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Flexible and lightweight sensors can assess their environment for applications that include wearables for health monitoring and soft robotics.

 

Plant-derived phenylpropanoids, in particular phenylpropenes, have diverse industrial applications ranging from flavors and fragrances to polymers and pharmaceuticals. Heterologous biosynthesis of these products has the potential to address low, seasonally dependent yields hindering ease of widespread manufacturing. However, previous efforts have been hindered by the inherent pathway promiscuity and the microbial toxicity of key pathway intermediates. Here, in this study, we establish the propensity of a tripartite microbial co-culture to overcome these limitations and demonstrate to our knowledge the first reported de novo phenylpropene production from simple sugar starting materials. After initially designing the system to accumulate eugenol, the platform modularity and downstream enzyme promiscuity was leveraged to quickly create avenues for hydroxychavicol and chavicol production. The consortia was found to be compatible with Engineered Living Material production platforms that allow for reusable, cold-chain-independent distributed manufacturing. This work lays the foundation for further deployment of modular microbial approaches to produce plant secondary metabolites.

 

3D‐printed engineered living materials (ELM) are promising bioproduction platforms for agriculture, biotechnology, sustainable energy, and green technology applications. However, the design of these platforms faces several challenges, such as the processability of these materials into complex form factors and control over their mechanical properties. Herein, ELM are presented as 3D‐printed bioreactors with arbitrary shape geometries and tunable mechanical properties (moduli and toughness). Poly(ethylene glycol) diacrylate (PEGDA) is used as the precursor to create polymer networks that encapsulate the microorganisms during the vat photopolymerization process. A major limitation of PEGDA networks is their propensity to swell and fracture when submerged in water. The authors overcame this issue by adding glycerol to the resin formulation to 3D print mechanically tough ELM hydrogels. While polymer concentration affects the modulus and reduces bioproduction, ELM bioreactors still maintain their metabolic activity regardless of polymer concentration. These ELM bioreactors have the potential to be used in different applications for sustainable architecture, food production, and biomedical devices that require different mechanical properties from soft to stiff.

 

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts. 

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. 

The fabrication of hydrogel materials has gained increased attention for a broad range of biomedical and biotechnological applications. However, one longstanding challenge in the field is to develop hydrogels that can be easily processed into the desired form factor, while achieving the necessary final physical and biochemical properties. Herein, we report a shear-thinning hydrogel ink that can be photo-cured to create a stretchable, suturable hydrogel whose polymer network is formed via the combination of thiol-Michael addition and radical polymerization. A shear-thinning hydrogel based on bis-methacrylated Pluronic® F-127 was modified with varying equivalents of 2,2′-(ethylenedioxy)diethanethiol (EDT) as an additive. We observed that aging the hydrogel over time prior to extrusion allowed the relatively slow thiol-Michael addition to occur (between thiol and methacrylate) prior to UV initiated photopolymerization of the methacrylates. The viscoelastic properties of these hydrogels could be tuned based on the amount of EDT added, and the aging time of the hydrogel formulation. The changes to the physical properties of the hydrogels were attributed to the increased chain length between network junctions that resulted from the thiol-Michael addition reactions. The optimized hydrogel composition was then extruded from a coaxial nozzle to produce hydrogel tubes that, after curing, were resistant to tearing and were suturable. These extrudable synthetic hydrogels with tunable viscoelastic properties are promising for tissue engineering applications and as surgical training models for human vasculature.