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Monolayers of molybdenum disulfide (MoS2) are the most studied two-dimensional (2D) transition-metal dichalcogenides (TMDs), due to its exceptional optical, electronic, and opto-electronic properties. Recent studies have shown the possibility of incorporating a small amount of magnetic transition metals (e.g., Fe, Co, Mn, V) into MoS2 to form a 2D dilute magnetic semiconductor (2D-DMS). However, the origin of the observed ferromagnetism has remained elusive, due to the presence of randomly generated sulfur vacancies during synthesis that can pair with magnetic dopants to form complex dopant-vacancy configurations altering the magnetic order induced by the dopants. By combining high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) imaging with first-principles density functional theory (DFT) calculations and magnetometry data, we demonstrate the critical effects of sulfur vacancies and their pairings with vanadium atoms on the magnetic ordering in V-doped MoS2 (V-MoS2) monolayers. Additionally, we fabricated a series of field effect transistors on these V-MoS2 monolayers and observed the emergence of p-type behavior as the vanadium concentration increased. Our study sheds light on the origin of ferromagnetism in V-MoS2 monolayers and provides a foundation for future research on defect engineering to tune the electronic and magnetic properties of atomically thin TMD-based DMSs.

Substitutionally-doped 2D transition metal dichalcogenides are primed for next-generation device applications such as field effect transistors (FET), sensors, and optoelectronic circuits. In this work, we demonstrate substitutional Rhenium (Re) doping of MoS2 monolayers with controllable concentrations down to 500 parts-per-million (ppm) by metal-organic chemical vapor deposition (MOCVD). Surprisingly, we discover that even trace amounts of Re lead to a reduction in sulfur site defect density by 5-10x. Ab initio models indicate the free-energy of sulfur-vacancy formation is increased along the MoS2 growth-front when Re is introduced, resulting in an improved stoichiometry. Remarkably, defect photoluminescence (PL) commonly seen in as-grown MOCVD MoS2 is suppressed by 6x at 0.05 atomic percent (at.%) Re and completely quenched with 1 at.% Re. Furthermore, Re-MoS2 transistors exhibit up to 8x higher drain current and enhanced mobility compared to undoped MoS2 because of the improved material quality. This work provides important insights on how dopants affect 2D semiconductor growth dynamics, which can lead to improved crystal quality and device performance., Comment: 20 pages, 5 figures

High throughput characterization and processing techniques are becoming increasingly necessary to navigate multivariable, data-driven design challenges for sensors and electronic devices. For two-dimensional materials, device performance is highly dependent upon a vast array of material properties including number of layers, lattice strain, carrier concentration, defect density, and grain structure. In this work, laser-crystallization was used to locally pattern and transform hundreds of regions of amorphous MoS2 thin films into 2D 2H-MoS2. A high throughput Raman spectroscopy approach was subsequently used to assess the process-dependent structural and compositional variations for each illuminated region, yielding over 5500 distinct non-resonant, resonant, and polarized Raman spectra. The rapid generation of a comprehensive library of structural and compositional data elucidated important trends between structure-property-processing relationships involving laser-crystallized MoS2, including the relationships between grain size, grain orientation, and intrinsic strain. Moreover, extensive analysis of structure/property relationships allowed for intelligent design, and evaluation of major contributions to, device performance in MoS2 chemical sensors. In particular, it is found that sensor performance is strongly dependent on the orientation of the MoS2 grains relative to the crystal plane.

In this article, we study the transport properties of superconcentrated electrolytes using Molecular Dynamics simulations, which have been shown experimentally to retard elemental dissolution in vanadium containing cathode materials. Five compositions between one and seven molar lithium bis(trifluoromethanesulfonyl)imide in 1,3-Dioxolane and 1,2-Dimethoxyethane solvent mixture are studied using non-polarizable Optimized Potentials for Liquid Simulations - All Atom force field. The simulated physico-chemical properties such as ionic conductivity, self-diffusion coefficients, and density are observed to match well with the results obtained through experiments. Radial Distribution Function analysis reveals a strong co-ordination between salt anions and vanadium cations as the electrolyte transitions from a salt-in-solvent type to solvent-in-salt type electrolyte. A high anion content in the first solvation shell of vanadium cations is observed for solvent-in-salt type electrolytes, through ion-clustering calculations. Solvation free energy calculations using Free Energy Perturbation method indicate that the active material dissolution should be retarded by using superconcentrated electrolytes. Ion-dynamics of the clusters reveal that vanadium cation transport occurs against its concentration gradient due to strong coulombic interactions with the salt anions in superconcentrated electrolytes. The improvement in the cycleability of several vanadium containing cathode materials provides a robust proof for the theoretical framework described in this manuscript., Comment: 31 pages, 6 Figures, part of the work presented at 239th ECS Conference (A07-0469)

