University of Arkansas

About: University of Arkansas is a education organization based out in Fayetteville, Arkansas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 17225 authors who have published 33329 publications receiving 941102 citations. The organization is also known as: Arkansas & UA.

High-energy lithium metal pouch cells with limited anode swelling and long stable cycles

Abstract: Lithium metal anodes have attracted much attention as candidates for high-energy batteries, but there have been few reports of long cycling behaviour, and the degradation mechanism of realistic high-energy Li metal cells remains unclear. Here, we develop a prototypical 300 Wh kg−1 (1.0 Ah) pouch cell by integrating a Li metal anode, a LiNi0.6Mn0.2Co0.2O2 cathode and a compatible electrolyte. Under small uniform external pressure, the cell undergoes 200 cycles with 86% capacity retention and 83% energy retention. In the initial 50 cycles, flat Li foil converts into large Li particles that are entangled in the solid-electrolyte interphase, which leads to rapid volume expansion of the anode (cell thickening of 48%). As cycling continues, the external pressure helps the Li anode maintain good contact between the Li particles, which ensures a conducting percolation pathway for both ions and electrons, and thus the electrochemical reactions continue to occur. Accordingly, the solid Li particles evolve into a porous structure, which manifests in substantially reduced cell swelling by 19% in the subsequent 150 cycles. Much has been said about the high-energy, long-lasting potential of Li metal batteries, and yet little has been demonstrated at the cell scale. Here, Jun Liu and colleagues demonstrate a Li metal pouch cell with a 300 Wh kg−1 energy density and a 200-cycle lifetime.

The chandra deep field-south survey: 7 ms source catalogs

Abstract: We present X-ray source catalogs for the ≈7 Ms exposure of the Chandra Deep Field-South (CDF-S), which covers a total area of 484.2 arcmin2. Utilizing wavdetect for initial source detection and ACIS Extract for photometric extraction and significance assessment, we create a main source catalog containing 1008 sources that are detected in up to three X-ray bands: 0.5-7.0 keV, 0.5-2.0 keV, and 2-7 keV. A supplementary source catalog is also provided, including 47 lower-significance sources that have bright (Ks ≤ 23) near-infrared counterparts. We identify multiwavelength counterparts for 992 (98.4%) of the main-catalog sources, and we collect redshifts for 986 of these sources, including 653 spectroscopic redshifts and 333 photometric redshifts. Based on the X-ray and multiwavelength properties, we identify 711 active galactic nuclei (AGNs) from the main-catalog sources. Compared to the previous ≈4 Ms CDF-S catalogs, 291 of the main-catalog sources are new detections. We have achieved unprecedented X-ray sensitivity with average flux limits over the central ≈1 arcmin2 region of ≈1.9 ×10-17, 6.4 ×10-18, and 2.7 ×10-17 erg cm-2 s-1 in the three X-ray bands, respectively. We provide cumulative number-count measurements observing, for the first time, that normal galaxies start to dominate the X-ray source population at the faintest 0.5-2.0 keV flux levels. The highest X-ray source density reaches ≈50,500 deg-2, and 47% ± 4% of these sources are AGNs (≈23,900 deg-2). (Less)

Expectation disconfirmation and technology adoption: polynomial modeling and response surface analysis

Abstract: Individual-level information systems adoption research has recently seen the introduction of expectation-disconfirmation theory (EDT) to explain how and why user reactions change over time. This prior research has produced valuable insights into the phenomenon of technology adoption beyond traditional models, such as the technology acceptance model. First, we identify gaps in EDT research that present potential opportunities for advances-specifically, we discuss methodological and analytical limitations in EDT research in information systems and present polynomial modeling and response surface methodology as solutions. Second, we draw from research on cognitive dissonance, realistic job preview, and prospect theory to present a polynomial model of expectation-disconfirmation in information systems. Finally, we test our model using data gathered over a period of 6 months among 1,143 employees being introduced to a new technology. The results confirmed our hypotheses that disconfirmation in general was bad, as evidenced by low behavioral intention to continue using a system for both positive and negative disconfirmation, thus supporting the need for a polynomial model to understand expectation disconfirmation in information systems.

Learning through ferroelectric domain dynamics in solid-state synapses.

Abstract: In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.