133 research outputs found
Cobalt Spinel Nanocubes on N-Doped Graphene: A Synergistic Hybrid Electrocatalyst for the Highly Selective Reduction of Carbon Dioxide to Formic Acid
No full textAccelerating Population Balance Model - based particulate process simulations via parallel computing
No full textThe use of Population Balance Models (PBM) for simulating dynamics of particulate systems are inevitably limited at some point by the demands they place on computational resources. PBMs are widely used to describe the time evolutions and distributions of many industrial particulate processes, and its efficient and quick simulation would certainly be beneficial for process design, control and optimization. This thesis is an elucidation of how MATLAB's Parallel Computing Toolbox (PCT), a third-party toolbox called JACKET, and the MATLAB Distributed Computing Server (MDCS) may be combined with algorithmic modification of the PBM to speed up these computations on a CPU (Central Processing Unit), GPU (Graphics Processing Unit) and a computer cluster respectively. Parallel algorithms were developed for three dimensional and four dimensional population balance models incorporating hardware class-specific parallel constructs such as SPMD and gfor. Results indicate significant reduction in computational time without compromising numerical accuracy for all cases except for the GPU. The GPU seemed promising for larger problems despite its limitations of lower clock speeds and on-board memory compared to the CPU. Evaluations of the speedup and scalability further affirm the algorithms' performance.M.S.Includes bibliographical referencesIncludes vitaby Anuj Varghese Prakas
Zn Redistribution and Volatility in ZnZrOx Catalysts for CO2 Hydrogenation
No full textZnO–ZrO2 mixed oxide (ZnZrOx) catalysts are widely studied as selective catalysts for CO2 hydrogenation into methanol at high-temperature conditions (300–350 °C) that are preferred for the subsequent in situ zeolite-catalyzed conversion of methanol into hydrocarbons in a tandem process. Zn, a key ingredient of these mixed oxide catalysts, is known to volatilize from ZnO under high-temperature conditions, but little is known about Zn mobility and volatility in mixed oxides. Here, an array of ex situ and in situ characterization techniques (scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDX), transmission electron microscopy (TEM), powder X-ray diffraction (PXRD), X-ray absorption spectroscopy (XAS), X-ray photoelectron spectroscopy (XPS), Infrared (IR)) was used to reveal that Zn2+ species are mobile between the solid solution phase with ZrO2 and segregated and/or embedded ZnO clusters. Upon reductive heat treatments, partially reversible ZnO cluster growth was observed above 250 °C and eventual Zn evaporation above 550 °C. Extensive Zn evaporation leads to catalyst deactivation and methanol selectivity decline in CO2 hydrogenation. These findings extend the fundamental knowledge of Zn-containing mixed oxide catalysts and are highly relevant for the CO2-to-hydrocarbon process optimization.publishedVersio
Quantum transport in graphene nanotransistors
No full textOver the past decade, interest in using graphene in condensed-matter physics and materials science applications has exploded, owing to its unique electrical properties. Narrow strips of graphene, called graphene nanoribbons, also display exotic behavior. A nanoribbon’s edge geometry determines its electronic transport properties, and the rich behavior
of conductance of nanoribbons in response to external potentials makes them ideal for use within transistors.
In this thesis, we work towards creating an accurate model of graphene nanoribbon transistors, and we asses two possible applications which exploit their amazing potential. We begin by outlining the basic theoretical and computational framework for the model developed in this work. We then demonstrate the capability of graphene nanoribbon transistors, with nanopores, to electronically detect, characterize, and manipulate translocating DNA
strands. Specifically, we explore the tunability of such devices, by examining the role of lattice geometry, such as a quantum point contact constriction, on their performance. We perform a demonstration of the ability to detect the passage of double and single-stranded
DNA, through molecular dynamics simulations. The transistors presented are capable of sensing the helical shape of double-stranded DNA molecules, the unraveling of a DNA helix into a planar-zipper form, and the passage of individual nucleotides of a single strand of DNA
through the nanopore. We outline a preliminary analysis on the proper design of a multilayer transistor stack to control both the electronic properties of the conducting membrane, as well as the motion of the DNA. Lastly, we present another type of nanoribbon device,
an all-carbon spintronic transistor for use in cascaded logic circuits. A thorough analysis of the transport properties of zigzag nanoribbon transistors in magnetic fields, in addition to the design and construction of logic gate circuits containing these spintronic transistors, is presented.Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-05-01The student, Anuj Girdhar, accepted the attached license on 2015-04-18 at 16:01.The student, Anuj Girdhar, submitted this Dissertation for approval on 2015-04-18 at 16:02.This Dissertation was approved for publication on 2015-04-24 at 10:12.DSpace SAF Submission Ingestion Package generated from Vireo submission #7936 on 2015-07-22 at 14:18:16Made available in DSpace on 2015-07-22T22:33:35Z (GMT). No. of bitstreams: 2
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Previous issue date: 2015-04-24Embargo set by: Seth Robbins for item 79875
Lift date: 2017-07-22T22:34:16Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 79875 on 2017-07-23T09:15:20Z
Enhanced Oil Recovery using Carbonated Polymeric Nanofluids : A New Age Approach to CO2 Utilization and Corrosion Mitigation
No full textThe author would like to thank the CIF facility and its staff, including Mr. Anuj Prajapati, Mr. Zahoor Alam, and Mr. Brijesh. Thanks are also extended to all the members associated with the work.Peer reviewe
Probing the Ni(OH) Precursor for LiNiO at the Atomic Scale: Insights into the Origin of Structural Defect in a Layered Cathode Active Material
No full textEffects of sintering additives on defect chemistry and hydration of BaZr0.4Ce0.4(Y,Yb)0.2O3−δ proton conducting electrolytes
