Cradle-to-gate life cycle assessment of Spirulina bioplastic produced via plasticization with glycerol.

Bioplastics have been used as alternatives to conventional petroleum-based plastics to lessen the burdens on marine and terrestrial environments due to their non-biodegradability and toxicity. However, recent studies have shown that not all bioplastics may be environmentally friendly. Microalgae, such as Spirulina that do not require arable land, have been identified as a potential bioplastic source. In this study, cradle-to-gate life cycle assessment (LCA) was carried out in openLCA program using the Agribalyse database, to evaluate the environmental impacts of Spirulina bioplastic, formed from plasticization of Spirulina powder with glycerol. Two processes were created for the inventories of (i) Spirulina powder and (ii) Spirulina bioplastic, where the output of the former served as an input for the latter. The extruded bioplastic sheets were food-grade and could be used as edible packaging materials. The bioplastic was also compared to conventional plastics and it was found that the energy consumption was 3.83 ± 0.26 MJ/kg-bioplastic, which was 12% and 22% higher than that of LDPE and PVC plastic films, respectively. The impacts on the environment showed that the chemical growth medium (Zarrouk medium) and electricity were the main contributors in most of the categories. Compared to the PVC and LDPE films, the Spirulina bioplastic's impacts on the aquatic ecosystems were 2-3 times higher. The global warming potential of the Spirulina bioplastic was 1.99 ± 0.014 kg CO2 eq, which was 23% and 47% lower than that of LDPE and PVC films, respectively. Sensitivity analysis was carried out by changing the electricity source and using alternative growth media. Except for the case of switching to solar energy, the results for other cases did not differ significantly from the base case scenario. Future studies were suggested to identify different greener alternatives to the growth medium as well as different energy mixes for more environmentally benign solutions.

Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance.

Synthetic polymers, in contrast to small molecules and deterministic biomacromolecules, are typically ensembles composed of polymer chains with varying numbers, lengths, sequences, chemistry, and topologies. While numerous approaches exist for measuring pairwise similarity among small molecules and sequence-defined biomacromolecules, accurately determining the pairwise similarity between two polymer ensembles remains challenging. This work proposes the earth mover's distance (EMD) metric to calculate the pairwise similarity score between two polymer ensembles. EMD offers a greater resolution of chemical differences between polymer ensembles than the averaging method and provides a quantitative numeric value representing the pairwise similarity between polymer ensembles in alignment with chemical intuition. The EMD approach for assessing polymer similarity enhances the development of accurate chemical search algorithms within polymer databases and can improve machine learning techniques for polymer design, optimization, and property prediction.

Synthetic Collagen Hydrogels through Symmetric Self-Assembly of Small Peptides.

Animal-sourced hydrogels, such as collagen, are widely used as extracellular-matrix (ECM) mimics in tissue engineering but are plagued with problems of reproducibility, immunogenicity, and contamination. Synthetic, chemically defined hydrogels can avoid such issues. Despite the abundance of collagen in the ECM, synthetic collagen hydrogels are extremely rare due to design challenges brought on by the triple-helical structure of collagen. Sticky-ended symmetric self-assembly (SESSA) overcomes these challenges by maximizing interactions between the strands of the triple helix, allowing the assembly of collagen-mimetic peptides (CMPs) into robust synthetic collagen nanofibers. This optimization, however, also minimizes interfiber contacts. In this work, symmetric association states for the SESSA of short CMPs to probe their increased propensity for interfiber association are modelled. It is found that 33-residue CMPs not only self-assemble through sticky ends, but also form hydrogels. These self-assemblies behave with remarkable consistency across multiple scales and present a clear link between their triple-helical architecture and the properties of their hydrogels. The results show that SESSA is an effective and robust design methodology that enables the rational design of synthetic collagen hydrogels.

Reactivity-Guided Depercolation Processes Determine Fracture Behavior in End-Linked Polymer Networks.

