Enhancing the ionic conductivity across the electrolyte separator in nonaqueous redox flow batteries (NRFBs) is essential for improving their performance and enabling their widespread utilization. Separating redox-active species by size exclusion without greatly impeding the transport of supporting electrolyte is a potentially powerful alternative to the use of poorly performing ion-exchange membranes. However, this strategy has not been explored possibly due to the lack of suitable redox-active species that are easily varied in size, remain highly soluble, and exhibit good electrochemical properties. Here we report the synthesis, electrochemical characterization, and transport properties of redox-active poly(vinylbenzyl ethylviologen) (RAPs) with molecular weights between 21 and 318 kDa. The RAPs reported here show very good solubility (up to at least 2.0 M) in acetonitrile and propylene carbonate. Ultramicroelectrode voltammetry reveals facile electron transfer with E1/2 ∼ -0.7 V vs Ag/Ag(+)(0.1 M) for the viologen 2+/+ reduction at concentrations as high as 1.0 M in acetonitrile. Controlled potential bulk electrolysis indicates that 94-99% of the nominal charge on different RAPs is accessible and that the electrolysis products are stable upon cycling. The dependence of the diffusion coefficient on molecular weight suggests the adequacy of the Stokes-Einstein formalism to describe RAPs. The size-selective transport properties of LiBF4 and RAPs across commercial off-the-shelf (COTS) separators such as Celgard 2400 and Celgard 2325 were tested. COTS porous separators show ca. 70 times higher selectivity for charge balancing ions (Li(+)BF4(-)) compared to high molecular weight RAPs. RAPs rejection across these separators showed a strong dependence on polymer molecular weight as well as the pore size; the rejection increased with both increasing polymer molecular weight and reduction in pore size. Significant rejection was observed even for rpoly/rpore (polymer solvodynamic size relative to pore size) values as low as 0.3. The high concentration attainable (>2.0 M) for RAPs in common nonaqueous battery solvents, their electrochemical and chemical reversibility, and their hindered transport across porous separators make them attractive materials for nonaqueous redox flow batteries based on the enabling concept of size-selectivity.

Versatile and readily available battery materials compatible with a range of electrode configurations and cell designs are desirable for renewable energy storage. Here we report a promising class of materials based on redox active colloids (RACs) that are inherently modular in their design and overcome challenges faced by small-molecule organic materials for battery applications, such as crossover and chemical/morphological stability. RACs are cross-linked polymer spheres, synthesized with uniform diameters between 80 and 800 nm, and exhibit reversible redox activity as single particles, as monolayer films, and in the form of flowable dispersions. Viologen-based RACs display reversible cycling, accessing up to 99% of their capacity and 99 ± 1% Coulombic efficiency over 50 cycles by bulk electrolysis owing to efficient, long-distance intraparticle charge transfer. Ferrocene-based RACs paired with viologen-based RACs cycled efficiently in a nonaqueous redox flow battery employing a simple size-selective separator, thus demonstrating a possible application that benefits from their colloidal dimensions. The unprecedented versatility in RAC synthetic and electrochemical design opens new avenues for energy storage.

To identify high-affinity interactions between long-chain α-neurotoxins and nicotinic receptors, we determined the crystal structure of the complex between α-btx (α-bungarotoxin) and a pentameric ligand-binding domain constructed from the human α7 AChR (acetylcholine receptor) and AChBP (acetylcholine-binding protein). The complex buries ~2000 Å2 (1 Å = 0.1 nm) of surface area, within which Arg36 and Phe32 from finger II of α-btx form a π-cation stack that aligns edge-to-face with the conserved Tyr184 from loop-C of α7, while Asp30 of α-btx forms a hydrogen bond with the hydroxy group of Tyr184. These inter-residue interactions diverge from those in a 4.2 Å structure of α-ctx (α-cobratoxin) bound to AChBP, but are similar to those in a 1.94 Å structure of α-btx bound to the monomeric α1 extracellular domain, although compared with the monomer-bound complex, the α-btx backbone exhibits a large shift relative to the protein surface. Mutational analyses show that replacing Tyr184 with a threonine residue abolishes high-affinity α-btx binding, whereas replacing with a phenylalanine residue maintains high affinity. Comparison of the α-btx complex with that coupled to the agonist epibatidine reveals structural rearrangements within the binding pocket and throughout each subunit. The overall findings high-light structural principles by which α-neurotoxins interact with nicotinic receptors.

One of the key limitations of Molecular Dynamics (MD) simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long time scales. To overcome this bottleneck, we present the REinforcement learning based Adaptive samPling (REAP) algorithm that aims to efficiently sample conformational space by learning the relative importance of each order parameter as it samples the landscape. To achieve this, the algorithm uses concepts from the field of reinforcement learning, a subset of machine learning, which rewards sampling along important degrees of freedom and disregards others that do not facilitate exploration or exploitation. We demonstrate the effectiveness of REAP by comparing the sampling to long continuous MD simulations and least-counts adaptive sampling on two model landscapes (L-shaped and circular) and realistic systems such as alanine dipeptide and Src kinase. In all four systems, the REAP algorithm consistently demonstrates its ability to explore conformational space faster than the other two methods when comparing the expected values of the landscape discovered for a given amount of time. The key advantage of REAP is on-the-fly estimation of the importance of collective variables, which makes it particularly useful for systems with limited structural information.

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