Photocatalytic reduction of CO<Subscript>2</Subscript> with H<Subscript>2</Subscript>O over modified TiO<Subscript>2</Subscript> nanofibers: Understanding the reduction pathway

Nanosized metal (Pt or Pd)-decorated TiO₂ nanofibers (NFs) were synthesized by a wet impregnation method. CdSe quantum dots (QDs) were then anchored onto the metal-decorated TiO₂ NFs. The photocatalytic performance of these catalysts was tested for activation and reduction of CO₂ under UV-B light. Gas chromatographic analysis indicated the formation of methanol, formic acid, and methyl formate as the primary products. In the absence of CdSe QDs, Pd-decorated TiO₂ NFs were found to exhibit enhanced performance compared to Pt-decorated TiO₂ NFs for methanol production. However, in the presence of CdSe, Pt-decorated TiO₂ NFs exhibited higher selectivity for methanol, typically producing ～90 ppmg^?1·h^?1 methanol. The CO₂ photoreduction mechanism is proposed to take place via a hydrogenation pathway from first principles calculations, which complement the experimental observations.

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A mechanism of separation in electrostatic ion chromatography

The retention mechanism of electrostatic ion chromatography (EIC) is currently under debate and is the focus of this paper. A comprehensive set of retention data has been obtained on a C18 column coated with the zwitterionic surfactant 3-(N,N-dimethylmyristylammonio)propanesulfonate used with a range of mobile phases in which both the mobile-phase anion and cation have been varied systematically. Electro-osmotic flow measurements were also obtained on fused-silica capillaries coated with the zwitterion (and also some monofunctional surfactants) and were used to evaluate the nature of the surface charge on the layer of adsorbed surfactant in the presence of various background electrolytes. A new retention mechanism for EIC was developed on the basis of these data. This mechanism proposes that equilibration of the bound zwitterions with a mobile phase containing a suitable electrolyte causes the establishment of a charged layer created by the terminal sulfonate groups of the zwitterion, which acts as a Donnan membrane. The magnitude and polarity of the charge on this membrane depends on the nature of the mobile-phase ions. The Donnan membrane exerts weak electrostatic repulsion or attraction effects on analyte anions. A second component of the retention mechanism is chaotropic interaction of the analyte anion with the quaternary ammonium functional group of the zwitterion. This interaction exerts the major effect on the separation selectivity of EIC, such that analyte anions are eluted in order of increasing chaotropic interactions in accordance with the Hofmeister series.     

A method for rejecting 3k-th harmonics in bandpass 6N-path filters

In this paper, a method for 3 -th ( ) harmonics rejection in 6 -path filters is proposed, and the related analysis is provided. Using a single-ended-input to differential-output structure, the filter selectivity around even harmonics are also suppressed. Accordingly, a proof-of-concept band-pass filter is designed, and postlayout simulations in the 90-nm CMOS technology are carried out, which covers an input frequency range from 200 MHz to 1.2 GHz with a channel bandwidth of 10 to 15.5 MHz. The achieved third harmonic rejection at 1-GHz local oscillator (LO) frequency is about 43 dB. Over the entire radio frequency (RF) range, the in-band IIP3 and noise figure are better than 1.5 dBm and 5.3 dB, respectively. The power consumption of the analog circuitry is 21 mW from the 1.2-V supply, whereas the digital clock generation circuitry consumes between 0.9 and 5.2 mW, depending on the center frequency of the filter.     

Improved Enhanced Dbtma with Contention-Aware Admission Control to Improve the Network Performance in Manets

DBTMA relies entirely on RTS/CTS dialogue for un-collided transmission of data. The purpose is to improve the QoS at MAC layer by developing it over 802.11e standard. However, DBTMA does not guarantee real-time constraints without efficient method for controlling the network loads. The main challenges in MANETs include prediction of the available bandwidth, establishing communication with neighboring nodes and predicting the consumption of bandwidth flow. These challenges are provided with solutions using Contention-Aware Admission Control (CACP) protocol. In this paper, the EDBTMA protocol is combined with CACP protocol that introduces bandwidth calculation using admission control strategy. The calculation includes certain metrics like: admission control and bandwidth consumption. To compute the bandwidth of channel, bandwidth utilization and traffic priority is distinguished through dual busy tone is proposed. This operates distinctly on its own packet transmission operation. This CACP mechanism defends the conventional traffic flows from new nodes and based on the measured status information of the channel, it QoS of the admitted flows is maintained. This ensures maximum amount of bandwidth flows accommodated by resources and determines the resources in a system meet the new flow requirements while maintaining existing bandwidth flow levels.     

