Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits.

Most soft robots are pneumatically actuated and fabricated by molding and assembling processes that typically require many manual operations and limit complexity. Furthermore, complex control components (for example, electronic pumps and microcontrollers) must be added to achieve even simple functions. Desktop fused filament fabrication (FFF) three-dimensional printing provides an accessible alternative with less manual work and the capability of generating more complex structures. However, because of material and process limitations, FFF-printed soft robots often have a high effective stiffness and contain a large number of leaks, limiting their applications. We present an approach for the design and fabrication of soft, airtight pneumatic robotic devices using FFF to simultaneously print actuators with embedded fluidic control components. We demonstrated this approach by printing actuators an order of magnitude softer than those previously fabricated using FFF and capable of bending to form a complete circle. Similarly, we printed pneumatic valves that control a high-pressure airflow with low control pressure. Combining the actuators and valves, we demonstrated a monolithically printed electronics-free autonomous gripper. When connected to a constant supply of air pressure, the gripper autonomously detected and gripped an object and released the object when it detected a force due to the weight of the object acting perpendicular to the gripper. The entire fabrication process of the gripper required no posttreatment, postassembly, or repair of manufacturing defects, making this approach highly repeatable and accessible. Our proposed approach represents a step toward complex, customized robotic systems and components created at distributed fabricating facilities.

Building Tunable Degradation into High-Performance Poly(acrylate) Pressure-Sensitive Adhesives.

Pressure-sensitive adhesives (PSAs) based on poly(acrylate) chemistry are common in a wide variety of applications, but the absence of backbone degradability causes issues with recycling and sustainability. Here, we report a strategy to create degradable poly(acrylate) PSAs using simple, scalable, and functional 1,2-dithiolanes as drop-in replacements for traditional acrylate comonomers. Our key building block is α-lipoic acid, a natural, biocompatible, and commercially available antioxidant found in various consumer supplements. α-Lipoic acid and its derivative ethyl lipoate efficiently copolymerize with n-butyl acrylate under conventional free-radical conditions leading to high-molecular-weight copolymers (Mn > 100 kg mol-1) containing a tunable concentration of degradable disulfide bonds along the backbone. The thermal and viscoelastic properties of these materials are practically indistinguishable from nondegradable poly(acrylate) analogues, but a significant reduction in molecular weight is realized upon exposure to reducing agents such as tris (2-carboxyethyl) phosphine (e.g., Mn = 198 kg mol-1 → 2.6 kg mol-1). By virtue of the thiol chain ends produced after disulfide cleavage, degraded oligomers can be further cycled between high and low molecular weights through oxidative repolymerization and reductive degradation. Transforming otherwise persistent poly(acrylates) into recyclable materials using simple and versatile chemistry could play a pivotal role in improving the sustainability of contemporary adhesives.

Predicting surfactant phase behavior with a molecularly informed field theory.

HYPOTHESIS: The computational study of surfactants and self-assembly is challenging because 1) models need to reflect chemistry-specific interactions, and 2) self-assembled structures are difficult to equilibrate with conventional molecular dynamics. We propose to overcome these challenges with a multiscale simulation approach where relative entropy minimization transfers chemically-detailed information from all-atom (AA) simulations to coarse-grained (CG) models that can be simulated using field-theoretic methods. Field-theoretic simulations are not limited by intrinsic physical time scales like diffusion and allow for rigorous equilibration via free energy minimization. This approach should enable the study of properties that are difficult to obtain by particle-based simulations. SIMULATION WORK: We apply this workflow to sodium dodecylsulfate. To ensure chemical fidelity we present an AA force field calibrated against interfacial tension experiments. We generate CG models from AA simulation trajectories and show that particle-based and field-theoretic simulations of the CG model reproduce AA simulations and experimental measurements. FINDINGS: The workflow captures the complex balance of interactions in a multicomponent system ultimately described by an atomistic model. The resulting CG models can study complex 3D phases like double or alternating gyroids, and reproduce salt effects on properties like aggregation number and shape transitions.

Light-Mediated Synthesis and Reprocessing of Dynamic Bottlebrush Elastomers under Ambient Conditions.

