Operando spectral imaging of the lithium ion battery's solid-electrolyte interphase.

The lithium-ion battery is currently the preferred power source for applications ranging from smart phones to electric vehicles. Imaging the chemical reactions governing its function as they happen, with nanoscale spatial resolution and chemical specificity, is a long-standing open problem. Here, we demonstrate operando spectrum imaging of a Li-ion battery anode over multiple charge-discharge cycles using electron energy-loss spectroscopy (EELS) in a scanning transmission electron microscope (STEM). Using ultrathin Li-ion cells, we acquire reference EELS spectra for the various constituents of the solid-electrolyte interphase (SEI) layer and then apply these "chemical fingerprints" to high-resolution, real-space mapping of the corresponding physical structures. We observe the growth of Li and LiH dendrites in the SEI and fingerprint the SEI itself. High spatial- and spectral-resolution operando imaging of the air-sensitive liquid chemistries of the Li-ion cell opens a direct route to understanding the complex, dynamic mechanisms that affect battery safety, capacity, and lifetime.

Electron-Transparent Thermoelectric Coolers Demonstrated with Nanoparticle and Condensation Thermometry.

More efficient thermoelectric devices would revolutionize refrigeration and energy production, and low-dimensional thermoelectric materials are predicted to be more efficient than their bulk counterparts. But nanoscale thermoelectric devices generate thermal gradients on length scales that are too small to resolve with traditional thermometry methods. Here we fabricate, using single-crystal bismuth telluride (Bi2Te3) and antimony/bismuth telluride (Sb2-xBixTe3) flakes exfoliated from commercially available bulk materials, functional thermoelectric coolers (TECs) that are only 100 nm thick. These devices are the smallest TECs ever demonstrated by a factor of 104. After depositing indium nanoparticles to serve as nanothermometers, we measure the heating and cooling produced by the devices with plasmon energy expansion thermometry (PEET), a high-spatial-resolution, transmission electron microscopy (TEM)-based thermometry technique, demonstrating a ΔT = -21 ± 4 K from room temperature. We also establish proof-of-concept for condensation thermometry, a quantitative temperature-change mapping technique with a spatial precision of ≲300 nm.

Irreversibility at macromolecular scales in the flake graphite of the lithium-ion battery anode.

Charging a commercial lithium-ion battery intercalates lithium into the graphite-based anode, creating various lithium carbide structures. Despite their economic importance, these structures and the dynamics of their charging-discharging transitions are not well-understood. We have videoed single microcrystals of high-quality, natural graphite undergoing multiple lithiation-delithiation cycles. Because the equilibrium lithium-carbide compounds corresponding to full, half, and one-third charge are gold, red, and blue respectively, video observations give direct insight into both the macromolecular structures and the kinematics of charging and discharging. We find that the transport during the first lithiation is slow and orderly, and follows the core-shell or shrinking annuli model with phase boundaries moving at constant velocities (i.e. non-diffusively). Subsequent lithiations are markedly different, showing transport that is both faster and disorderly, which indicates that the initially pristine graphite is irreversibly and considerably altered during the first cycle. In all cases deintercalation is not the time-reverse of intercalation. These findings both illustrate how lithium enters nearly defect-free host material, and highlight the differences between the idealized case and an actual, cycling graphite anode.

Unfolding the band structure of non-crystalline photonic band gap materials.

Non-crystalline photonic band gap (PBG) materials have received increasing attention, and sizeable PBGs have been reported in quasi-crystalline structures and, more recently, in disordered structures. Band structure calculations for periodic structures produce accurate dispersion relations, which determine group velocities, dispersion, density of states and iso-frequency surfaces, and are used to predict a wide-range of optical phenomena including light propagation, excited-state decay rates, temporal broadening or compression of ultrashort pulses and complex refraction phenomena. However, band calculations for non-periodic structures employ large super-cells of hundreds to thousands building blocks, and provide little useful information other than the PBG central frequency and width. Using stereolithography, we construct cm-scale disordered PBG materials and perform microwave transmission measurements, as well as finite-difference time-domain (FDTD) simulations. The photonic dispersion relations are reconstructed from the measured and simulated phase data. Our results demonstrate the existence of sizeable PBGs in these disordered structures and provide detailed information of the effective band diagrams, dispersion relation, iso-frequency contours, and their angular dependence. Slow light phenomena are also observed in these structures near gap frequencies. This study introduces a powerful tool to investigate photonic properties of non-crystalline structures and provides important effective dispersion information, otherwise difficult to obtain.

A data-mining approach for multiple structural alignment of proteins.

Comparing the 3D structures of proteins is an important but computationally hard problem in bioinformatics. In this paper, we propose studying the problem when much less information or assumptions are available. We model the structural alignment of proteins as a combinatorial problem. In the problem, each protein is simply a set of points in the 3D space, without sequence order information, and the objective is to discover all large enough alignments for any subset of the input. We propose a data-mining approach for this problem. We first perform geometric hashing of the structures such that points with similar locations in the 3D space are hashed into the same bin in the hash table. The novelty is that we consider each bin as a coincidence group and mine for frequent patterns, which is a well-studied technique in data mining. We observe that these frequent patterns are already potentially large alignments. Then a simple heuristic is used to extend the alignments if possible. We implemented the algorithm and tested it using real protein structures. The results were compared with existing tools. They showed that the algorithm is capable of finding conserved substructures that do not preserve sequence order, especially those existing in protein interfaces. The algorithm can also identify conserved substructures of functionally similar structures within a mixture with dissimilar ones. The running time of the program was smaller or comparable to that of the existing tools.

Reconstructing an ultrametric galled phylogenetic network from a distance matrix.

Given a distance matrix M that specifies the pairwise evolutionary distances between n species, the phylogenetic tree reconstruction problem asks for an edge-weighted phylogenetic tree that satisfies M, if one exists. We study some extensions of this problem to rooted phylogenetic networks. Our main result is an O(n(2) log n)-time algorithm for determining whether there is an ultrametric galled network that satisfies M, and if so, constructing one. In fact, if such an ultrametric galled network exists, our algorithm is guaranteed to construct one containing the minimum possible number of nodes with more than one parent (hybrid nodes). We also prove that finding a largest possible submatrix M' of M such that there exists an ultrametric galled network that satisfies M' is NP-hard. Furthermore, we show that given an incomplete distance matrix (i.e. where some matrix entries are missing), it is also NP-hard to determine whether there exists an ultrametric galled network which satisfies it.