The fracture of polymer networks is tied to the molecular behavior of strands within the network, yet the specific molecular-level processes that determine the mechanical limits of a network remain elusive. Here, the question of reactivity-guided fracture is explored in otherwise indistinguishable end-linked networks by tuning the relative composition of strands with two different mechanochemical reactivities. Increasing the substitution of less mechanochemically reactive ("strong") strands into a network comprising more reactive ("weak") strands has a negligible impact on the fracture energy until the strong strand content reaches approximately 45%, at which point the fracture energy sharply increases with strong strand content. This aligns with the measured strong strand percolation threshold of 48 ± 3%, revealing that depercolation, or the loss of a percolated network structure, is a necessary criterion for crack propagation in a polymer network. Coarse-grained fracture simulations agree closely with the tearing energy trend observed experimentally, confirming that weak strand scissions dominate the failure until the strong strands approach percolation. The simulations further show that twice as many strands break in a mixture than in a pure network.

BigSMARTS: A Topologically Aware Query Language and Substructure Search Algorithm for Polymer Chemical Structures.

Molecular search is important in chemistry, biology, and informatics for identifying molecular structures within large data sets, improving knowledge discovery and innovation, and making chemical data FAIR (findable, accessible, interoperable, reusable). Search algorithms for polymers are significantly less developed than those for small molecules because polymer search relies on searching by polymer name, which can be challenging because polymer naming is overly broad (i.e., polyethylene), complicated for complex chemical structures, and often does not correspond to official IUPAC conventions. Chemical structure search in polymers is limited to substructures, such as monomers, without awareness of connectivity or topology. This work introduces a novel query language and graph traversal search algorithm for polymers that provides the first search method able to fully capture all of the chemical structures present in polymers. The BigSMARTS query language, an extension of the small-molecule SMARTS language, allows users to write queries that localize monomer and functional group searches to different parts of the polymer, like the middle block of a triblock, the side chain of a graft, and the backbone of a repeat unit. The substructure search algorithm is based on the traversal of graph representations of the generating functions for the stochastic graphs of polymers. Operationally, the algorithm first identifies cycles representing the monomers and then the end groups and finally performs a depth-first search to match entire subgraphs. To validate the algorithm, hundreds of queries were searched against hundreds of target chemistries and topologies from the literature, with approximately 440,000 query-target pairs. This tool provides a detailed algorithm that can be implemented in search engines to provide search results with full matching of the monomer connectivity and polymer topology.

High-Throughput Screening of Streptavidin-Binding Proteins in Self-Assembled Solid Films for Directed Evolution of Materials.

Evolution has shaped the development of proteins with an incredible diversity of properties. Incorporating proteins into materials is desirable for applications including biosensing; however, high-throughput selection techniques for screening protein libraries in materials contexts is lacking. In this work, a high-throughput platform to assess the binding affinity for ordered sensing proteins was established. A library of fusion proteins, consisting of an elastin-like polypeptide block, one of 22 variants of rcSso7d, and a coiled-coil order-directing sequence, was generated. All selected variants had high binding in films, likely due to the similarity of the assay to magnetic bead sorting used for initial selection, while solution binding was more variable. From these results, both the assembly of the fusion proteins in their operating state and the functionality of the binding protein are key factors in the biosensing performance. Thus, the integration of directed evolution with assembled systems is necessary to the design of better materials.

Structural and dynamic heterogeneity in associative networks formed by artificially engineered protein polymers.

This work investigates static gel structure and cooperative multi-chain motion in associative networks using a well-defined model system composed of artificial coiled-coil proteins. The combination of small-angle and ultra-small-angle neutron scattering provides evidence for three static length scales irrespective of protein gel design which are attributed to correlations arising from the blob length, inter-junction spacing, and multi-chain density fluctuations. Self-diffusion measurements using forced Rayleigh scattering demonstrate an apparent superdiffusive regime in all gels studied, reflecting a transition between distinct "slow" and "fast" diffusive species. The interconversion between the two diffusive modes occurs on a length scale on the order of the largest correlation length observed by neutron scattering, suggesting a possible caging effect. Comparison of the self-diffusive behavior with characteristic molecular length scales and the single-sticker dissociation time inferred from tracer diffusion measurements supports the primarily single-chain mechanisms of self-diffusion as previously conceptualized. The step size of the slow mode is comparable to the root-mean-square length of the midblock strands, consistent with a single-chain walking mode rather than collective motion of multi-chain aggregates. The transition to the fast mode occurs on a timescale 10-1000 times the single-sticker dissociation time, which is consistent with the onset of single-molecule hopping. Finally, the terminal diffusivity depends exponentially on the number of stickers per chain, further suggesting that long-range diffusion occurs by molecular hopping rather than sticky Rouse motion of larger assemblies. Collectively, the results suggest that diffusion of multi-chain clusters is dominated by the single-chain pictures proposed in previous coarse-grained modeling.