In recent years, Attribute-Based Access Control (ABAC) has become quite popular and effective for enforcing access control in dynamic and collaborative environments. Implementation of ABAC requires the creation of a set of attribute-based rules which cumulatively form a policy. Designing an ABAC policy ab initio demands a substantial amount of effort from the system administrator. Moreover, organizational changes may necessitate the inclusion of new rules in an already deployed policy. In such a case, re-mining the entire ABAC policy will require a considerable amount of time and administrative effort. Instead, it is better to incrementally augment the policy. Keeping these aspects of reducing administrative overhead in mind, in this paper, we propose PAMMELA, a Policy Administration Methodology using Machine Learning to help system administrators in creating new ABAC policies as well as augmenting existing ones. PAMMELA can generate a new policy for an organization by learning the rules of a policy currently enforced in a similar organization. For policy augmentation, PAMMELA can infer new rules based on the knowledge gathered from the existing rules. Experimental results show that our proposed approach provides a reasonably good performance in terms of the various machine learning evaluation metrics as well as execution time., Comment: This work is under progress

Scalable substitutional doping of two-dimensional (2D) transition metal dichalcogenides (TMDCs) is a prerequisite to developing next-generation logic and memory devices based on 2D materials. To date, doping efforts are still nascent. Here, we report scalable growth and vanadium (V) doping of 2D WSe2 at front-end-of-line (FEOL) and back-end-of-line (BEOL) compatible temperatures of 800 {\deg}C and 400 {\deg}C, respectively. A combination of experimental and theoretical studies confirm that vanadium atoms substitutionally replace tungsten in WSe2, which results in p-type doping via the introduction of discrete defect levels that lie close to the valence band maxima. The p-type nature of the V dopants is further verified by constructed field-effect transistors, where hole conduction becomes dominant with increasing vanadium concentration. Hence, our study presents a method to precisely control the density of intentionally introduced impurities, which is indispensable in the production of electronic-grade wafer-scale extrinsic 2D semiconductors.

Realization of wafer-scale single-crystal films of transition metal dichalcogenides (TMDs) such as tungsten sulfide requires epitaxial growth and coalescence of oriented domains to form a continuous monolayer. The domains must be oriented in the same crystallographic direction on the substrate to avoid the formation of metallic inversion domain boundaries (IDBs) which are a common feature of layered chalcogenides. Here we demonstrate fully-coalesced single orientation tungsten sulfide monolayers on 2-inch diameter c-plane sapphire by metalorganic chemical vapor deposition using a multi-step growth process. High growth temperatures and sulfur/metal ratios were required to reduce domain misorientation and achieve epitaxial tungsten sulfide monolayers with low in-plane rotational twist (0.09 deg). Transmission electron microscopy analysis reveals that the tungsten sulfide monolayers lack IDBs but instead have translational boundaries that arise when tungsten sulfide domains with slightly off-set lattices merge together. By adjusting the monolayer growth rate, the density of translational boundaries and bilayer coverage were significantly reduced. The preferred orientation of domains is attributed to the presence of steps on the sapphire surface coupled with growth conditions promote surface diffusion and oriented attachment. The transferred tungsten sulfide monolayers show neutral and charged exciton emission at 80K with negligible defect-related luminescence. Back-gated tungsten sulfide field effect transistors exhibited mobility of 16 cm2/Vs. The results demonstrate the potential of achieving wafer-scale TMD monolayers free of inversion domains with properties approaching that of exfoliated flakes.