New Class of Electrocatalysts Based on 2D Transition Metal Dichalcogenides in Ionic Liquid

The optimization of traditional electrocatalysts has reached a point where progress is impeded by fundamental physical factors including inherent scaling relations among thermokinetic characteristics of different elementary reaction steps, non‐Nernstian behavior, and electronic structure of the catalyst. This indicates that the currently utilized classes of electrocatalysts may not be adequate for future needs. This study reports on synthesis and characterization of a new class of materials based on 2D transition metal dichalcogenides including sulfides, selenides, and tellurides of group V and VI transition metals that exhibit excellent catalytic performance for both oxygen reduction and evolution reactions in an aprotic medium with Li salts. The reaction rates are much higher for these materials than previously reported catalysts for these reactions. The reasons for the high activity are found to be the metal edges with adiabatic electron transfer capability and a cocatalyst effect involving an ionic‐liquid electrolyte. These new materials are expected to have high activity for other core electrocatalytic reactions and open the way for advances in energy storage and catalysis.     

Emerging Carbon‐Based Heterogeneous Catalysts for Electrochemical Reduction of Carbon Dioxide into Value‐Added Chemicals

The electrocatalytic reduction of CO₂ provides a sustainable way to mitigate CO₂ emissions, as well as store intermittent electrical energy into chemicals. However, its slow kinetics and the lack of ability to control the products of the reaction inhibit its industrial applications. In addition, the immature mechanistic understanding of the reduction process makes it difficult to develop a selective, scalable, and stable electrocatalyst. Carbon‐based materials are widely considered as a stable and abundant alternative to metals for catalyzing some of the key electrochemical reactions, including the CO₂ reduction reaction. In this context, recent research advances in the development of heterogeneous nanostructured carbon‐based catalysts for electrochemical reduction of CO₂ are summarized. The leading factors for consideration in carbon‐based catalyst research are discussed by analyzing the main challenges faced by electrochemical reduction of CO₂. Then the emerging metal‐free doped carbon and aromatic N‐heterocycle catalysts for electrochemical reduction of CO₂ with an emphasis on the formation of multicarbon hydrocarbons and oxygenates are discussed. Following that, the recent progress in metal–nitrogen–carbon structures as an extension of carbon‐based catalysts is scrutinized. Finally, an outlook for the future development of catalysts as well as the whole electrochemical system for CO₂ reduction is provided.     

Big Data Analytics Using Graph Signal Processing

The networks are fundamental to our modern world and they appear throughout science and society. Access to a massive amount of data presents a unique opportunity to the researcher’s community. As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace. Therefore, this paper initiates a discussion on graph signal processing for large-scale data analysis. We first provide a comprehensive overview of core ideas in Graph signal processing (GSP) and their connection to conventional digital signal processing (DSP). We then summarize recent developments in developing basic GSP tools, including methods for graph filtering or graph learning, graph signal, graph Fourier transform (GFT), spectrum, graph frequency, etc. Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise. We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets. To show the suitability and the effeteness, we first created a noisy graph signal and then applied it to the filter. After several rounds of simulation results. We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal. By using this example application, we thoroughly demonstrated that graph filtration is efficient for big data analytics.     

Synthesis and photocatalytic studies of TiO2-clinoptilolite on spent caustic wastewater treatment

Mixed spent caustic is an industrial wastewater that is generated from oil refineries and olefin units. In this investigation, the TiO₂ film was loaded with Clinoptilolite for preparing TiO₂-clinoptilolite photocatalyst. In order to characterize the surface of composite and its microstructure, scanning electron microscopy (SEM) and powder x-ray diffraction (XRD) have been used. Subsequently, this photocatalyst is utilized for the degradation of spent caustic wastewater to determine the photocatalytic efficiency of TiO₂-clinophotocatalyst. Then the photocatalytic degradation of chemical oxygen demand (COD) in spent caustic wastewater has been modeled using the design of experiment method. This procedure was performed in two ways: in the presence of an oxidant agent (H₂O₂) and by minimizing the amount of oxidant. The results show that the maximum degradation efficiency of TiO₂-clinophotocatalyst is 74.3% and 77.3% under the condition of applying limitation and without concerning limitation for the presence of oxidant, respectively.     

Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems

The potential clinical applications of adaptive neural network control for pharmacology in general, and anesthesia and critical care unit medicine in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. The approach is applicable to nonlinear nonnegative systems with unmodeled dynamics of unknown dimension and guarantees that the physical system states remain in the nonnegative orthant of the state-space for nonnegative initial conditions. Finally, a numerical example involving the infusion of the anesthetic drug midazolam for maintaining a desired constant level of depth of anesthesia for noncardiac surgery is provided to demonstrate the efficacy of the proposed approach.     

Neural network adaptive control for nonlinear nonnegative dynamical systems

Nonnegative and compartmental dynamical system models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative. These models are widespread in engineering and life sciences and typically involve the exchange of nonnegative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a full-state feedback neural adaptive control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state-space for nonnegative initial conditions.