A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

CoO nanoparticles embedded in three-dimensional nitrogen/sulfur co-doped carbon nanofiber networks as a bifunctional catalyst for oxygen reduction/evolution reactions

High-performance and low-cost bifunctional electrocatalysts play crucial roles in oxygen reduction and evolution reactions. Herein, a novel three-dimensional (3D) bifunctional electrocatalyst was prepared by embedding CoO nanoparticles into nitrogen and sulfur co-doped carbon nanofiber networks (denoted as CoO@N/S-CNF) through a facile approach. The carbon nanofiber networks were derived from a nanostructured biological material which provided abundant functional groups to nucleate and anchor nanoparticles while retaining its interconnected 3D porous structure. The composite possesses a high specific surface area and graphitization degree, which favors both mass transport and charge transfer for electrochemical reaction. The CoO@N/S-CNF not only exhibits highly efficient catalytic activity towards oxygen reduction reaction (ORR) in alkaline media with an onset potential of about 0.84 V, but also shows better stability and stronger resistance to methanol than Pt/C. Furthermore, it only needs an overpotential of 1.55 V to achieve a current density of 10 mA cm^-2, suggesting that it is an efficient electrocatalyst for oxygen evolution reaction (OER). The ΔE value (oxygen electrode activity parameter) of CoO@N/S-CNF is calculated to be 0.828 V, which demonstrates that the composite could be a promising bifunctional electrocatalyst for both ORR and OER.

An effective three-dimensional ordered mesoporous CuCo2O4 as electrocatalyst for Li-O2 batteries

Three-dimensional ordered mesoporous (3DOM) CuCo₂O₄ materials have been synthesized via a hard template and used as bifunctional electrocatalysts for rechargeable Li-O₂ batteries. The characterization of the catalyst by X-ray diffractometry and transmission electron microscopy confirms the formation of a single-phase, 3-dimensional, ordered mesoporous CuCo₂O₄ structure. The as-prepared CuCo₂O₄ nanoparticles possess a high specific surface area of 97.1 m² g^{- 1} and a spinel crystalline structure. Cyclic voltammetry demonstrates that mesoporous CuCo₂O₄ catalyst enhances the kinetics for either oxygen reduction reaction (ORR) or oxygen evolution reaction (OER). The Li-O₂ battery utilizing 3DOM CuCo₂O₄ shows a higher specific capacity of 7456 mAh g^{- 1} than that with pure Ketjen black (KB). Moreover, the CuCo₂O₄-based electrode enables much enhanced cyclability with a 610 mV smaller discharge-recharge voltage gap than that of the carbon-only cathode at a current rate of 100 mA g^{- 1}. Such excellent catalytic performance of CuCo₂O₄ could be associated with its larger surface area and 3D ordered mesoporous structure. The excellent electrochemical performances coupled with its facile and cost-effective way will render the 3D mesoporous CuCo₂O₄ nanostructures as attractive electrode materials for promising application in Li-O₂ batteries.

Three-dimensional simulations of gravitationally confined detonations compared to observations of SN 1991T

The gravitationally confined detonation (GCD) model has been proposed as a possible explosion mechanism for Type Ia supernovae in the single-degenerate evolution channel. It starts with ignition of a deflagration in a single off-centre bubble in a near-Chandrasekhar-mass white dwarf. Driven by buoyancy, the deflagration flame rises in a narrow cone towards the surface. For the most part, the main component of the flow of the expanding ashes remains radial, but upon reaching the outer, low-pressure layers of the white dwarf, an additional lateral component develops. This causes the deflagration ashes to converge again at the opposite side, where the compression heats fuel and a detonation may be launched. We first performed five three-dimensional hydrodynamic simulations of the deflagration phase in 1.4 M⊙ carbon/oxygen white dwarfs at intermediate-resolution (2563computational zones). We confirm that the closer the initial deflagration is ignited to the centre, the slower the buoyant rise and the longer the deflagration ashes takes to break out and close in on the opposite pole to collide. To test the GCD explosion model, we then performed a high-resolution (5123 computational zones) simulation for a model with an ignition spot offset near the upper limit of what is still justifiable, 200 km. This high-resolution simulation met our deliberately optimistic detonation criteria, and we initiated a detonation. The detonation burned through the white dwarf and led to its complete disruption. For this model, we determined detailed nucleosynthetic yields by post-processing 106 tracer particles with a 384 nuclide reaction network, and we present multi-band light curves and time-dependent optical spectra. We find that our synthetic observables show a prominent viewing-angle sensitivity in ultraviolet and blue wavelength bands, which contradicts observed SNe Ia. The strong dependence on the viewing angle is caused by the asymmetric distribution of the deflagration ashes in the outer ejecta layers. Finally, we compared our model to SN 1991T. The overall flux level of the model is slightly too low, and the model predicts pre-maximum light spectral features due to Ca, S, and Si that are too strong. Furthermore, the model chemical abundance stratification qualitatively disagrees with recent abundance tomography results in two key areas: our model lacks low-velocity stable Fe and instead has copious amounts of high-velocity 56Ni and stable Fe. We therefore do not find good agreement of the model with SN 1991T.

