Extreme thermodynamics with polymer gel tori:Harnessing thermodynamic instabilities to induce large-scale deformations

When a swollen, thermoresponsive polymer gel is heated in a solvent bath, it expels solvent and deswells. When this heating is slow, deswelling proceeds homogeneously, as observed in a toroid-shaped gel that changes volume whilst maintaining its toroidal shape. By contrast, if the gel is heated quickly, an impermeable layer of collapsed polymer forms and traps solvent within the gel, arresting the volume change. The ensuing evolution of the gel then happens at fixed volume, leading to phase-separation and the development of inhomogeneous stress that deforms the toroidal shape. We observe that this stress can cause the torus to buckle out of the plane, via a mechanism analogous to the bending of bimetallic strips upon heating. Our results demonstrate that thermodynamic instabilities, i.e., phase transitions, can be used to actuate mechanical deformation in an extreme thermodynamics of materials.Comment: 5 pages, 4 figures. To appear in Physical Review E (2018

Self-Propelled Microswimmer Actuated by Stimuli-Sensitive Bilayered Hydrogel

Using computational modeling, we design a microscopic swimmer made of a bilayered responsive hydrogel capable of swimming in a viscous fluid when actuated by a periodically applied stimulus. The gel has an X-shaped geometry and two bonded layers, one of which is responsive to environmental changes and the other which is passive. When the stimulus is turned on, the responsive layer swells and causes the swimmer to deform. We demonstrate that when such stimulus-induced deformations occur periodically the gel swimmer effectively propels forward through the fluid. We show that the swimming speed depends on the relative stiffness of the two gel layers composing the swimmer, and we determine the optimal stiffness ratio that maximizes the swimming speed

Bimorph Silk Microsheets with Programmable Actuating Behavior: Experimental Analysis and Computer Simulations

Microscaled self-rolling construct sheets from silk protein material have been fabricated, containing a silk bimorph composed of silk ionomers as an active layer and cross-linked silk β-sheet as the passive layer. The programmable morphology was experimentally explored along with a computational simulation to understand the mechanism of shape reconfiguration. The neutron reflectivity shows that the active silk ionomers layer undergoes remarkable swelling (eight times increase in thickness) after deprotonation while the passive silk β-sheet retains constant volume under the same conditions and supports the bimorph construct. This selective swelling within the silk-on-silk bimorph microsheets generates strong interfacial stress between layers and out-of-plane forces, which trigger autonomous self-rolling into various 3D constructs such as cylindrical and helical tubules. The experimental observations and computational modeling confirmed the role of interfacial stresses and allow programming the morphology of the 3D constructs with particular design. We demonstrated that the biaxial stress distribution over the 2D planar films depends upon the lateral dimensions, thickness and the aspect ratio of the microsheets. The results allow the fine-tuning of autonomous shape transformations for the further design of complex micro-origami constructs and the silk based rolling/unrolling structures provide a promising platform for polymer-based biomimetic devices for implant applications

CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points