No full text[EN] The effects of NiO, ZnO, and CuO sintering additives (0.5, 1.0, or 2.0 wt%) on the sintering behaviour, effective acceptor concentration, and hydration thermodynamics are examined for BaZr0.4Ce0.4Y0.1Yb0.1O3_delta and BaZr0.4Ce0.4Y0.2O3_delta proton conducting electrolytes. Thermogravimetry of hydration shows that the sintering additives - except for 0.5 wt% CuO - lead to a decrease in the effective acceptor concentration, and the decrease per mole sintering additive is the largest for NiO. The absence of typical secondary phases such as BaY2NiO5 and the homogeneous distribution of Ni determined from elemental mapping imply that sintering additives dissolve into the perovskite lattice. Exsolution of metallic Ni and Cu upon reduction leads to a substantial recovery of the effective acceptor concentration. Subsequent oxidation is accompanied by a repeated decrease in the effective acceptor concentration as the sintering additives appear to re-dissolve. Defect chemical reactions are proposed to explain the observed results and these are supported by energetics from first principles calculations. Overall, NiO has the highest positive impact on densification and grain growth, and a relatively small amount of 0.5 wt% NiO or CuO is preferable to optimise both sintering and hydration.This study has received European Union's Horizon 2020-Societal Challenges Research and Innovation funding under grant agreement No 838077 (eCOCO2 project). The Research Council of Norway is acknowledged for computational resources provided through the Uninett Sigma2 project nn4604k and for support to the Norwegian Center for Transmission Electron Microscopy (NORTEM) national infrastructure project 197405.r Center for Transmission Electron Microscopy (NORTEM) national infrastructure project 197405.Dayaghi, AM.; Polfus, JM.; Strandbakke, R.; Pokle, A.; Almar-Liante, L.; Escolástico Rozalén, S.; Vollestad, E.... (2023). Effects of sintering additives on defect chemistry and hydration of BaZr0.4Ce0.4(Y,Yb)0.2O3-δ proton conducting electrolytes. Solid State Ionics. 401. https://doi.org/10.1016/j.ssi.2023.116355S40
The Impact of bioenergy on the EU energy security: A comprehensive analysis
No full textBioenergy has received less attention in recent times due to its changing negative image. For the EU, bioenergy was considered mostly for its contribution to greening and diversifying its energy mix. While its impact on CO2 emissions is hotly debated, there is a dearth of studies regarding the energy security implications of bioenergy in the EU. This opens up a window of opportunities and challenges to perform such a study. The main research question of this thesis is:What has been the impact of bioenergy on the energy security for the EU from 2000 to 2018?The main research question and the sub-research questions are addressed in the context of the EU economy and its vision for climate mitigation. The thesis starts with the literature review of energy security leading up to framing a new theoretical framework. Past implications of bioenergy that includes biofuels in the last decade and a half since 2000 have been analyzed. The thesis has assessed what bioenergy has delivered in that time. Positive and negative developments are taken into account and analyzed with respect to the selected energy security indicators and metrics.Both ES literature and PEST tool have been employed to narrow down the data gathering and relevance of energy security indicators for bioenergy. The use of the PEST analysis tool also offers a unique combination of elements in the framework and a boarder categorization, which in traditional energy security is often not employed.To get a better overview of EU bioenergy, a review of the current policy of the EU for bioenergy has been done. It presents all relevant data related to bioenergy like various bioenergy potential, total energy consumption, and demand, etc. Then, different characteristics and features of bioenergy are described along with the various conversion routes to obtain different forms of energy (namely biofuels, bio-electricity, and bio-heat) are explained. Some of the projections for 2020 for bioenergy in the EU have also been provided to better gauge the state of availability of resources and likely policy direction taken in the future. The effects on the energy security indicators of bioenergy in the EU have been derived from the data on EU bioenergy and is assessed with respect to the 22 selected energy security indicators and metrics from the analytical framework. A rating of 3.0 here implies no or a little effect for energy security indicator or metric on account of bioenergy, anything above 3.0 is positive, and below 3.0 implies a negative impact. It is found that the overall effects have been slightly positive with an average rating of 3.09 for all the 22 selected ES indicators. With 3.5, for the dimension of ‘technology development and efficiency’, it has a positive effect implying an increase in employment in the bio-sector and high research budget for bioenergy from 2000-2018. However, with a score of 2.9 for the dimension of ‘Environmental & social sustainability’, the effects have been overall negative, implying a negative impact on the environment in the last 18 years.Electrical Engineering | Sustainable Energy Technolog
TRIDENT: Transductive Variational Inference of Decoupled Latent Variables for Few Shot Classification
No full textThe versatility to learn from a handful of samples is the hallmark of human intelligence. Few-shot learning is an endeavour to transcend this capability down to machines. Inspired by the promise and power of probabilistic deep learning, we propose a novel variational inference network for few-shot classification (coined as TRIDENT) to decouple the representation of an image into semantic and label latent variables, and simultaneously infer them in an intertwined fashion. To induce task-awareness, as part of the inference mechanics of TRIDENT, we exploit information across both query and support images of a few-shot task using a novel built-in attention-based transductive feature extraction module (we call AttFEX). Our extensive experimental results corroborate the efficacy of TRIDENT and demonstrate that, using the simplest of backbones, it sets a new state-of-the-art in the most commonly adopted datasets miniImageNet and tieredImageNet (offering up to 4% and 5% improvements, respectively), as well as for the recent challenging cross-domain miniImagenet --> CUB scenario offering a significant margin (up to 20% improvement) beyond the best existing cross-domain baselines.Computer Science | Bioinformatic
Uncertainty quantification of the lifetime of self-healing thermal barrier coatings based on surrogate modelling of thermal cyclic fracture and healing
No full textAerospace Engineerin
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