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Abstract Over 8 billion tons of plastic have been produced to date, and a 100% reclamation recycling strategy is not foreseeable. This review summarizes how the mechanochemistry of polymers may contribute to a sustainable polymer future by controlling the degradation not only of de novo developed designer polymers but also of plastics in existing waste streams. The historical development of polymer mechanochemistry is presented while highlighting current examples of mechanochemically induced polymer degradation. Additionally, theoretical and computational frameworks are discussed that may lead to the discovery and better understanding of new mechanochemical reactions in the future. This review takes into account technical and engineering perspectives converging the fields of trituration and polymer mechanochemistry with a particular focus on the fate of commodity polymers and potential technologies to monitor mechanochemical reactions while they occur. Therefore, a unique perspective of multiple communities is presented, highlighting the need for future transdisciplinary research to tackle the high-leverage parameters governing an eventually successful mechanochemical degradation approach for a circular economy. 

Abstract There is a need for developing reliable models for water and solute transport in graphene oxide (GO) membranes for advancing their emerging industrial water processing applications. In this direction, we develop predictive transport models for GO and reduced‐GO (rGO) membranes over a wide solute concentration range (0.01–0.5 M) and compositions, based on the extended Nernst–Planck transport equations, Donnan equilibrium condition, and solute adsorption models. Some model parameters are obtained by fitting experimental permeation data for water and unary (single‐component) aqueous solutions. The model is validated by predicting experimental permeation behavior in binary solutions, which display very different characteristics. Sensitivity analysis of salt rejections as a function of membrane design parameters (pore size and membrane charge density) allows us to infer design targets to achieve high salt rejections. Such models will be useful in accelerating structure‐separation property relationships of GO membranes and for separation process design and optimization. 

Sustainable and economically viable plastic recycling methodologies are vital for addressing the increasing environmental consequences of single-use plastics. In this study, we evaluate the plastic waste management value for the state of Georgia, US and investigate the potential of introducing novel depolymerization methods within the network. An equation-based formulation is developed to identify the optimum supply-chain design given the geographic location of existing facilities. Chemical recycling technologies that have received increasing attention are evaluated as candidate technologies to be integrated within the network. The optimum supply-chain design is selected based on environmental and economic objectives. The designed network of pathways uses a mix of different technologies (chemical and mechanical recycling) in a way that are both economically environmentally sound. 

Feasibility of mechanochemical depolymerization of commodity poly(olefin)s in a ball mill reactor is assessed using thermodynamic data. 

Given the heightened global awareness and attention to the negative externalities of plastics use, many state and local governments are considering legislation that will limit single-use plastics for consumers and retailers under extended producer responsibility laws. Considering the growing momentum of these climate regulations globally, there is a need for reliable and cost-effective measures of the public response to this rulemaking for inference and prediction. Automated computational approaches such as generative AI could enable real-time discovery of consumer preferences for regulations but have yet to see broad adoption in this domain due to concerns about evaluation costs and reliability across large-scale social data. In this study, we leveraged the zero and few-shot learning capabilities of GPT-4 to classify public sentiment towards regulations with increasing complexity in expert prompting. With a zero-shot approach, we achieved a 92% F1 score (s.d. 1%) and 91% accuracy (s.d. 1%), which resulted in three orders of magnitude lower research evaluation cost at 0.138 pennies per observation. We then use this model to analyze 5,132 tweets related to the policy process of the California SB-54 bill, which mandates user fees and limits plastic packaging. The policy study reveals a 12.4% increase in opposing public sentiment immediately after the bill was enacted with no significant changes earlier in the policy process. These findings shed light on the dynamics of public engagement with lower cost models for research evaluation. We find that public opposition to single-use plastics regulation becomes evident in social data only when a bill is effectively enacted. 

Efficient and sustainable chemical recycling pathways for plastics are vital for addressing the negative environmental impacts associated with their end-of-life management. Mechanochemical depolymerization in ball mill reactors is a new promising route to achieve solid-state conversion of polymers to monomers, without the need for additional solvents. Physics-based models that accurately describe the reactor system are necessary for process design, scaling up, and reducing energy consumption. Motivated by this, a Discrete Element Method (DEM) model is developed to investigate the ball milling process at laboratory and industrial scales. The lab-scale model is calibrated and validated with data extracted from videos using computer vision tools. Finally, scaled-up ball mill designs capable of depolymerizing varying feeds of PET waste were simulated, and their capital and operating costs are estimated to assess the economic potential of this route.