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Abstract Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning approaches hold great promise to fitting complex many-body data. However, training models may require collection of large amounts of expensive data. Moreover, quantifying trained model accuracy is challenging, especially in cases of non-trivial free energy configurations, where training data may be sparse. We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces. Specifically, we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and opens the possibility of adaptive transfer of models across different chemical systems. Uncertainties also characterize models’ accuracy of free energy predictions, even when training is performed only on forces. This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models. 

Abstract An aqueous emulsion of conducting polymer is commonly applied on a substrate to form a coating after drying. The coating, however, disintegrates in water. This paper reports a coating prepared using a mixture of two emulsions: an aqueous emulsion of conducting polymer, and an aqueous emulsion of hydrophobic and rubbery chains copolymerized with silane coupling agents. When applied on a substrate and dried, particles of the mixed emulsion merge into a continuous film. While the conducting polymer forms percolated nanocrystals, the silane groups crosslink the rubbery chains and interlink the rubbery chains to the substrate. The percolated nanocrystals make the coating highly conductive. The covalent network of hydrophobic polymer chains stabilizes the coating in water. The high conductivity and stability in water may enable broad applications. 

ABSTRACT Corners concentrate elastic fields and often initiate fracture. For small deformations, it is well established that the elastic field around a corner is power-law singular. For large deformations, we show here that the elastic field around a corner is concentrated but bounded. We conduct computation and an experiment on the lap shear of a highly stretchable material. A rectangular sample was sandwiched between two rigid substrates, and the edges of the stretchable material met the substrates at 90° corners. The substrates were pulled to shear the sample. We computed the large-deformation elastic field by assuming several models of elasticity. The theory of elasticity has no length scale, and lap shear is characterized by a single length, the thickness of the sample. Consequently, the field in the sample was independent of any length once the spatial coordinates were normalized by the thickness. We then lap sheared samples of a polyacrylamide hydrogel of various thicknesses. For all samples, fracture initiated from corners, at a load independent of thickness. These experimental findings agree with the computational prediction that large-deformation elastic fields at corners are concentrated but bounded. 

Abstract BackgroundRecent reports of extreme levels of undersaturation in internal leaf air spaces have called into question one of the foundational assumptions of leaf gas exchange analysis, that leaf air spaces are effectively saturated with water vapour at leaf surface temperature. Historically, inferring the biophysical states controlling assimilation and transpiration from the fluxes directly measured by gas exchange systems has presented a number of challenges, including: (1) a mismatch in scales between the area of flux measurement, the biochemical cellular scale and the meso-scale introduced by the localization of the fluxes to stomatal pores; (2) the inaccessibility of the internal states of CO2 and water vapour required to define conductances; and (3) uncertainties about the pathways these internal fluxes travel. In response, plant physiologists have adopted a set of simplifying assumptions that define phenomenological concepts such as stomatal and mesophyll conductances. ScopeInvestigators have long been concerned that a failure of basic assumptions could be distorting our understanding of these phenomenological conductances, and the biophysical states inside leaves. Here we review these assumptions and historical efforts to test them. We then explore whether artefacts in analysis arising from the averaging of fluxes over macroscopic leaf areas could provide alternative explanations for some part, if not all, of reported extreme states of undersaturation. ConclusionsSpatial heterogeneities can, in some cases, create the appearance of undersaturation in the internal air spaces of leaves. Further refinement of experimental approaches will be required to separate undersaturation from the effects of spatial variations in fluxes or conductances. Novel combinations of current and emerging technologies hold promise for meeting this challenge. 

Abstract Elastomers generally possess low Young's modulus and high failure strain, which are widely used in soft robots and intelligent actuators. However, elastomers generally lack diverse functionalities, such as stimulated shape morphing, and a general strategy to implement these functionalities into elastomers is still challenging. Here, a microfluidic 3D droplet printing platform is developed to design composite elastomers architected with arrays of functional droplets. Functional droplets with controlled size, composition, position, and pattern are designed and implemented in the composite elastomers, imparting functional performances to the systems. The composited elastomers are sensitive to stimuli, such as solvent, temperature, and light, and are able to demonstrate multishape (bow‐ and S‐shaped), multimode (gradual and sudden), and multistep (one‐ and two‐step) deformations. Based on the unique properties of droplet‐embedded composite elastomers, a variety of stimuli‐responsive systems are developed, including designable numbers, biomimetic flowers, and soft robots, and a series of functional performances are achieved, presenting a facile platform to impart diverse functionalities into composite elastomers by microfluidic 3D droplet printing. 

Abstract Multielectrode arrays would benefit from intimate engagement with neural cells, but typical arrays do not present a physical environment that mimics that of neural tissues. It is hypothesized that a porous, conductive hydrogel scaffold with appropriate mechanical and conductive properties could support neural cells in 3D, while tunable electrical and mechanical properties could modulate the growth and differentiation of the cellular networks. By incorporating carbon nanomaterials into an alginate hydrogel matrix, and then freeze‐drying the formulations, scaffolds which mimic neural tissue properties are formed. Neural progenitor cells (NPCs) incorporated in the scaffolds form neurite networks which span the material in 3D and differentiate into astrocytes and myelinating oligodendrocytes. Viscoelastic and more conductive scaffolds produce more dense neurite networks, with an increased percentage of astrocytes and higher myelination. Application of exogenous electrical stimulation to the scaffolds increases the percentage of astrocytes and the supporting cells localize differently with the surrounding neurons. The tunable biomaterial scaffolds can support neural cocultures for over 12 weeks, and enable a physiologically mimicking in vitro platform to study the formation of neuronal networks. As these materials have sufficient electrical properties to be used as electrodes in implantable arrays, they may allow for the creation of biohybrid neural interfaces and living electrodes. 

