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Chemical reactions that mimic the function of ATP hydrolysis in biochemistry are of current interest in nonequilibrium systems chemistry. The formation of transient bonds from these reactions can drive molecular machines or generate materials with time-dependent properties. While the behavior of these systems can be complicated, the underlying chemistry is often simple: they are therefore potentially interesting topics for undergraduate introductory organic chemistry students, combining state-of-the-art advances in systems chemistry with straightforward reactions. Here, a teaching experiment has been developed that explores the transient assembly of benzoic acid derivatives driven by carbodiimide hydration. Working in teams, students examine the formation and decomposition of anhydrides from two benzoic acids using a carbodiimide “fuel”. The students examine classic reaction kinetics of anhydride hydrolysis using two independent methods, NMR and IR spectroscopies. They then explore how the amount of carbodiimide affects the lifetimes of precipitates of benzoic anhydride as a simple example of out-of-equilibrium self-assembly. 

We characterize perovskite TiF3, a material which is reported to display significant negative thermal expansion at elevated temperatures above its cubic-to-rhombohedral structural phase transition at 330 K. We find the optical response favors an insulating state in both structural phases, which we show can be produced in density functional theory calculations only through the introduction of an on-site Coulomb repulsion. Analysis of the magnetic susceptibility data gives a S = 21 local moment per Ti+3 ion and an antiferromagnetic exchange coupling. Together, these results show that TiF3 is a strongly correlated electron system, a fact which constrains possible mechanisms of strong negative thermal expansion in the Sc1−x Tix F3 system. We consider the relative strength of the Jahn-Teller and electric dipole interactions in driving the structural transition. 

The size and amount of data captured from numerous sources has created a situation where the large quantity of data challenges our ability to understand the meaning within the data. This has motivated studies for mechanized data analysis and in particular for the clustering, or partitioning, of data into related groups. In fact, the size of the data has grown to the point where it is now often necessary to stream the data through the system for online and high speed analysis. This paper explores the application of approximate methods for the stream clustering of high-dimensional data (feature sizes contains 100+ measures). In particular, the algorithm that has been developed, called streamingRPHash, combines Random Projection with Locality Sensitive Hashing and a count-min sketch to implement a high-performance method for the parallel and distributed clustering of streaming data in a MapReduce framework. streamingRPHash is able to perform clustering at a rate much faster than traditional clustering algorithms such as K-Means. streamingRPHash provides clustering results that are only slightly less accurate than K-Means, but in runtimes that are nearly half that required by K-Means. The performance advantage for streamingRPHash becomes even more significant as the dimensionality of the input data stream increases. Furthermore, the experimental results show that streamingRPHash has a near linear speedup relative to the number of CPU cores. This speedup efficiency is possible because the approximate methods used in streamingRPHash allow independent and largely unsynchronized analyses to be performed on each streamed data vectors.