Techno-economic assessment of co-production of edible bioplastic and food supplements from Spirulina.

Large amount of plastic wastes harming the environment have raised concerns worldwide on finding alternatives to non-biodegradable plastics. Microalgae has been found as a potential source for bioplastic production, besides its more common application in the pharmaceutical and nutraceutical industry. In this study, the objective was to techno-economically evaluate the large-scale co-production of Spirulina powder as food supplements and edible bioplastic for food packaging. The scale of production was large enough to satisfy 1% of local (Thailand) plastic demand (i.e., approx. 1200 MT y-1), and 1% of the global Spirulina demand (approx. 1000 MT y-1) as food supplements. Results showed that the co-production of the Spirulina powder and bioplastic revealed an attractive venture with a payback time (PBT) as low as 2.6 y and ROI as high as 38.5%. This was because the revenues generated were as high as US$ 55.6 million y-1, despite high capital (US$ 55.7 million) and operating (US$ 34.9 million y-1) costs. Sensitivity analysis showed differences in the profitability based on variations of major parameters in the study, where the split ratio of biomass used for food supplement versus bioplastic production and the bioplastic's selling price were found to be the most sensitive.

Automated, High-Throughput Screening of Hybrid Elastin-like Polypeptide/Polysaccharide Multilayer Film Deposition.

The self-assembled layer-by-layer technique has attracted a great deal of attention as a method for engineering bio-functional surfaces under mild chemical conditions. The production of multilayer films, starting from newly designed building blocks, may be laborious, considering the inherent limitations for anticipating how minimal changes in the macromolecular composition may impact both film deposition and performance. This paper presents an automated, high-throughput approach to depositing polyelectrolyte multilayers (PEMs) in multiwell plates, enabling the screening of nearly 100 film formulations in the same process. This high-throughput layer-by-layer (HT-LbL) method runs in an affordable, fully commercial platform using Python-coded routines that can be easily adapted for the materials science lab settings. The HT-LbL system was validated by investigating the deposition of polysaccharide-based films in multiwell plates, probing the absorbance signal of ionically stained polyelectrolyte multilayers (PEMs) prepared in one single batch. The HT-LbL method was also used to investigate the deposition of PEMs with a small library of genetically engineered elastin-like polypeptides (ELPs) with different levels of ionizable and hydrophobic amino acid residues. The deposition of ELP/chitosan films was assessed based on the signal of fluorescently labeled species (chitosan or ELP-mCherry), demonstrating that both electrostatic and hydrophobic residues are essential for film buildup. The growth and surface properties of ELP-mCherry/chitosan films also seemed susceptible to the assembly pH, forming a higher film growth and a rougher and more hydrophobic surface for both polyelectrolytes deposited under a low ionization degree. Overall, this study illustrates the challenge of predicting the growth and properties of multilayer films and how the HT-LbL can accelerate the development of multilayer films that demand high levels of testing and optimization.

High-throughput experimentation for discovery of biodegradable polyesters.

The consistent rise of plastic pollution has stimulated interest in the development of biodegradable plastics. However, the study of polymer biodegradation has historically been limited to a small number of polymers due to costly and slow standard methods for measuring degradation, slowing new material innovation. High-throughput polymer synthesis and a high-throughput polymer biodegradation method are developed and applied to generate a biodegradation dataset for 642 chemically distinct polyesters and polycarbonates. The biodegradation assay was based on the clear-zone technique, using automation to optically observe the degradation of suspended polymer particles under the action of a single Pseudomonas lemoignei bacterial colony. Biodegradability was found to depend strongly on aliphatic repeat unit length, with chains less than 15 carbons and short side chains improving biodegradability. Aromatic backbone groups were generally detrimental to biodegradability; however, ortho- and para-substituted benzene rings in the backbone were more likely to be degradable than metasubstituted rings. Additionally, backbone ether groups improved biodegradability. While other heteroatoms did not show a clear improvement in biodegradability, they did demonstrate increases in biodegradation rates. Machine learning (ML) models were leveraged to predict biodegradability on this large dataset with accuracies over 82% using only chemical structure descriptors.

