A kinetic investigation of the reaction of ethylenedinitrilotetraacetic acid (EDTA) and cerium (IV) in acid solution

The reaction between Ce(IV) salt solutions and EDTA solution was followed titrimetrically and spectrophotometrically and found to occur in stages. Four equivalents of Ce(IV) are reduced per mole of EDTA almost instantaneously at room temperature or even lower. With increasing temperature and reaction time an ultimate of about 14 equivalents of Ce(IV) is consumer per mole of EDTA. Formaldehyde, carbon dioxide, and other unidentified compounds are the products of oxidation of EDTA with Ce(IV). The kinetics of the reaction in aqueous sulfuric acid (0.09 - 2.95 M̲) was studied over the temperature range 11.7-40⁰ by a spectrophotometric technique. In the region of concentrations workable in UV spectrophotometry (10⁻⁵ -- 10⁻⁴ M̲ Ce(IV)), and where one mole of EDTA reduced four moles of Ce(IV), the reaction is first order in each reactant and shows variable dependence on the concentration of hydrogen ions. The reaction rate is a maximum at about 1 M̲ [H⁺] and decreases on either side. Only below unit molar concentration of H⁺ a Ce(IV)-EDTA complex is observed to form and decay --Abstract, page ii

Rutile TiO2 films as electron transport layer in inverted organic solar cell

Titanium dioxide (TiO2) thin films were prepared by sol–gel spin coating method and deposited on ITO-coated glass substrates. The effects of different heat treatment annealing temperatures on the phase composition of TiO2 films and its effect on the optical band gap, morphological, structural as well as using these layers in P3HT:PCBM-based organic solar cell were examined. The results show the presence of rutile phases in the TiO2 films which were heat-treated for 2 h at different temperatures (200, 300, 400, 500 and 600 °C). The optical properties of the TiO2 films have altered by temperature with a slight decrease in the transmittance intensity in the visible region with increasing the temperature. The optical band gap values were found to be in the range of 3.28–3.59 eV for the forbidden direct electronic transition and 3.40–3.79 eV for the allowed direct transition. TiO2 layers were used as electron transport layer in inverted organic solar cells and resulted in a power conversion efficiency of 1.59% with short circuit current density of 6.64 mA cm−2 for TiO2 layer heat-treated at 600 °C

Leakage Current Analysis for Diagnosis of Bridge Defects in Power-Gating Designs

Manufacturing defects that do not affect the functional operation of low power Integrated Circuits (ICs) can nevertheless impact their power saving capability. We show that stuck-ON faults on the power switches and resistive bridges between the power networks can impair the power saving capability of power-gating designs. For quantifying the impact of such faults on the power savings of power-gating designs, we propose a diagnosis technique that targets bridges between the power networks. The proposed technique is based on the static power analysis of a power-gating design in stand-by mode and it utilizes a novel on-chip signature generation unit, which is sensitive to the voltage level between power rails, the measurements of which are processed off-line for the diagnosis of bridges that can adversely affect power savings. We explore, through SPICE simulation of the largest IWLS’05 benchmarks synthesised using a 32 nm CMOS technology, the trade-offs achieved by the proposed technique between diagnosis accuracy and area cost and we evaluate its robustness against process variation. The proposed technique achieves a diagnosis resolution that is higher than 98.6% and 97.9% for bridges of R ≳ 10MΩ(weak bridges) and bridges of R ≲ 10MΩ (strong bridges), respectively, and a diagnosis accuracy higher than 94.5% for all the examined defects. The area overhead is small and scalable: it is found to be 1.8% and 0.3% for designs with 27K and 157K gate equivalents, respectively

Adaptive energy minimization of OpenMP parallel applications on many-core systems

Energy minimization of parallel applications is an emerging challenge for current and future generations of many-core computing systems. In this paper, we propose a novel and scalable energy minimization approach that suitably applies DVFS in the sequential part and jointly considers DVFS and dynamic core allocations in the parallel part. Fundamental to this approach is an iterative learning based control algorithm that adapt the voltage/frequency scaling and core allocations dynamically based on workload predictions and is guided by the CPU performance counters at regular intervals. The adaptation is facilitated through performance annotations in the application codes, defined in a modified OpenMP runtime library. The proposed approach is validated on an Intel Xeon E5-2630 platform with up to 24 CPUs running NAS parallel benchmark applications. We show that our proposed approach can effectively adapt to different architecture and core allocations and minimize energy consumption by up to 17% compared to the existing approaches for a given performance requirement

Thermochemistry and Kinetics of the Thermal Degradation of 2-Methoxyethanol as Possible Biofuel Additives

