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Abstract The recovery of persistent microplastics (MPs) from aquatic systems is a pressing environmental issue that is hard to address by conventional methods such as filtration or centrifugation. Strategies are investigated for the design of the self‐dispersal and collection cycle of a class of active microcleaners comprising soft dendritic colloids (SDCs). The SDCs are made of chitosan and have a hierarchical fibrillar structure which enables adhesive collection of MP particles through van der Waals attraction. Wide‐scale dispersion is achieved by agglomerating the SDCs into larger supraparticles, which self‐propel on the water surface by the Marangoni effect driven by small amounts of organic oil. The cycle of propulsion, rehydration, and sinking enables efficient MP capture by the sedimenting SDCs. Further, magnesium hydrolysis reaction timed by encapsulation leads to vertical bubble propulsion and collection of the SDC‐MPs aggregates on the surface. Overall, the results present a proof of concept of the potential of comprehensive MP cleanup methods based on sustainable self‐dispersing microcleaners. 

Abstract Poly(ethylene terephthalate) (PET) is a highly recyclable plastic that has been extensively used and manufactured. Like other plastics, PET resists natural degradation, thus accumulating in the environment. Several recycling strategies have been applied to PET, but these tend to result in downcycled products that eventually end up in landfills. This accumulation of landfilled PET waste contributes to the formation of microplastics, which pose a serious threat to marine life and ecosystems, and potentially to human health. To address this issue, our project leveraged synthetic biology to develop a whole‐cell biocatalyst capable of depolymerizing PET in seawater environments by using the fast‐growing, nonpathogenic, moderate halophileVibrio natriegens. By leveraging a two‐enzyme system—comprising a chimera ofIsPETase andIsMHETase fromIdeonella sakaiensis—displayed onV. natriegens, we constructed whole‐cell catalysts that depolymerize PET and convert it into its monomers in salt‐containing media and at a temperature of 30°C. 

Abstract Identifying and removing microplastics (MPs) from the environment is a global challenge. This study explores how the colloidal fraction of MPs assemble into distinct 2D patterns at aqueous interfaces of liquid crystal (LC) films with the goal of developing surface‐sensitive methods for identifying MPs. Polyethylene (PE) and polystyrene (PS) microparticles are measured to exhibit distinct aggregation patterns, with addition of anionic surfactant amplifying differences in PS/PE aggregation patterns: PS changes from a linear chain‐like morphology to a singly dispersed state with increasing surfactant concentration whereas PE forms dense clusters at all surfactant concentrations. Statistical analysis of assembly patterns using deep learning image recognition models yields accurate classification, with feature importance analysis confirming that dense, multibranched assemblies are unique features of PE relative to PS. Microscopic characterization of LC ordering at the microparticle surfaces leads to predict LC‐mediated interactions (due to elastic strain) with a dipolar symmetry, a prediction consistent with the interfacial organization of PS but not PE. Further analysis leads to conclude that PE microparticles, due to their polycrystalline nature, possess rough surfaces that lead to weak LC elastic interactions and enhanced capillary forces. Overall, the results highlight the potential utility of LC interfaces for rapid identification of colloidal MPs based on their surface properties. 

Abstract It is challenging to find a conventional nanofabrication technique that can consistently produce soft polymeric matter of high surface area and nanoscale morphology in a way that is scalable, versatile, and easily tunable. Here, the capabilities of a universal method for fabricating diverse nano‐ and micro‐scale morphologies based on polymer precipitation templated by the fluid streamlines in multiphasic flow are explored. It is shown that while the procedure is operationally simple, various combinations of its intertwined mechanisms can controllably and reproducibly lead to the formation of an extraordinary wide range of colloidal morphologies. By systematically investigating the process conditions, 12 distinct classes of polymer micro‐ and nano‐structures including particles, rods, ribbons, nanosheets, and soft dendritic colloids (dendricolloids) are identified. The outcomes are interpreted by delineating the physical processes into three stages: hydrodynamic shear, capillary and mechanical breakup, and polymer precipitation rate. The insights into the underlying fundamental mechanisms provide guidance toward developing a versatile and scalable nanofabrication platform. It is verified that the liquid shear‐based technique is versatile and works well with many chemically diverse polymers and biopolymers, showing potential as a universal tool for simple and scalable nanofabrication of many morphologically distinct soft matter classes. 