Engineering a Two-Component Hemostat for the Treatment of Internal Bleeding through Wound-Targeted Crosslinking.

Primary hemostasis (platelet plug formation) and secondary hemostasis (fibrin clot formation) are intertwined processes that occur upon vascular injury. Researchers have sought to target wounds by leveraging cues specific to these processes, such as using peptides that bind activated platelets or fibrin. While these materials have shown success in various injury models, they are commonly designed for the purpose of treating solely primary or secondary hemostasis. In this work, a two-component system consisting of a targeting component (azide/GRGDS PEG-PLGA nanoparticles) and a crosslinking component (multifunctional DBCO) is developed to treat internal bleeding. The system leverages increased injury accumulation to achieve crosslinking above a critical concentration, addressing both primary and secondary hemostasis by amplifying platelet recruitment and mitigating plasminolysis for greater clot stability. Nanoparticle aggregation is measured to validate concentration-dependent crosslinking, while a 1:3 azide/GRGDS ratio is found to increase platelet recruitment, decrease clot degradation in hemodiluted environments, and decrease complement activation. Finally, this approach significantly increases survival relative to the particle-only control in a liver resection model. In light of prior successes with the particle-only system, these results emphasize the potential of this technology in aiding hemostasis and the importance of a holistic approach in engineering new treatments for hemorrhage.

Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure.

The Community Resource for Innovation in Polymer Technology (CRIPT) data model is designed to address the high complexity in defining a polymer structure and the intricacies involved with characterizing material properties.

Conformation of Network Strands in Polymer Gels.

Small angle neutron scattering was used to measure single chain radii of gyration of end-linked polymer gels before and after cross-linking to calculate the prestrain, which is the ratio of the average chain size in a cross-linked network to that of a free chain in solution. The prestrain increased from 1.06 ± 0.01 to 1.16 ± 0.02 as gel synthesis concentration decreased near the overlap concentration, indicating that the chains are slightly more stretched in the network than in solution. Dilute gels with higher loop fractions were found to be spatially homogeneous. Form factor and volumetric scaling analyses independently confirmed that elastic strands stretch by 2-23% from Gaussian conformations to create a space-spanning network, with increased stretching as network synthesis concentration decreases. Prestrain measurements reported here serve as a point of reference for network theories that rely on this parameter for the calculation of mechanical properties.

Canonicalizing BigSMILES for Polymers with Defined Backbones.

BigSMILES, a line notation for encapsulating the molecular structure of stochastic molecules such as polymers, was recently proposed as a compact and readable solution for writing macromolecules. While BigSMILES strings serve as useful identifiers for reconstructing the molecular connectivity for polymers, in general, BigSMILES allows the same polymer to be codified into multiple equally valid representations. Having a canonicalization scheme that eliminates the multiplicity would be very useful in reducing time-intensive tasks like structural comparison and molecular search into simple string-matching tasks. Motivated by this, in this work, two strategies for deriving canonical representations for linear polymers are proposed. In the first approach, a canonicalization scheme is proposed to standardize the expression of BigSMILES stochastic objects, thereby standardizing the expression of overall BigSMILES strings. In the second approach, an analogy between formal language theory and the molecular ensemble of polymer molecules is drawn. Linear polymers can be converted into regular languages, and the minimal deterministic finite automaton uniquely associated with each prescribed language is used as the basis for constructing the unique text identifier associated with each distinct polymer. Overall, this work presents algorithms to convert linear polymers into unique structure-based text identifiers. The derived identifiers can be readily applied in chemical information systems for polymers and other polymer informatics applications.

Extending BigSMILES to non-covalent bonds in supramolecular polymer assemblies.