We introduce a novel grafting-through polymerization strategy to synthesize dynamic bottlebrush polymers and elastomers in one step using light to construct a disulfide-containing backbone. The key starting material-α-lipoic acid (LA)-is commercially available, inexpensive, and biocompatible. When installed on the chain end(s) of poly(dimethylsiloxane) (PDMS), the cyclic disulfide unit derived from LA polymerizes under ultraviolet (UV) light in ambient conditions. Significantly, no additives such as initiator, solvent, or catalyst are required for efficient gelation. Formulations that include bis-LA-functionalized cross-linker yield bottlebrush elastomers with high gel fractions (83-98%) and tunable, supersoft shear moduli in the ∼20-200 kPa range. An added advantage of these materials is the dynamic disulfide bonds along each bottlebrush backbone, which allow for light-mediated self-healing and on-demand chemical degradation. These results highlight the potential of simple and scalable synthetic routes to generate unique bottlebrush polymers and elastomers based on PDMS.

Entrepreneurship in Polymer Chemistry.

Launching a startup company is like synthesizing a new molecule. There is a starting point and a general concept for how to achieve the desired end. Known steps may be taken, but a successful synthesis is rarely the result of the original plan and relies on perseverance and creativity. If done well, the starting molecule (idea) gives rise to a new final product (business). Having personally lived these journeys, the authors of this viewpoint distilled their combined experiences into relevant topics for scientific entrepreneurs. This viewpoint is not a how-to guide for launching a startup. Instead, relatable personal insights and potential best practices are shared to catalyze discussions around a topic of growing relevance to both the polymer community and workforce of the future.

Molecularly Informed Field Theories from Bottom-up Coarse-Graining.

Polymer formulations possessing mesostructures or phase coexistence are challenging to simulate using atomistic particle-explicit approaches due to the disparate time and length scales, while the predictive capability of field-based simulations is hampered by the need to specify interactions at a coarser scale (e.g., χ-parameters). To overcome the weaknesses of both, we introduce a bottom-up coarse-graining methodology that leverages all-atom molecular dynamics to molecularly inform coarser field-theoretic models. Specifically, we use relative-entropy coarse-graining to parametrize particle models that are directly and analytically transformable into statistical field theories. We demonstrate the predictive capability of this approach by reproducing experimental aqueous poly(ethylene oxide) (PEO) cloud-point curves with no parameters fit to experimental data. This synergistic approach to multiscale polymer simulations opens the door to de novo exploration of phase behavior across a wide variety of polymer solutions and melt formulations.

Learning composition-transferable coarse-grained models: Designing external potential ensembles to maximize thermodynamic information.

Achieving thermodynamic faithfulness and transferability across state points is an outstanding challenge in the bottom-up coarse graining of molecular models, with many efforts focusing on augmenting the form of coarse-grained interaction potentials to improve transferability. Here, we revisit the critical role of the simulation ensemble and the possibility that even simple models can be made more predictive through a smarter choice of ensemble. We highlight the efficacy of coarse graining from ensembles where variables conjugate to the thermodynamic quantities of interest are forced to respond to applied perturbations. For example, to learn activity coefficients, it is natural to coarse grain from ensembles with spatially varying external potentials applied to one species to force local composition variations and fluctuations. We apply this strategy to coarse grain both an atomistic model of water and methanol and a binary mixture of spheres interacting via Gaussian repulsions and demonstrate near-quantitative capture of activity coefficients across the whole composition range. Furthermore, the approach is able to do so without explicitly measuring and targeting activity coefficients during the coarse graining process; activity coefficients are only computed after-the-fact to assess accuracy. We hypothesize that ensembles with applied thermodynamic potentials are more "thermodynamically informative." We quantify this notion of informativeness using the Fisher information metric, which enables the systematic design of optimal bias potentials that promote the learning of thermodynamically faithful models. The Fisher information is related to variances of structural variables, highlighting the physical basis underlying the Fisher information's utility in improving coarse-grained models.

Band Gap and HOMO Level Control in Poly(thienylene vinylene)s Prepared by ADMET Polymerization.

Three dipropenyl monomers were prepared for ruthenium-catalyzed acyclic diene metathesis (ADMET) polymerization. Homopolymerization afforded three poly(thienylene vinylene)s (PTVs) with distinct optoelectronic properties. Binary combinations of the monomers over a range of compositions gave three series of copolymer samples with tunable HOMO levels and band gaps. The utility of this method was further demonstrated by the preparation of a stoichiometric terpolymer. Polymers were characterized by 1H NMR spectroscopy, size-exclusion chromatography, ultraviolet-visible spectroscopy, and cyclic voltammetry. This copolymerization approach effectively demonstrates the ability of ADMET polymerization to prepare conjugated copolymers with tuned optoelectronic properties that span a broad composition window.