pH effects on the electrochemical reduction of CO₍₂₎ towards C₂ products on stepped copper

CO2 conversion to reduced products provides a use for greenhouse gases, but reaction complexity stymies mechanistic studies. Here, authors present a microkinetic model for CO2 and CO reduction on copper, based on ab initio simulations, to elucidate pH’s impact on competitive reaction pathways

Pyrolysis of medium-density fiberboard: optimized search for kinetics scheme and parameters via a genetic algorithm driven by Kissinger's method

The pyrolysis kinetics of charring materials plays an important role in understanding material combustions especially for construction materials with complex degradation chemistry. Thermogravimetric analysis (TGA) is frequently used to study the heterogeneous kinetics of solid fuels; however, there is no agreed method to determine the pyrolysis scheme and kinetic parameters for charring polymers with multiple components and competing reaction pathways. This study develops a new technique to estimate the possible numbers of species and sub-reactions in pyrolysis by analyzing the second derivatives of thermogravimetry (DDTG) curves. The pyrolysis of a medium-density fiberboard (MDF) in nitrogen is studied in detail, and the DDTG curves are used to locate the temperature of the peak mass-loss rate for each sub-reaction. Then, on the basis of the TG data under multiple heating rates, Kissinger’s method is used to quickly find the possible range of values of the kinetic parameters (<i>A</i> and <i>E</i>). These ranges are used to accelerate the optimization of the inverse problem using a genetic algorithm (GA) for the kinetic and stoichiometric parameters. The proposed method and kinetic scheme found are shown to match the experimental data and are able to predict accurately results at different heating rates better than Kissinger’s method. Moreover, the search method (K–K method) is highly efficient, faster than the regular GA search alone. Modeling results show that, as the TG data available increase, the interdependence among kinetic parameters becomes weak and the accuracy of the first-order model declines. Furthermore, conducting TG experiment under multiple heating rates is found to be crucial in obtaining good kinetic parameters

Thermal alteration of biomarkers in the presence of elemental sulfur and sulfur-bearing minerals during hydrous and anhydrous pyrolysis

Although elemental sulfur and sulfur-bearing minerals are not the main constituents of sedimentary rock, they are still important for the formation and destruction of biomarkers. In this study, a bitumen of Sichuan Basin mudstone with abundant biomarkers was separately pyrolyzed (under both hydrous and anhydrous conditions) with elemental sulfur (S degrees) and sulfur-bearing minerals (including pyrite, ferrous sulfate, and ferric sulfate) at various temperatures (300, 330 and 350 degrees C). The results show that the effects of different forms of sulfur on the evolution of biomarkers vary. Pyrite (FeS2) had only a slight influence on the characteristics of the biomarkers during anhydrous and hydrous pyrolysis. On the other hand, the presence of S, ferrous sulfate (FeSO4) and ferric sulfate (Fe-2(SO4)(3)) promoted the thermal cracking of the biomarkers and changed the biomarker distributions under anhydrous conditions. The extent of biomarker thermal alterations decreased in the following order: S degrees &gt; Fe-2(SO4)(3) &gt; FeSO4 &gt; FeS2. Additionally, the presence of water seemed to promote the effects of the sulfur additive on the changes in biomarker compositions, but this did not change their raking in terms of influence. The elemental sulfur alteration of the biomarkers increased with pyrolysis temperature (simulated maturity) and the abundance of elemental sulfur in the sample. The results obtained offer new insights into how biomarkers evolve when elemental sulfur and sulfur-bearing minerals are present. (C) 2018 Elsevier Ltd. All rights reserved

Water permeability prediction of sponge city pavement materials based on different machine learning algorithms

Permeable pavement material is one of the most important supporting materials in the construction of sponge city, and its water permeability is the most important performance index. The water permeability test of permeable pavement materials is a tedious and complicated experimental work. It is of great research significance to predict the water permeability of permeable pavement materials through structural parameters modeling. In this paper, the database is first established by experimental means, and then the prediction models of LASSO (Least absolute shrinkage and selection operator), SVR (Support vector regression) and GBR (Gradient Boosting Regression) machine learning algorithms are established. Through the four factors of particle size, particle size distribution, shape parameters and binder content predict the water permeability of sponge city pavement materials. The results show that different machine learning algorithms have different sensitivity to the distribution of data samples. The fitting effect of GBR model water permeability prediction is better than that of SVR and LASSO models. The test value-predicted value MSE is 0.0051 and R2 is 0.92, which can effectively predict the water permeability of sponge city pavement materials

User Presence Inference via Encrypted Traffic of Wireless Camera in Smart Homes

Wireless cameras are widely deployed in smart homes for security guarding, baby monitoring, fall detection, and so on. Those security cameras, which are supposed to protect users, however, may in turn leak a user’s personal privacy. In this paper, we reveal that attackers are able to infer whether users are at home or not, that is, the user presence, by eavesdropping the traffic of wireless cameras from distance. We propose HomeSpy, a system that infers user presence by inspecting the intrinsic pattern of the wireless camera traffic. To infer the user presence, HomeSpy first eavesdrops the wireless traffic around the target house and detects the existence of wireless cameras with a Long Short-Term Memory (LSTM) network. Then, HomeSpy infers the user presence using the bitrate variation of the wireless camera traffic based on a cumulative sum control chart (CUSUM) algorithm. We implement HomeSpy on the Android platform and validate it on 20 cameras. The evaluation results show that HomeSpy can achieve a successful attack rate of 97.2%

Investigation on the stability of the Rijke-type thermoacoustic system with an axially distributed heat source

Due to the incurred damages to the combustors, large-amplitude self-sustained thermoacoustic oscillations are unwanted in many propulsion systems, such as liquid/solid rocket motors and aero-engines. To suppress these thermoacoustic oscillations efficiently, the mechanism of thermoacoustic instability needs to be clarified. Following the previous experimental work, the transitions to instability in a Rijke-type thermoacoustic system with an axially distributed heat source are studied numerically in this paper. The URANS numerical method is utilized and verified by means of a mesh sensitivity analysis. The influences of the axially distributed heater length, the heater location, and the mean flow velocity on the nonlinear dynamic behaviors of thermoacoustic oscillations are evaluated. To explore the corresponding mechanism behind these influences, the principle of acoustic energy conservation has been applied. The acoustic energy gains from the thermal-acoustic coupling are quantified via Rayleigh’s integral, and their phase differences are calculated by the cross-correlation function. The acoustic damping induced by the vortex dissipation is qualitatively analyzed by the characteristics of the flow fields in the Rijke tube. Finally, as the heater length, the heater location, or the mean flow velocity is varied, three mechanisms of the transitions to instability in a Rijke-type thermoacoustic system are identified

LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol, L E S S , which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate L E S S in both real WSN systems and a large-scale simulation, and the results show that L E S S can reduce energy and bandwidth consumption by up to 50 % while still achieving more than 90 % link quality estimation accuracy