Efficient Blue Electroluminescence Using Quantum-Confined Two-Dimensional Perovskites

Solution-processed hybrid organic–inorganic lead halide perovskites are emerging as one of the most promising candidates for low-cost light-emitting diodes (LEDs). However, due to a small exciton binding energy, it is not yet possible to achieve an efficient electroluminescence within the blue wavelength region at room temperature, as is necessary for full-spectrum light sources. Here, we demonstrate efficient blue LEDs based on the colloidal, quantum-confined 2D perovskites, with precisely controlled stacking down to one-unit-cell thickness (n = 1). A variety of low-k organic host compounds are used to disperse the 2D perovskites, effectively creating a matrix of the dielectric quantum wells, which significantly boosts the exciton binding energy by the dielectric confinement effect. Through the Förster resonance energy transfer, the excitons down-convert and recombine radiatively in the 2D perovskites. We report room-temperature pure green (n = 7–10), sky blue (n = 5), pure blue (n = 3), and deep blue (n = 1) electroluminescence, with record-high external quantum efficiencies in the green-to-blue wavelength region.

High-Performance WSe2 CMOS Technology and Integrated Circuits.

Because of their extraordinary structural and electrical properties, two dimensional materials are currently being pursued for applications such as thin-film transistors and integrated circuit. One of the main challenges that still needs to be overcome for these applications is the fabrication of air-stable transistors with industry-compatible complementary metal oxide semiconductor (CMOS) technology. In this work, we experimentally demonstrate a novel high performance air-stable WSe2 CMOS technology with almost ideal voltage transfer characteristic, full logic swing and high noise margin with different supply voltages. More importantly, the inverter shows large voltage gain (~38) and small static power (Pico-Watts), paving the way for low power electronic system in 2D materials.

Graphene-MoS2 Hybrid Technology for Large-Scale Two-Dimensional Electronics

Two-dimensional (2D) materials have generated great interest in the last few years as a new toolbox for electronics. This family of materials includes, among others, metallic graphene, semiconducting transition metal dichalcogenides (such as MoS2) and insulating Boron Nitride. These materials and their heterostructures offer excellent mechanical flexibility, optical transparency and favorable transport properties for realizing electronic, sensing and optical systems on arbitrary surfaces. In this work, we develop several etch stop layer technologies that allow the fabrication of complex 2D devices and present for the first time the large scale integration of graphene with molybdenum disulfide (MoS2) , both grown using the fully scalable CVD technique. Transistor devices and logic circuits with MoS2 channel and graphene as contacts and interconnects are constructed and show high performances. In addition, the graphene/MoS2 heterojunction contact has been systematically compared with MoS2-metal junctions experimentally and studied using density functional theory. The tunability of the graphene work function significantly improves the ohmic contact to MoS2. These high-performance large-scale devices and circuits based on 2D heterostructure pave the way for practical flexible transparent electronics in the future. The authors acknowledge financial support from the Office of Naval Research (ONR) Young Investigator Program, the ONR GATE MURI program, and the Army Research Laboratory. This research has made use of the MI.