Synergistic Enzyme Mixtures to Realize Near-Complete Depolymerization in Biodegradable Polymer/Additive Blends

Exploring biodegradable alternatives: microorganism-mediated plastic degradation and environmental policies for sustainable plastic management

Plastics offer versatility, durability and low production costs, but they also pose environmental and health risks due to improper disposal, accumulation in water bodies, low recycling rates and temporal action that causes physicochemical changes in plastics and the release of toxic products to animal health and nature. Some microorganisms may play crucial roles in improving plastic waste management in the future. Cunningamella echinulata has been identified as a promising candidate that remains viable for long periods and produces a cutinase that is capable of degrading plastic. Other recent approaches involving the use of microorganisms are discussed in this review. However, there does not seem to be a single science that is efficient or most appropriate for solving the problem of plastic pollution on the planet at present. Regulations, especially the implementation of different laws that address the entire plastic cycle in different countries, such as Brazil, the USA, China and the European Union, play important roles in the management of this waste and can contribute to reducing this problem. In the context of the transversality of the information compiled here, the current limitations are discussed, and an effective plan is proposed to reduce plastic pollution. Although it is challenging, it involves implementing legislation, promoting sustainable alternatives, improving collection and recycling systems, encouraging reuse, supporting research and technological innovation, promoting corporate responsibility, collaborating globally, raising public awareness, optimizing waste management and, above all, continuously monitoring the progress of actions based on measurable metrics.

Degradable living plastics programmed by engineered spores

Plastics are widely used materials that pose an ecological challenge because their wastes are difficult to degrade. Embedding enzymes and biomachinery within polymers could enable the biodegradation and disposal of plastics. However, enzymes rarely function under conditions suitable for polymer processing. Here, we report degradable living plastics by harnessing synthetic biology and polymer engineering. We engineered Bacillus subtilis spores harboring the gene circuit for the xylose-inducible secretory expression of Burkholderia cepacia lipase (BC-lipase). The spores that were resilient to stresses during material processing were mixed with poly(caprolactone) to produce living plastics in various formats. Spore incorporation did not compromise the physical properties of the materials. Spore recovery was triggered by eroding the plastic surface, after which the BC-lipase released by the germinated cells caused near-complete depolymerization of the polymer matrix. This study showcases a method for fabricating green plastics that can function when the spores are latent and decay when the spores are activated and sheds light on the development of materials for sustainability.

Designed to Degrade: Tailoring Polyesters for Circularity

A powerful toolbox is needed to turn the linear plastic economy into circular. Development of materials designed for mechanical recycling, chemical recycling, and/or biodegradation in targeted end-of-life environment are all necessary puzzle pieces in this process. Polyesters, with reversible ester bonds, are already forerunners in plastic circularity: poly(ethylene terephthalate) (PET) is the most recycled plastic material suitable for mechanical and chemical recycling, while common aliphatic polyesters are biodegradable under favorable conditions, such as industrial compost. However, this circular design needs to be further tailored for different end-of-life options to enable chemical recycling under greener conditions and/or rapid enough biodegradation even under less favorable environmental conditions. Here, we discuss molecular design of the polyester chain targeting enhancement of circularity by incorporation of more easily hydrolyzable ester bonds, additional dynamic bonds, or degradation catalyzing functional groups as part of the polyester chain. The utilization of polyester circularity to design replacement materials for current volume plastics is also reviewed as well as embedment of green catalysts, such as enzymes in biodegradable polyester matrices to facilitate the degradation process.

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Protein modified cellulose nanocrystals on reinforcement and self-driven biodegradation of aliphatic polyester

Due to the highly environment-dependent biodegradation and uncontrolled degradation period, the long-run feasibility and effectiveness of biodegradable polymers are extensively questioned to solve plastics waste accumulation and pollution problems. This work physically incorporated lipase PS from Burkholderia cepacian on cellulose nanocrystals (CNC) and embedded it in polycaprolactone (PCL) to construct stable and controllable interfacial microenvironment between CNC and PCL for the reinforcement and controllable self-driven biodegradation. The physical adsorption of lipase PS on CNC was studied by monitoring the surface charge and particle size. FT-IR spectra confirmed the successful incorporation of lipase PS and CNC. Compared with CNC, protein-modified CNC had a higher maximum thermal decomposition temperature of 345 °C and lower interfacial tension of 11 mN/m with PCL which provided PCL composites with higher nucleation efficiency and tensile elongation of 1086 % at break. In addition, only 0.67 % embedded lipase PS completely hydrolyzed PCL membranes in <140 h. The post-compression molding at 80-100 °C had negligible influence on the lipase activity, which indicated that CNC could protect the lipase from inactivation in polymer extrusion and compression. This work also highlighted protein-modified CNC as a new technology for polymer reinforcement.