As a machine-recognizable representation of polymer connectivity, BigSMILES line notation extends SMILES from deterministic to stochastic structures. The same framework that allows BigSMILES to accommodate stochastic covalent connectivity can be extended to non-covalent bonds, enhancing its value for polymers, supramolecular materials, and colloidal chemistry. Non-covalent bonds are captured through the inclusion of annotations to pseudo atoms serving as complementary binding pairs, minimal key/value pairs to elaborate other relevant attributes, and indexes to specify the pairing among potential donors and acceptors or bond delocalization. Incorporating these annotations into BigSMILES line notation enables the representation of four common classes of non-covalent bonds in polymer science: electrostatic interactions, hydrogen bonding, metal-ligand complexation, and π-π stacking. The principal advantage of non-covalent BigSMILES is the ability to accommodate a broad variety of non-covalent chemistry with a simple user-orientated, semi-flexible annotation formalism. This goal is achieved by encoding a universal but non-exhaustive representation of non-covalent or stochastic bonding patterns through syntax for (de)protonated and delocalized state of bonding as well as nested bonds for correlated bonding and multi-component mixture. By allowing user-defined descriptors in the annotation expression, further applications in data-driven research can be envisioned to represent chemical structures in many other fields, including polymer nanocomposite and surface chemistry.

Predicting transport of intra-articularly injected growth factor fusion proteins into human knee joint cartilage.

There are no drugs or treatment methods known to prevent the development of post-traumatic osteoarthritis (PTOA), a type of osteoarthritis (OA) that is triggered by traumatic joint injuries and accounts for ∼12% of the nearly 600 million OA cases worldwide. Lack of effective drug delivery techniques remains a major challenge in developing clinically effective treatments, but cationic delivery carriers can help overcome this challenge. Scaling up treatments that are effective in in vitro models to achieve success in preclinical in vivo models and clinical trials is also a challenging problem in the field. Here we use a cationic green fluorescent protein (GFP) as a carrier to deliver Insulin-Like Growth Factor 1 (IGF-1), a drug considered as a potential therapeutic for PTOA. GFP-IGF-1 conjugates were first synthesized as fusion proteins with different polypeptide linkers, and their transport properties were characterized in human cartilage explants. In vitro experimental data were used to develop a predictive mathematical transport model that was validated using an independent in vitro experimental data set. The model was used to predict the transport of these fusion proteins upon intra-articular injection into human knee joints. The predictions included results for the rate and extent of fusion protein penetration into cartilage, and the maximum levels of fusion proteins that would escape into systemic circulation through the joint capsule. Together, our transport measurements and model set the stage for translation of such explant culture studies to in vivo preclinical studies and potentially clinical application. STATEMENT OF SIGNIFICANCE: The lack of blood supply in cartilage and rapid clearance of drugs injected into human knees presents a major challenge in developing clinically effective treatments for osteoarthritis. Cationic delivery carriers can target negatively charged cartilage and help overcome this problem. Scaling up treatments that are effective in vitro to achieve success in vivo is also challenging. Here, we use a cationic green fluorescent protein (GFP) to deliver Insulin-Like Growth Factor-1 (IGF-1) into cartilage. Experiments measuring transport of GFP-IGF-1 fusion proteins in human cartilage explants were used to develop and validate a mathematical model to predict fusion protein transport upon injection into human knee joints. This work translates such explant culture studies to in vivo preclinical studies and potentially clinical application.

Strengthening and Toughening of Protein-Based Thermosets via Intermolecular Self-Assembly.

As proteins are abundant polymers in biomass sources such as agricultural feedstocks and byproducts, leveraging them to develop alternatives to synthetic polymers is of great interest. However, the mechanical performance of protein materials is not suitable for most target applications. Constructing copolymers with proteins as hard domains and rubbery polymers as soft domains has been shown to be a promising strategy for improving mechanical properties. Herein, it is demonstrated that toughening and strengthening of protein copolymers can be advanced further by thermal treatment, leading to mechanical enhancements that generalize across a variety of different protein feedstocks, including whey, serum, soy, and pea proteins. The thermal treatment induces a rearrangement of protein structure, leading to the formation of intermolecular ß-sheets. The ordered intermolecular structures in the hard domains of thermosets greatly improve their mechanical properties, providing simultaneous increases in strength, toughness, and modulus, with little sacrifice in fracture strain. Analogous to crystalline structures, the formation of intermolecular ß-sheet structures also leads to reduced hygroscopicity. This is a valuable contribution, as practical applications of natural polymer-based plastics are frequently hindered by the materials' humidity sensitivity. Therefore, this work demonstrates a simple yet versatile strategy to improve the materials' performance from a wide range of protein feedstocks, as well as signifies the implications of protein structural assembly in materials design.

