CyRSoXS: a GPU-accelerated virtual instrument for polarized resonant soft X-ray scattering.

Polarized resonant soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool that combines the principles of X-ray scattering and X-ray spectroscopy. P-RSoXS provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from P-RSoXS pattern data is challenging, however, because the scattering processes originate from sample properties that must be represented as energy-dependent three-dimensional tensors with heterogeneities at nanometre to sub-nanometre length scales. This challenge is overcome here by developing an open-source virtual instrument that uses graphical processing units (GPUs) to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. This computational framework - called CyRSoXS (https://github.com/usnistgov/cyrsoxs) - is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. The accuracy and robustness of the approach are demonstrated by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating an acceleration of over three orders of magnitude relative to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally unfeasible, including pattern fitting, co-simulation with the physical instrument for operando analytics, data exploration and decision support, data creation and integration into machine learning workflows, and utilization in multi-modal data assimilation approaches. Finally, the complexity of the computational framework is abstracted away from the end user by exposing CyRSoXS to Python using Pybind. This eliminates input/output requirements for large-scale parameter exploration and inverse design, and democratizes usage by enabling seamless integration with a Python ecosystem (https://github.com/usnistgov/nrss) that can include parametric morphology generation, simulation result reduction, comparison with experiment and data fitting approaches.

Carbon Nanotube Composites with Bottlebrush Elastomers for Compliant Electrodes.

Wearable electronics and biointerfacing technology require materials that are both compliant and conductive. The typical design strategy exploits polymer composites containing conductive particles, but the addition of a hard filler generally leads to a substantial increase in modulus that is not well-matched to biological tissue. Here, we report a new class of supersoft, conductive composites comprising carbon nanotubes (CNT) embedded in bottlebrush polymer networks. By virtue of the bottlebrush polymer architecture, these materials are several orders of magnitude softer than comparable composites in the literature involving linear polymer networks. For example, a CNT content of 0.25 wt % yields a shear modulus of 66 kPa while maintaining a typical conductivity for a CNT composite (ca. 10-2 S/m). An added benefit of this bottlebrush matrix chemistry is the presence of dynamic polyester bonds that facilitate thermal (re)processing. This unique strategy of designing soft composites provides new opportunities to tailor the structure and properties of sustainable advanced materials.

Light-Switchable and Self-Healable Polymer Electrolytes Based on Dynamic Diarylethene and Metal-Ion Coordination.

Self-healing polymer electrolytes are reported with light-switchable conductivity based on dynamic N-donor ligand-containing diarylethene (DAE) and multivalent Ni2+ metal-ion coordination. Specifically, a polystyrene polymer grafted with poly(ethylene glycol-r-DAE)acrylate copolymer side chains was effectively cross-linked with nickel(II) bis(trifluoromethanesulfonimide) (Ni(TFSI)2) salts to form a dynamic network capable of self-healing with fast exchange kinetics under mild conditions. Furthermore, as a photoswitching compound, the DAE undergoes a reversible structural and electronic rearrangement that changes the binding strength of the DAE-Ni2+ complex under irradiation. This can be observed in the DAE-containing polymer electrolyte where irradiation with UV light triggers an increase in the resistance of solid films, which can be recovered with subsequent visible light irradiation. The increase in resistance under UV light irradiation indicates a decrease in ion mobility after photoswitching, which is consistent with the stronger binding strength of ring-closed DAE isomers with Ni2+. 1H-15N heteronuclear multiple-bond correlation nuclear magnetic resonance (HMBC NMR) spectroscopy, continuous wave electron paramagnetic resonance (cw EPR) spectroscopy, and density functional theory (DFT) calculations confirm the increase in binding strength between ring-closed DAE with metals. Rheological and in situ ion conductivity measurements show that these polymer electrolytes efficiently heal to recover their mechanical properties and ion conductivity after damage, illustrating potential applications in smart electronics.

Room temperature 3D printing of super-soft and solvent-free elastomers.

Super-soft elastomers derived from bottlebrush polymers show promise as advanced materials for biomimetic tissue and device applications, but current processing strategies are restricted to simple molding. Here, we introduce a design concept that enables the three-dimensional (3D) printing of super-soft and solvent-free bottlebrush elastomers at room temperature. The key advance is a class of inks comprising statistical bottlebrush polymers that self-assemble into well-ordered body-centered cubic sphere phases. These soft solids undergo sharp and reversible yielding at 20°C in response to shear with a yield stress that can be tuned by manipulating the length scale of microphase separation. The addition of a soluble photocrosslinker allows complete ultraviolet curing after extrusion to form super-soft elastomers with near-perfect recoverable elasticity well beyond the yield strain. These structure-property design rules create exciting opportunities to tailor the performance of 3D-printed elastomers in ways that are not possible with current materials and processes.