Probing the Effects of Antimicrobial-Lysozyme Derivatization on Enzymatic Degradation of Poly(ε-caprolactone) Film and Fiber

Surface derivatization is essential for incorporating unique functionalities into biodegradable polymers. Nonetheless, its precise effects on enzymatic biodegradation still lack comprehensive understanding. In this study, a facile solution-based method is employed to surface derivatize poly(ε-caprolactone) films and electrospun fibers with lysozyme, aiming to impart antimicrobial properties and examine the impact on enzymatic degradation. The derivatized films and fibers have shown high antibacterial efficacy against Escherichia coli and Staphylococcus aureus. Through gravimetric analysis, it is observed that the degradation rate experiences a slight decrease upon lysozyme derivatization. However, this reduction is effectively countered by the inclusion of Tween-20, as affirmed by isothermal titration calorimetry. Comparing films and fibers, the latter undergoes degradation at a more accelerated pace, coupled with a rapid decline in molecular weight. This study provides valuable insights into the factors influencing the degradation of surface-derivatized biopolymers through electrospinning, offering a simple strategy to mitigate biomaterial-associated infections.

Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning

Single-chain nanoparticles (SCNPs) are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as catalysis, the utility of a single-chain nanoparticle will intricately depend on the formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. To address this knowledge gap, we simulate the formation of 7680 distinct single-chain nanoparticles from precursor chains that span a wide range of, in principle, tunable patterning characteristics of cross-linking moieties. Using a combination of molecular simulation and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of certain local and global morphological characteristics. Importantly, we illustrate and quantify the dispersity of morphologies that arise due to the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Moreover, we also examine the efficacy of precise sequence control in achieving morphological outcomes in different regimes of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and provides a platform to pursue future sequence-based design.

Boosting Degradation of Biodegradable Polymers

Biodegradation of polymers in composting conditions is an alternative end-of-life (EoL) scenario for contaminated materials collected through the municipal solid waste management system, mainly when mechanical or chemical methods cannot be used to recycle them. Compostability certification requirements are time-consuming and expensive. Therefore, approaches to accelerate the biodegradation of these polymers in simulated composting conditions can facilitate and speed up the evaluation and selection of potential compostable polymer alternatives and inform faster methods to biodegrade these polymers in real composting. This review highlights recent trends, challenges, and future strategies to accelerate biodegradation by modifying the polymer properties/structure and the compost environment. Both abiotic and biotic methods show potential for accelerating the biodegradation of biodegradable polymers. Abiotic methods, such as the incorporation of additives, reduction of molecular weight, reduction of size and reactive blending, are potentially the most straightforward, providing a level of technology that allows for easy adoption and adaptability. Novel methods, including the concept of self-immolative and triggering the scission of polymer chains in specific conditions, are increasingly sought. In terms of biotic methods, dispersion/encapsulation of enzymes during the processing step, biostimulation of the environment, and bioaugmentation with specific microbial strains during the biodegradation process are promising to accelerate biodegradation.

Sequence Design of Random Heteropolymers as Protein Mimics

Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo-Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.

Conductive Ink with Circular Life Cycle for Printed Electronics

Electronic waste carries energetic costs and an environmental burden rivaling that of plastic waste due to the rarity and toxicity of the heavy-metal components. Recyclable conductive composites are introduced for printed circuits formulated with polycaprolactone (PCL), conductive fillers, and enzyme/protectant nanoclusters. Circuits can be printed with flexibility (breaking strain ≈80%) and conductivity (≈2.1 × 10⁴ S m^-1 ). These composites are degraded at the end of life by immersion in warm water with programmable latency. Approximately 94% of the functional fillers can be recycled and reused with similar device performance. The printed circuits remain functional and degradable after shelf storage for at least 7 months at room temperature and one month of continuous operation under electrical voltage. The present studies provide composite design toward recyclable and easily disposable printed electronics for applications such as wearable electronics, biosensors, and soft robotics.

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids

Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer-protein hybrid materials.