Oxygenated organic compounds derived from biomass (biofuel) are a promising alternative renewable energy resource. Alcohols are widely used as biofuels, but studies on bifunctional alcohols are still limited. This work investigates the unimolecular thermal degradation of 2-methoxyethanol (2ME) using DFT/BMK and ab initio (CBS-QB3 and G3) methods. Enthalpies of the formation of 2ME and its decomposition species have been calculated. Conventional transition state theory has been used to estimate the rate constant of the pyrolysis of 2ME over a temperature range of 298–2000 K. Production of methoxyethene via 1,3-H atom transfer represents the most kinetically favored path in the course of 2ME pyrolysis at room temperature and requires less energy than the weakest C α − C β simple bond fission. Thermodynamically, the most preferred channel is methane and glycoladhyde formation. A ninefold frequency factor gives a superiority of the C α − C β bond breaking over the C γ − O β bond fission despite comparable activation energies of these two processes. © 2019, The Author(s).Scopu

Learning-based runtime management of energy-efficient and reliable many-core systems

This paper highlights and demonstrates our research works to date addressing the energy-efficiency and reliability challenges of many-core systems through intelligent runtime management algorithms. The algorithms are implemented through cross-layer interactions between the three layers: application, runtime and hardware, forming our core theme of working together. The annotated application tasks communicate the performance, energy or reliability requirements to the runtime. With such requirements, the runtime exercises the hardware through various control knobs and gets the feedback of these controls through the performance monitors. The aim is to learn the best possible hardware controls during runtime to achieve energy-efficiency and improved reliability, while meeting the specified application requirements

Thermal-aware adaptive energy minimization of open MP parallel applications

Energy minimization of parallel applications considering thermal distributions among the processor cores is an emerging challenge for current and future generations of many-core computing systems. This paper proposes an adaptive energy minimization approach that hierarchically applies dynamic voltage\slash frequency scaling (DVFS), thread-to-core affinity and dynamic concurrency controls (DCT) to address this challenge. The aim is to minimize the energy consumption and achieve balanced thermal distributions among cores, thereby improving the lifetime reliability of the system, while meeting a specified power budget requirement. Fundamental to this approach is an iterative learning-based control algorithm that adapts the VFS and core allocations dynamically based on the CPU workloads and thermal distributions of the processor cores, guided by the CPU performance counters at regular intervals. The adaptation is facilitated through modified OpenMP library-based power budget annotations. The proposed approach is extensively validated on an Intel Xeon E5-2630 platform with up to 12 CPUs running NAS parallel benchmark applications

Computational Studies on the Thermodynamic and Kinetic Parameters of Oxidation of 2-Methoxyethanol Biofuel via H-Atom Abstraction by Methyl Radical

In this work, a theoretical investigation of thermochemistry and kinetics of the oxidation of bifunctional 2-Methoxyethanol (2ME) biofuel using methyl radical was introduced. Potential-energy surface for various channels for the oxidation of 2ME was studied at density function theory (M06-2X) and ab initio CBS-QB3 levels of theory. H-atom abstraction reactions, which are essential processes occurring in the initial stages of the combustion or oxidation of organic compounds, from different sites of 2ME were examined. A similar study was conducted for the isoelectronic n-butanol to highlight the consequences of replacing the ϒ CH2 group by an oxygen atom on the thermodynamic and kinetic parameters of the oxidation processes. Rate coefficients were calculated from the transition state theory. Our calculations show that energy barriers for n-butanol oxidation increase in the order of α ‹ O ‹ ϒ ‹ β ‹ ξ, which are consistent with previous data. However, for 2ME the energy barriers increase in the order α ‹ β ‹ ξ ‹ O. At elevated temperatures, a slightly high total abstraction rate is observed for the bifunctional 2ME (4 abstraction positions) over n-butanol (5 abstraction positions). © 2019, The Author(s).Scopu

Learning transfer-based adaptive energy minimization in embedded systems

Embedded systems execute applications with different performance requirements. These applications exercise the hardware differently depending on the types of computation being carried out, generating varying workloads with time. We will demonstrate that energy minimization with such workload and performance variations within (intra) and across (inter) applications is particularly challenging. To address this challenge we propose an online energy minimization approach, capable of minimizing energy through adaptation to these variations. At the core of the approach is an initial learning through reinforcement learning algorithm that suitably selects the appropriate voltage/frequency scalings (VFS) based on workload predictions to meet the applications’ performance requirements. The adaptation is then facilitated and expedited through learning transfer, which uses the interaction between the system application, runtime and hardware layers to adjust the power control levers. The proposed approach is implemented as a power governor in Linux and validated on an ARM Cortex-A8 running different benchmark applications. We show that with intra- and inter-application variations, our proposed approach can effectively minimize energy consumption by up to 33% compared to existing approaches. Scaling the approach further to multi-core systems, we also show that it can minimize energy by up to 18% with 2X reduction in the learning time when compared with a recently reported approach