode rationalizes the kinetic trends of inner-sphere CT of transition-metal ions. We demonstrate that V2+/V3+, an important redox reaction for flow batteries, is an inner-sphere reaction and that the kinetic parameters correlate with the adsorption strength of the vanadium intermediate on Au, Ag, Cu, Bi, and W electrodes, with W being the most active electrode reported to date. We show that the adsorption strength of the vanadium intermediate linearly correlates with the d-band center such that the d-band center serves as a simple descriptor for the V2+/V3+ kinetics. We extract kinetic data from the literature for four other inner-sphere CT reactions of metal ions involving Cr-, Fe-, and Co-based complexes to show that the d-band center also linearly correlates with kinetic trends for these systems. The d-band center of the electrode is a general descriptor for heterogeneous inner-sphere CT because it correlates with the adsorption strength of the metal-ion intermediate. The d-band center descriptor is analogous to the d-electron configuration of metal ions serving as a descriptor for homogeneous inner-sphere CT because the d-electron configuration controls bond strengths of intermediate metal-ion complexes. 

Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.