Abstract Many living tissues achieve functions through architected constituents with strong adhesion. An Achilles tendon, for example, transmits force, elastically and repeatedly, from a muscle to a bone through staggered alignment of stiff collagen fibrils in a soft proteoglycan matrix. The collagen fibrils align orderly and adhere to the proteoglycan strongly. However, synthesizing architected materials with strong adhesion has been challenging. Here we fabricate architected polymer networks by sequential polymerization and photolithography, and attain adherent interface by topological entanglement. We fabricate tendon-inspired hydrogels by embedding hard blocks in topological entanglement with a soft matrix. The staggered architecture and strong adhesion enable high elastic limit strain and high toughness simultaneously. This combination of attributes is commonly desired in applications, but rarely achieved in synthetic materials. We further demonstrate architected polymer networks of various geometric patterns and material combinations to show the potential for expanding the space of material properties. 

Abstract A hydrogel is often fabricated from preexisting polymer chains by covalently crosslinking them into a polymer network. The crosslinks make the hydrogel swell‐resistant but brittle. This conflict is resolved here by making a hydrogel from a dough. The dough is formed by mixing long polymer chains with a small amount of water and photoinitiator. The dough is then homogenized by kneading and annealing at elevated temperatures, during which the crowded polymer chains densely entangle. The polymer chains are then sparsely crosslinked into a polymer network under an ultraviolet lamp, and submerged in water to swell to equilibrium. The resulting hydrogel is both swell‐resistant and tough. The hydrogel also has near‐perfect elasticity, high strength, high fatigue resistance, and low friction. The method is demonstrated with two widely used polymers, poly(ethylene glycol) and cellulose. These hydrogels have never been made swell‐resistant, elastic, and tough before. The method is generally applicable to synthetic and natural polymers, and is compatible with industrial processing technologies, opening doors to the development of sustainable, high‐performance hydrogels. 

Abstract Fatigue-induced cracking in steel components and other brittle materials of civil structures is one of the primary mechanisms of degrading structural integrity and can lead to sudden failures. However, these cracks are often difficult to detect during visual inspections, and off-the-shelf sensing technologies can generally only be used to monitor already identified cracks because of their spatial localization. A solution is to leverage advances in large area electronics to cover large surfaces with skin-type sensors. Here, the authors propose an elastic and stretchable multifunctional skin sensor that combines optical and capacitive sensing properties. The multifunctional sensor consists of a soft stretchable structural color film sandwiched between transparent carbon nanotube electrodes to form a parallel plate capacitor. The resulting device exhibits a reversible and repeatable structural color change from light blue to deep blue with an angle-independent property, as well as a measurable change in capacitance, under external mechanical strain. The optical function is passive and engineered to visually assist in localizing fatigue cracks, and the electrical function is added to send timely warnings to infrastructure operators. The performance of the device is characterized in a free-standing configuration and further extended to a fatigue crack monitoring application. A correlation coefficient-based image processing method is developed to quantify the strain measured by the optical color response. Results show that the sensor performs well in detecting and quantifying fatigue cracks using both the color and capacitive signals. In particular, the color signal can be measured with inexpensive cameras, and the electrical signal yields good linearity, resolution, and accuracy. Tests conducted on two steel specimens demonstrate a minimum detectable crack length of 0.84 mm. 

Abstract Strong and tough bio‐based fibers are attractive for both fundamental research and practical applications. In this work, strong and tough hierarchical core–shell fibers with cellulose nanofibrils (CNFs) in the core and regenerated silk fibroins (RSFs) in the shell are designed and prepared, mimicking natural spider silks. CNF/RSF core–shell fibers with precisely controlled morphology are continuously wet‐spun using a co‐axial microfluidic device. Highly‐dense non‐covalent interactions are introduced between negatively‐charged CNFs in the core and positively‐charged RSFs in the shell, diminishing the core/shell interface and forming an integral hierarchical fiber. Meanwhile, shearing by microfluidic channels and post‐stretching induce a better ordering of CNFs in the core and RSFs in the shell, while ordered CNFs and RSFs are more densely packed, thus facilitating the formation of non‐covalent interactions within the fiber matrix. Therefore, CNF/RSF core–shell fibers demonstrate excellent mechanical performances; especially after post‐stretching, their tensile strength, tensile strain, Young's modulus, and toughness are up to 635 MPa, 22.4%, 24.0 GPa, and 110 MJ m⁻³, respectively. In addition, their mechanical properties are barely compromised even at −40 and 60 °C. Static load and dynamic impact tests suggest that CNF/RSF core–shell fibers are strong and tough, making them suitable for advanced structural materials.