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Quantification of amine functional groups on silica nanoparticles: a multi-method approach
No full textSurface chemistry is an important factor for quality control during production of nanomaterials and for controlling their behavior in applications and when released into the environment. Here we report a comparison of four methods for quantifying amine functional groups on silica nanoparticles (NPs). Two colorimetric assays are examined, ninhydrin and 4-nitrobenzaldehyde, which are convenient for routine analysis and report on reagent accessible amines. Results from the study of a range of commercial NPs with different sizes and surface loadings show that the assays account for 50\u2013100% of the total amine content, as determined by dissolution of NPs under basic conditions and quantification by solution-state \ub9H NMR. To validate the surface quantification by the colorimetric assays, the NPs are modified with a trifluoromethylated benzaldehyde probe to enhance sensitivity for quantitative \ub9\u2079F solid state NMR and X-ray photoelectron spectroscopy (XPS). Good agreement between the assays and the determination from solid-state NMR is reinforced by elemental ratios from XPS, which indicate that in most cases the difference between total and accessible amine content reflects amines that are outside the depth probed by XPS. Overall the combined results serve to validate the relatively simple colorimetric assays and indicate that the reactions are efficient at quantifying surface amines, by contrast to some other covalent modifications that have been employed for functional group quantification.Peer reviewed: YesNRC publication: Ye
An extension of adaptive multi-start tabu search for the maximum quasi-clique problem
No full textGiven a simple undirected graph G=(V,E) and real constant (threshold) \u3b3 08(0,1], a subset of vertices S 86V is a \u3b3-quasi-clique (\u3b3-clique) if the number of edges in the subgraph induced by S is at least equal to \u3b3|S|2. The Maximum \u3b3-Quasi-Clique Problem (MQCP) aims to find a \u3b3-quasi-clique of maximum cardinality and it was shown to be NP-hard. The MQCP has numerous applications in several real-life networks including social, biological, transportation, internet and communication networks. This paper proposes an extension of Tabu Search approach (TSQC) for solving the maximum Quasi-Clique problem. TSQC is an extension of Adaptive Multistart Tabu Search (AMTS) method given by Wu and Hao (2013) for solving the maximum clique problem (MCP). The TSQC algorithm is evaluated on DIMACS and BHOSLIB benchmark instances of the maximum clique problem and on real-life networks and compared with the most recent exact and heuristic methods. Computational results disclose that the TSQC algorithm reaches the largest known quasi-clique in a reasonable time on the real-life networks and it outperforms the existing methods on DIMACS and BHOSLIB instances. Moreover, our proposed algorithm gives some new results with improved quality of MQCP.Peer reviewed: YesNRC publication: Ye
BA-PNN-based methods for power transformer fault diagnosis
No full textThis paper presents a machine learning-based approach to power transformer fault diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the probabilistic neural network (PNN). PNN is a radial basis function feedforward neural network based on Bayesian decision theory, which has a strong fault tolerance and significant advantages in pattern classification. However, one challenge still remains: the performance of PNN is greatly affected by its hidden layer element smooth factor which impacts the classification performance. The proposed approach addresses this challenge by deploying the BA algorithm, a kind of bio-inspired algorithm to optimize PNN. Using the real data collected from a transformer system, we conducted the experiments for validating the performance of the developed method. The experimental results demonstrated that BA is an effective algorithm for optimizing PNN smooth factor and BA-PNN can improve the fault diagnosis performance; in turn, and the machine learning-based model (BA-PNN) can significantly enhance the accuracies of power transformer fault diagnosis.Peer reviewed: YesNRC publication: Ye
Theoretical estimation of floating ice sheet deflection caused by the motion of a submerged object
No full textThe vertical deflection of floating ice sheets subjected to the movements of an underwater object is the subject of the present theoretical study. Steady deflections are estimated based on theories of small deformation of thin plates and potential flow using Fourier and Laplace transform techniques and complex analysis. Deformations are computed for different combinations of object shape, speed, depth, and ice thickness. Depending on the problem condition, deflection extremes range between a few millimeters to tens of centimeters. Ice vertical deflections are reduced by an increase in object depth and ice thickness and with a decrease in object speed. Flexural-gravity waves, resembling a Kelvin wake pattern, are formed when the speed of the object is more than a critical speed. Below this speed, deflections resemble a Bernoulli hump seen in openwater conditions. The report presents a summary and recommendations for future work.Peer reviewed: NoNRC publication: Ye
