Abstract:The kinetics and uniformity of ion insertion reactions at the solid/liquid interface govern the rate capability and lifetime, respectively, of electrochemical devices such as Li-ion batteries.We develop an operando X-ray microscopy platform that maps the dynamics of the Li composition and insertion rate in LiXFePO4, and show that nanoscale spatial variations in rate and in composition control the lithiation pathway at the sub-particle length scale. Specifically, spatial variations in the insertion rate constant lead to the formation of nonuniform domains, and the composition dependence of the rate constant amplifies nonuniformities during delithiation but suppresses them during lithiation, and moreover stabilizes the solid solution during lithiation. This coupling of lithium composition and surface reaction rates controls the kinetics and uniformity during electrochemical ion insertion.One Sentence Summary: X-ray microscopy reveals the nanoscale evolution of composition and reaction rate inside a Li-ion battery during cycling Main Text: The insertion of a guest ion into the host crystal is the fundamental reaction underpinning insertion electrochemistry and has been applied to store energy (1), tune catalysts (2), and switch optoelectronic properties (3). In Li-ion batteries, for example, Li ions from the 2 liquid electrolyte insert into solid host particles in the electrode. Nanoscale intraparticle electrochemical inhomogeneities in phase and in composition are responsible for mechanical strain and fracture which decrease the reversibility of the reaction (4). Moreover, these nonuniformities make it difficult to correlate current-voltage measurements to microscopic ion insertion mechanisms. Simultaneously quantifying nonuniform nanoscale reaction kinetics and the underlying material composition at the solid-liquid interface holds the key to improving device performance.A gold standard material for investigating ion insertion reactions is LiXFePO4 (0

Block copolymer patterned holey silicon (HS) was successfully integrated into a microdevice for simultaneous measurements of Seebeck coefficient, electrical conductivity, and thermal conductivity of the same HS microribbon. These fully integrated HS microdevices provided excellent platforms for the systematic investigation of thermoelectric transport properties tailored by the dimensions of the periodic hole array, that is, neck and pitch size, and the doping concentrations. Specifically, thermoelectric transport properties of HS with a neck size in the range of 16-34 nm and a fixed pitch size of 60 nm were characterized, and a clear neck size dependency was shown in the doping range of 3.1 × 10(18) to 6.5 × 10(19) cm(-3). At 300 K, thermal conductivity as low as 1.8 ± 0.2 W/mK was found in HS with a neck size of 16 nm, while optimized zT values were shown in HS with a neck size of 24 nm. The controllable effects of holey array dimensions and doping concentrations on HS thermoelectric performance could aid in improving the understanding of the phonon scattering process in a holey structure and also in facilitating the development of silicon-based thermoelectric devices.

Motivation New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity. Results We introduce a highly scalable graph-based clustering algorithm PARC—Phenotyping by Accelerated Refined Community-partitioning—for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis. Availability and implementation https://github.com/ShobiStassen/PARC. Supplementary information Supplementary data are available at Bioinformatics online.

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