Dilute magnetic semiconductors, achieved through substitutional doping of spin-polarized transition metals into semiconducting systems, enable experimental modulation of spin dynamics in ways that hold great promise for novel magneto-electric or magneto-optical devices, especially for two-dimensional systems such as transition metal dichalcogenides that accentuate interactions and activate valley degrees of freedom. Practical applications of 2D magnetism will likely require room-temperature operation, air stability, and (for magnetic semiconductors) the ability to achieve optimal doping levels without dopant aggregation. Here we describe room-temperature ferromagnetic order obtained in semiconducting vanadium-doped tungsten disulfide monolayers produced by a reliable single-step film sulfidation method across an exceptionally wide range of vanadium concentrations, up to 12 at% with minimal dopant aggregation. These monolayers develop p-type transport as a function of vanadium incorporation and rapidly reach ambipolarity. Ferromagnetism peaks at an intermediate vanadium concentration of a few atomic percent and decreases for higher concentrations, which is consistent with quenching due to orbital hybridization at closer vanadium-vanadium spacings, as supported by transmission electron microscopy, magnetometry and first-principles calculations. Room-temperature two-dimensional dilute magnetic semiconductors provide a new component to expand the functional scope of van der Waals heterostructures and bring semiconducting magnetic 2D heterostructures them into the realm of practical application.

Two dimensional (2D) materials continue to hold great promise for future electronics, due to their atomic-scale thicknesses and wide range of tunable properties. However, commercial efforts in this field are relatively recent, and much progress is required to fully realize 2D materials for commercial success. Here, we present a roadmap for the realization of electronic-grade 2D materials. We discuss technology drivers, along with key aspects of synthesis and materials engineering required for development of these materials. Additionally, we highlight several fundamental milestones required for realization of electronic-grade 2D materials, and intend this article to serve as a guide for researchers in the field.

The generation of thermopower in a miniaturized device modeled as a channel connected to reservoirs/contacts maintained at different temperatures is studied in this work. The left reservoir is also coupled to a periodic THz driving source leading to a rearrangement of its internal energy levels. The modified levels are determined using Floquet theory that governs the dynamics of a periodic Hamiltonian. The starting point for the presented thermopower calculations (mirrored in the related thermal gradient controlled current) is the Landauer formula rooted in the transmission formalism. Primarily, we show that while thermally activated electrons can be pumped from the hot reservoir into the cold side as a straightforward manifestation of the Seebeck effect through a difference in their respective Fermi levels, the quantum of charge flow increases in presence of the periodic perturbation. We explain this phenomenon by noting how the periodic driving makes available a greater number of states in the left reservoir that are able to inject electrons into the channel. The calculations also uncover a useful feature whereby the strength of such a thermally-pumped current is amenable through a joint control of the amplitude and frequency of the signal, offering an additional experimentally-adjustable tool to regulate their flow. The calculated results are shown for two classes of materials defined by prototypical linear and quadratic dispersion, with the former capable of furnishing a larger current by virtue of higher available density-of-modes. We close by pointing out possible improvements to the calculation by accounting for dissipative effects in the channel., Comment: 9 pages, 6 figures

In real-world Bayesian inference applications, prior assumptions regarding the parameters of interest may be unrepresentative of their actual values for a given dataset. In particular, if the likelihood is concentrated far out in the wings of the assumed prior distribution, this can lead to extremely inefficient exploration of the resulting posterior by nested sampling algorithms, with unnecessarily high associated computational costs. Simple solutions such as broadening the prior range in such cases might not be appropriate or possible in real-world applications, for example when one wishes to assume a single standardised prior across the analysis of a large number of datasets for which the true values of the parameters of interest may vary. This work therefore introduces a posterior repartitioning (PR) method for nested sampling algorithms, which addresses the problem by redefining the likelihood and prior while keeping their product fixed, so that the posterior inferences and evidence estimates remain unchanged but the efficiency of the nested sampling process is significantly increased. Numerical results show that the PR method provides a simple yet powerful refinement for nested sampling algorithms to address the issue of unrepresentative priors.