Controlling the potential landscape and normal modes of ion Coulomb crystals by a standing-wave optical potential
No full textLight-induced control of ions within small Coulomb crystals is investigated. By intense intracavity optical standing-wave fields, subwavelength localization of individual ions is achieved for one-, two-, and three-dimensional crystals. Based on these findings, we illustrate numerically how the application of such optical potentials can be used to tailor the normal-mode spectra and patterns of multidimensional Coulomb crystals. The results represent, among others, important steps towards controlling the crystalline structure of Coulomb crystals, investigating heat-transfer processes at the quantum limit, and quantum simulations of many-body systems.Peer reviewed: YesNRC publication: Ye
Development of a mathematical model to follow alumina injection
No full textTo improve the operation of the aluminum reduction technology, each step of the alumina incorporation into the bath needs to be understood. The mathematical model presented in this paper, uses the Discrete Element Method (DEM), based on the description of the interactions between the particles and surrounding liquid, solving the kinematic equations. First, the description of particle-particle interactions was validated by experimental values of bulk density and angle of repose. Next, the interaction of particles with the liquid when arriving onto the free surface was added. Furthermore, modelling of phenomena like aggregate formation, breakup and dissolution requires the quantitative description and integration of further physicochemical knowledge. The number of injected powder particles during feeding is in the order of few hundred million. Following of such a number of particles is very much demanding computationally. Consequently, it is primordial to determine the minimal number of particles that reproduces correctly the physical reality.Peer reviewed: YesNRC publication: Ye
Elastomers and plastics for resisting erosion attack of abrasive/erosive slurries
No full textElastomers and plastics are widely used as the materials for handling abrasive/erosive slurries because of their wear resistance, toughness, corrosion resistance and light weight. In this study, slurry jet erosion, Coriolis slurry scouring erosion and large particle slurry erosion are used to characterize the erosion resistance of selected elastomers and plastics, including two natural rubbers, a neoprene rubber, a polyurethane and three types of polyethylene, at different test conditions. The wear rates and wear modes of the tested materials are presented and the relationships between the erosion resistance of these materials and their mechanical properties are discussed. Suitable applications of these materials in slurry transport are also recommended.Peer reviewed: YesNRC publication: Ye
High-resolution mapping of nitrogen dioxide with TROPOMI: first results and validation over the Canadian oil sands
No full textTROPOspheric Monitoring Instrument (TROPOMI), on\u2010board the Sentinel\u20105 Precurser satellite, is a nadir\u2010viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near\u2010infrared, and shortwave infrared. From these spectra several important air quality and climate\u2010related atmospheric constituents are retrieved, including nitrogen dioxide (NO2) at unprecedented spatial resolution from a satellite platform. We present the first retrievals of TROPOMI NO2 over the Canadian Oil Sands, contrasting them with observations from the Ozone Monitoring Instrument satellite instrument, and demonstrate TROPOMI's ability to resolve individual plumes and highlight its potential for deriving emissions from individual mining facilities. Further, the first TROPOMI NO2 validation is presented, consisting of aircraft and surface in situ NO2 observations, and ground\u2010based remote\u2010sensing measurements between March and May 2018. Our comparisons show that the TROPOMI NO2 vertical column densities are highly correlated with the aircraft and surface in situ NO2 observations, and the ground\u2010based remote\u2010sensing measurements with a low bias (15\u201330\u2009%); this bias can be reduced by improved air mass factors.Peer reviewed: YesNRC publication: Ye
Combining machine learning and metaheuristics algorithms for classification method PROAFTN
No full textThe supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems.Peer reviewed: YesNRC publication: Ye
