Energy storage is more important today than at any time in human history. Future generations of rechargeable lithium batteries are required to power portable electronic devices (cellphones, laptop computers etc.), store electricity from renewable sources, and as a vital component in new hybrid electric vehicles. To achieve the increase in energy and power density essential to meet the future challenges of energy storage, new materials chemistry, and especially new nanomaterials chemistry, is essential. We must find ways of synthesizing new nanomaterials with new properties or combinations of properties, for use as electrodes and electrolytes in lithium batteries. Herein we review some of the recent scientific advances in nanomaterials, and especially in nanostructured materials, for rechargeable lithium-ion batteries.

Nanomaterials for rechargeable lithium batteries

Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dimensionality curse, referred to as hubness, that affects the distribution of k-occurrences: the number of times a point appears among the k nearest neighbors of other points in a data set. Through theoretical and empirical analysis involving synthetic and real data sets we show that under commonly used assumptions this distribution becomes considerably skewed as dimensionality increases, causing the emergence of hubs, that is, points with very high k-occurrences which effectively represent "popular" nearest neighbors. We examine the origins of this phenomenon, showing that it is an inherent property of data distributions in high-dimensional vector space, discuss its interaction with dimensionality reduction, and explore its influence on a wide range of machine-learning tasks directly or indirectly based on measuring distances, belonging to supervised, semi-supervised, and unsupervised learning families.

/pdf/hubs-in-space-popular-nearest-neighbors-in-high-dimensional-5vcno8gf0x.pdf

Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data

The strength of lightweight mechanical metamaterials, which aim to exploit material-strengthening size effects by their microscale lattice structure, has been limited by the resolution of three-dimensional lithography technologies and their restriction to mainly polymer resins. Here, we demonstrate that pyrolysis of polymeric microlattices can overcome these limitations and create ultra-strong glassy carbon nanolattices with single struts shorter than 1 μm and diameters as small as 200 nm. They represent the smallest lattice structures yet produced--achieved by an 80% shrinkage of the polymer during pyrolysis--and exhibit material strengths of up to 3 GPa, corresponding approximately to the theoretical strength of glassy carbon. The strength-to-density ratios of the nanolattices are six times higher than those of reported microlattices. With a honeycomb topology, effective strengths of 1.2 GPa at 0.6 g cm(-3) are achieved. Diamond is the only bulk material with a notably higher strength-to-density ratio.

Approaching theoretical strength in glassy carbon nanolattices.

Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outliers have to be detected from data streams that arrive at an enormous pace. In this paper, an incremental LOF (local outlier factor) algorithm, appropriate for detecting outliers in data streams, is proposed. The proposed incremental LOF algorithm provides equivalent detection performance as the iterated static LOF algorithm (applied after insertion of each data record), while requiring significantly less computational time. In addition, the incremental LOF algorithm also dynamically updates the profiles of data points. This is a very important property, since data profiles may change over time. The paper provides theoretical evidence that insertion of a new data point as well as deletion of an old data point influence only limited number of their closest neighbors and thus the number of updates per such insertion/deletion does not depend on the total number of points TV in the data set. Our experiments performed on several simulated and real life data sets have demonstrated that the proposed incremental LOF algorithm is computationally efficient, while at the same time very successful in detecting outliers and changes of distributional behavior in various data stream applications

https://cis.temple.edu/~latecki/Papers/CIDM07.pdf

Incremental Local Outlier Detection for Data Streams

Materials processing for lithium-ion batteries

Reverse Nearest Neighbor (RNN) queries are of particular interest in a wide range of applications such as decision support systems, profile based marketing, data streaming, document databases, and bioinformatics. The earlier approaches to solve this problem mostly deal with two dimensional data. However most of the above applications inherently involve high dimensions and high dimensional RNN problem is still unexplored. In this paper, we propose an approximate solution to answer RNN queries in high dimensions. Our approach is based on the strong correlation in practice between k-NN and RNN. It works in two phases. In the first phase the k-NN of a query point is found and in the next phase they are further analyzed using a novel type of query Boolean Range Query (BRQ). Experimental results show that BRQ is much more efficient than both NN and range queries, and can be effectively used to answer RNN queries. Performance is further improved by running multiple BRQ simultaneously. The proposed approach can also be used to answer other variants of RNN queries such as RNN of order k, bichromatic RNN, and Matching Query which has many applications of its own. Our technique can efficiently answer NN, RNN, and its variants with approximately same number of I/O as running a NN query.

/pdf/high-dimensional-reverse-nearest-neighbor-queries-1unokvjmkp.pdf

High dimensional reverse nearest neighbor queries

Carbon structures were fabricated by the pyrolysis of photopatterned negative photoresists (SU-8 and photosensitive polyimide) on silicon and fused silica wafers. Results here are compared with those of positive resists published earlier by this group. Negative resist films need exposure to ultraviolet light prior to pyrolysis to produce carbon films. The pyrolysis was carried out in a closed quartz tube furnace in a forming gas (95% N 2 , 5% H 2 ) atmosphere. The pyrolysis process was characterized using a combination of thermogravimetric analysis and differential thermal analysis. The pyrolysis of SU-8 involved gas evolution in a narower range of temperature than polyimide, The adhesion of the carbon film was found to depend on the resist, the substrate, and the heating cycle used. The carbon structures were characterized in terms of their shrinkage during the pyrolysis, the resistivity, the degree of crystallinity and the peak separation in cyclic voltammetry. Carbons derived from pyrolysis of negative resists showed higher resistivity, vertical shrinkage, and peak-to-peak separation voltage than positive resists. Transmission electron microscope results showed a distinct lack of crystallinity even after pyrolysis at 1100°C, unlike the positive resist derived carbon.

/pdf/pyrolysis-of-negative-photoresists-to-fabricate-carbon-56fodmm28e.pdf

Amit Singh

Papers

High dimensional reverse nearest neighbor queries

Pyrolysis of Negative Photoresists to Fabricate Carbon Structures for Microelectromechanical Systems and Electrochemical Applications