In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHVp) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier., Comment: 34 pages, 11 figures

Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet packet transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR) - an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts., Comment: 45 pages, 47 figures

This paper derives new formulations for designing dominant pole placement based proportional-integral-derivative (PID) controllers to handle second order processes with time delays (SOPTD). Previously, similar attempts have been made for pole placement in delay-free systems. The presence of the time delay term manifests itself as a higher order system with variable number of interlaced poles and zeros upon Pade approximation, which makes it difficult to achieve precise pole placement control. We here report the analytical expressions to constrain the closed loop dominant and non-dominant poles at the desired locations in the complex s-plane, using a third order Pade approximation for the delay term. However, invariance of the closed loop performance with different time delay approximation has also been verified using increasing order of Pade, representing a closed to reality higher order delay dynamics. The choice of the nature of non-dominant poles e.g. all being complex, real or a combination of them modifies the characteristic equation and influences the achievable stability regions. The effect of different types of non-dominant poles and the corresponding stability regions are obtained for nine test-bench processes indicating different levels of open-loop damping and lag to delay ratio. Next, we investigate which expression yields a wider stability region in the design parameter space by using Monte Carlo simulations while uniformly sampling a chosen design parameter space. Various time and frequency domain control performance parameters are investigated next, as well as their deviations with uncertain process parameters, using thousands of Monte Carlo simulations, around the robust stable solution for each of the nine test-bench processes., Comment: 50 pages, 42 figures, Knowledge-Based Systems, 2018

This paper proposes a novel approach of integrating different neuroimaging techniques to characterize an autistic brain. Different techniques like EEG, fMRI and DTI have traditionally been used to find biomarkers for autism, but there have been very few attempts for a combined or multimodal approach of EEG, fMRI and DTI to understand the neurobiological basis of autism spectrum disorder (ASD). Here, we explore how the structural brain network correlate with the functional brain network, such that the information encompassed by these two could be uncovered only by using the latter. In this paper, source localization from EEG using independent component analysis (ICA) and dipole fitting has been applied first, followed by selecting those dipoles that are closest to the active regions identified with fMRI. This allows translating the high temporal resolution of EEG to estimate time varying connectivity at the spatial source level. Our analysis shows that the estimated functional connectivity between two active regions can be correlated with the physical properties of the structure obtained from DTI analysis. This constitutes a first step towards opening the possibility of using pervasive EEG to monitor the long-term impact of ASD treatment without the need for frequent expensive fMRI or DTI investigations., Comment: 14 pages, 14 figures, IEEE Transactions on Cognitive and Developmental Systems, 2017

The present paper proposes a novel method of quantification of the variation in biofilm architecture, in correlation with the alteration of growth conditions that include, variations of substrate and conditioning layer. The polymeric biomaterial serving as substrates are widely used in implants and indwelling medical devices, while the plasma proteins serve as the conditioning layer. The present method uses descriptive statistics of FESEM images of biofilms obtained during a variety of growth conditions. We aim to explore here the texture and fractal analysis techniques, to identify the most discriminatory features which are capable of predicting the difference in biofilm growth conditions. We initially extract some statistical features of biofilm images on bare polymer surfaces, followed by those on the same substrates adsorbed with two different types of plasma proteins, viz. Bovine serum albumin (BSA) and Fibronectin (FN), for two different adsorption times. The present analysis has the potential to act as a futuristic technology for developing a computerized monitoring system in hospitals with automated image analysis and feature extraction, which may be used to predict the growth profile of an emerging biofilm on surgical implants or similar medical applications., Comment: 34 pages, 14 figures

In a recent paper, we presented an intelligent evolutionary search technique through genetic programming (GP) for finding new analytical expressions of nonlinear dynamical systems, similar to the classical Lorenz attractor's which also exhibit chaotic behaviour in the phase space. In this paper, we extend our previous finding to explore yet another gallery of new chaotic attractors which are derived from the original Lorenz system of equations. Compared to the previous exploration with sinusoidal type transcendental nonlinearity, here we focus on only cross-product and higher-power type nonlinearities in the three state equations. We here report over 150 different structures of chaotic attractors along with their one set of parameter values, phase space dynamics and the Largest Lyapunov Exponents (LLE). The expressions of these new Lorenz-like nonlinear dynamical systems have been automatically evolved through multi-gene genetic programming (MGGP). In the past two decades, there have been many claims of designing new chaotic attractors as an incremental extension of the Lorenz family. We provide here a large family of chaotic systems whose structure closely resemble the original Lorenz system but with drastically different phase space dynamics. This advances the state of the art knowledge of discovering new chaotic systems which can find application in many real-world problems. This work may also find its archival value in future in the domain of new chaotic system discovery., Comment: 103 pages, 169 figures, Applied Mathematical Modelling, 2018