A review of treatments for non-compressible torso hemorrhage (NCTH) and internal bleeding.

Non-compressible torso hemorrhage (NCTH) is associated with significant mortality in preventable deaths, both in the field and in civilian settings. Current management strategies of these injuries include fluid resuscitation, the use of foaming materials to occlude damaged vessels, and fibrin sealants. Researchers in the field have proposed multiple alternatives to these treatments, such as hemostatic sponges, self-assembling peptide materials, in situ crosslinking hydrogels, and intravenous nanoparticles, which are then challenged in a wide variety of injury models to evaluate their efficacy. This review first discusses the treatment of NCTH in the clinic and field before providing an overview of materials in literature designed for this same purpose, with the intention of summarizing the treatment options and research currently available in this field. The mechanisms of these hemostats, as well as their effectiveness in promoting hemostasis (evaluated through survival, bleeding time, and blood loss volume) are summarized side-by-side for easy comparison across various studies and animal models. Ultimately, a better understanding of existing technologies and the metrics through which they are evaluated may facilitate the development of safer, more effective therapies for non-compressible torso hemorrhage and internal bleeding.

Hierarchy of relaxation times in supramolecular polymer model networks.

Supramolecular polymer gels are an evolving class of soft materials with a vast number of properties that can be tuned to desired applications. Despite continuous advances concerning polymer synthesis, sustainability or adaptability, a consistent understanding of the interplay between structure, dynamics, and diffusion processes within transient networks is lacking. In this study, the hierarchy of several relaxation processes is investigated, starting from a microscopic perspective of a single sticker dissociation event up to the center-of-mass diffusion of a star-shaped polymer building block on different length scales, as well as the resulting macroscopic mechanical response to applied external stress. In addition to that, a second focus is placed on the gel micro-structure that is analyzed by light scattering. Conversion of the dynamic light scattering (DLS) inverse length scale into real space allows for a combination of relaxation times with those obtained by forced Rayleigh scattering (FRS). For these investigations, a model-type metallo-supramolecular network consisting of narrowly dispersed tetra-arm poly(ethylene glycol)-terpyridine macromolecules that are interconnected via complexation with zinc ions is chosen. Assembling the obtained activation energies reveals that all complex dissociation-governed relaxation processes exhibit similar activation energies.

Modulating Nanoparticle Size to Understand Factors Affecting Hemostatic Efficacy and Maximize Survival in a Lethal Inferior Vena Cava Injury Model.

Intravenous nanoparticle hemostats offer a potentially attractive approach to promote hemostasis, in particular for inaccessible wounds such as noncompressible torso hemorrhage (NCTH). In this work, particle size was tuned over a range of <100-500 nm, and its effect on nanoparticle-platelet interactions was systematically assessed using in vitro and in vivo experiments. Smaller particles bound a larger percentage of platelets per mass of particle delivered, while larger particles resulted in higher particle accumulation on a surface of platelets and collagen. Intermediate particles led to the greatest platelet content in platelet-nanoparticle aggregates, indicating that they may be able to recruit more platelets to the wound. In biodistribution studies, smaller and intermediate nanoparticles exhibited longer circulation lifetimes, while larger nanoparticles resulted in higher pulmonary accumulation. The particles were then challenged in a 2 h lethal inferior vena cava (IVC) puncture model, where intermediate nanoparticles significantly increased both survival and injury-specific targeting relative to saline and unfunctionalized particle controls. An increase in survival in the second hour was likewise observed in the smaller nanoparticles relative to saline controls, though no significant increase in survival was observed in the larger nanoparticle size. In conjunction with prior in vitro and in vivo experiments, these results suggest that platelet content in aggregates and extended nanoparticle circulation lifetimes are instrumental to enhancing hemostasis. Ultimately, this study elucidates the role of particle size in platelet-particle interactions, which can be a useful tool for engineering the performance of particulate hemostats and improving the design of these materials.