Abstract Environmental and health risks posed by microplastics (MPs) have spurred numerous studies to better understand MPs' properties and behavior. Yet, we still lack a comprehensive understanding due to MP's heterogeneity in properties and complexity of plastic property evolution during aging processes. There is an urgent need to thoroughly understand the properties and behavior of MPs as there is increasing evidence of MPs' adverse health and environmental effects. In this perspective, we propose an integrated chemical engineering approach to improve our understanding of MPs. The approach merges artificial intelligence, theoretical methods, and experimental techniques to integrate existing data into models of MPs, investigate unknown features of MPs, and identify future areas of research. The breadth of chemical engineering, which spans biological, computational, and materials sciences, makes it well‐suited to comprehensively characterize MPs. Ultimately, this perspective charts a path for cross‐disciplinary collaborative research in chemical engineering to address the issue of MP pollution. 

Abstract A highly controllable and scalable process for fabrication of large amounts of concentrated lignin nanoparticles (LNPs) is reported. These lignin core nanoparticles are formed through flash nanoprecipitation, however, scaling up of the fabrication process requires fundamental understanding of their operational formation mechanism and surface properties. It is shown how a semicontinuous synthesis system with a recirculation loop makes it possible to produce flash precipitated lignin nanoparticles in large amounts for practical applications. The roles of the process parameters, including flow rates and lignin concentration, are investigated and analyzed. The results indicate that the LNPs are formed by a process of continuous burst nucleation at the point of mixing without diffusive growth, which yields nanoparticles of highly uniform size following a modified LaMer nucleation and growth mechanism. This mechanism makes possible facile process control and scale‐up. Effective control of the resulting nanoparticle size is achieved through the initial concentration of lignin in the injected solution. The impressive capability to produce suspensions of any predesigned multimodal distribution is demonstrated. The resulting nanofabrication technique can produce large volumes of concentrated LNP suspensions of high stability and tightly controlled size distributions for biological or agricultural applications. 

Methods are needed to mitigate microplastic (MP) pollution to minimize their harm to the environment and human health. Given the ability of polypeptides to adsorb strongly to materials of micro- or nanometer size, plastic-binding peptides (PBPs) could help create bio-based tools for detecting, filtering, or degrading MNP pollution. However, the development of such tools is prevented by the lack of PBPs. In this work, we discover and evaluate PBPs for several common plastics by combining biophysical modeling, molecular dynamics (MD), quantum computing, and reinforcement learning. We frame peptide affinity for a given plastic through a Potts model that is a function of the amino acid sequence and then search for the amino acid sequences with the greatest predicted affinity using quantum annealing. We also use proximal policy optimization to find PBPs with a broader range of physicochemical properties, such as isoelectric point or solubility. Evaluation of the discovered PBPs in MD simulations demonstrates that the peptides have high affinity for two of the plastics: polyethylene and polypropylene. We conclude by describing how our computational approach could be paired with experimental approaches to create a nexus for designing and optimizing peptide-based tools that aid the detection, capture, or biodegradation of MPs. We thus hope that this study will aid in the fight against MP pollution. 

De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics—a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing. Hybrid quantum-classical methods can leverage complementary strengths of near-term quantum algorithms and classical techniques for complex tasks like peptide design. This work introduces a hybrid quantum-classical generative framework for designing plastic-binding peptides combining variational quantum circuits with a variational autoencoder network. We demonstrate the framework’s effectiveness in generating peptide candidates, evaluate its efficiency for property-oriented design, and validate the candidates with molecular dynamics simulations. This quantum computing–based approach could accelerate the development of biomolecular tools for environmental and biomedical applications while advancing the study of biomolecular systems through quantum technologies.