Tuning fermi level and band gap in Li4Ti5O12 by doping and vacancy for ultrafast Li+ insertion/extraction

Li₄T_i5O₁₂ (LTO) attracts great interest due to the “zero strain” during cycles but the poor electronic and ionic conductivity critically impede the practical application. Herein, we report a synergy strategy of tuning localized electrons to shift Fermi level and band gap by Mg/Zr co-doping and oxygen vacancy incorporation, which significantly improves Li⁺ and electronic transport. More importantly, the intrinsic synergistic mechanism has been revealed by neutron diffraction, X-ray absorption spectra, and first-principles calculations. The “elastic effect” of lattice induced by Mg/Zr co-doping allows LTO to accommodate more oxygen vacancies to a certain degree without a severe lattice distortion, which largely improves the electronic conductivity. Mg/Zr co-doping and oxygen vacancy incorporation effectively enhanced the dynamic characteristics of LTO electrode, achieving the excellent rate performance (90 mAh/g at 20C) and cycle stability (96.9% after 500 cycles at 10C). First-principles calculations confirm Fermi level shifts to the conduction band, and the band gap becomes narrowed due to the synergistic modulation, and the intrinsic mechanism of the enhanced electronic and Li-ion conductivity is clarified. This study offers some insights into achieving the fast Li⁺ insertion/extraction by tuning the crystal and electronic structure with lattice doping and oxygen vacancy engineering.     

Synthesis of amphiphilic (meth)acrylates with oligo(ethylene glycol) and (or) oligo(propylene glycol) blocks by the esterification of (meth)acrylic acid

Thermoresponsive PEG-based (PEG stands polyethylene glycol) polymers are unique for use in medicine because of their low toxicity, good biocompatibility and biodegradability, but usually more hydrophobic and more toxic comonomers are used to adjust lower critical solution temperature (LCST). A convenient way to overcome this problem and to finely tune LCST is to use alkoxy oligo(ethylene glycol)- or alkoxy oligo(propylene glycol) (meth)acrylates as starting comonomers. Here we report on the conditions for the simple and affordable synthesis of methoxy oligo(propylene glycol) (meth)acrylate- and methoxy oligo(propylene glycol)-block-oligo(ethylene glycol) (meth)acrylate-based macromonomers with high yields (80%–98.7%) by the acid-catalyzed esterification of (meth)acrylic acid with alkoxy oligo(alkylene glycols) containing oligo(ethylene glycol) (OEG) and/or oligo(propylene glycol) (OPG) blocks. p-Toluene sulphonic acid (pTSA), alkyl(C₁₂–C₁₄)benzene sulfonic acid (ABSA) and H₂SO₄ were used as catalysts. It has been shown that pTSA and ABSA are practically the same in catalytic activity and are superior to sulfuric acid. The reaction orders with respect to catalyst was found to be close to 1 in all cases. It has been shown that the reaction is actually insensitive to the lengths of OEG and OPG blocks, as well as to the structure of the terminal alkyl group, while the esterification of acrylic acid (AA) proceeds much faster compared to methacrylic acid (MAA) one under the same conditions. The influence of temperature on the equilibrium conversions of alcohols was determined, which were found to be 89%–93% for the esterification of AA and 61%–86% for MAA in the temperature range of 60–120°C. A further increase in conversion was achieved by introducing an azeotropic agent (toluene), its optimal concentration was found to be 10%–15%.     

Accuracy estimate and optimization techniques for SimRank computation

The measure of similarity between objects is a very useful tool in many areas of computer science, including information retrieval. SimRank is a simple and intuitive measure of this kind, based on a graph-theoretic model. SimRank is typically computed iteratively, in the spirit of PageRank. However, existing work on SimRank lacks accuracy estimation of iterative computation and has discouraging time complexity. In this paper, we present a technique to estimate the accuracy of computing SimRank iteratively. This technique provides a way to find out the number of iterations required to achieve a desired accuracy when computing SimRank. We also present optimization techniques that improve the computational complexity of the iterative algorithm from O(n ⁴) in the worst case to min(O(nl), O(n ³/ log₂ n)), with n denoting the number of objects, and l denoting the number object-to-object relationships. We also introduce a threshold sieving heuristic and its accuracy estimation that further improves the efficiency of the method. As a practical illustration of our techniques, we computed SimRank scores on a subset of English Wikipedia corpus, consisting of the complete set of articles and category links.     

Optimal allocation–consumption problem for a portfolio with an illiquid asset

During financial crises investors manage portfolios with low liquidity, where the paper-value of an asset differs from the price proposed by the buyer. We consider an optimization problem for a portfolio with an illiquid, a risky and a risk-free asset. We work in the Merton's optimal consumption framework with continuous time. The liquid part of the investment is described by a standard Black–Scholes market. The illiquid asset is sold at a random moment with prescribed distribution and generates additional liquid wealth dependent on its paper-value. The investor has a hyperbolic absolute risk aversion also denoted as HARA-type utility function, in particular, the logarithmic utility function as a limit case. We study two different distributions of the liquidation time of the illiquid asset – a classical exponential distribution and a more practically relevant Weibull distribution. Under certain conditions we show the smoothness of the viscosity solution and obtain closed formulae relevant for numerics.     

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.     

From Autonomy to Cooperative Traded Control of Humanoid Manipulation Tasks with Unreliable Communication

In this paper, we present our system design, operational procedure, testing process, field results, and lessons learned for the valve-turning task of the DARPA Robotics Challenge (DRC). We present a software framework for cooperative traded control that enables a team of operators to control a remote humanoid robot over an unreliable communication link. Our system, composed of software modules running on-board the robot and on a remote workstation, allows the operators to specify the manipulation task in a straightforward manner. In addition, we have defined an operational procedure for the operators to manage the teleoperation task, designed to improve situation awareness and expedite task completion. Our testing process, consisting of hands-on intensive testing, remote testing, and remote practice runs , demonstrates that our framework is able to perform reliably and is resilient to unreliable network conditions. We analyze our approach, field tests, and experience at the DRC Trials and discuss lessons learned which may be useful for others when designing similar systems.