Microtopography‐Guided Conductive Patterns of Liquid‐Driven Graphene Nanoplatelet Networks for Stretchable and Skin‐Conformal Sensor Array

Flexible thin‐film sensors have been developed for practical uses in invasive or noninvasive cost‐effective healthcare devices, which requires high sensitivity, stretchability, biocompatibility, skin/organ‐conformity, and often transparency. Graphene nanoplatelets can be spontaneously assembled into transparent and conductive ultrathin coatings on micropatterned surfaces or planar substrates via a convective Marangoni force in a highly controlled manner. Based on this versatile graphene assembled film preparation, a thin, stretchable and skin‐conformal sensor array (144 pixels) is fabricated having microtopography‐guided, graphene‐based, conductive patterns embedded without any complicated processes. The electrically controlled sensor array for mapping spatial distributions (144 pixels) shows high sensitivity (maximum gauge factor ≈1697), skin‐like stretchability (<48%), high cyclic stability or durability (over 10⁵ cycles), and the signal amplification (≈5.25 times) via structure‐assisted intimate‐contacts between the device and rough skin. Furthermore, given the thin‐film programmable architecture and mechanical deformability of the sensor, a human skin‐conformal sensor is demonstrated with a wireless transmitter for expeditious diagnosis of cardiovascular and cardiac illnesses, which is capable of monitoring various amplified pulse‐waveforms and evolved into a mechanical/thermal‐sensitive electric rubber‐balloon and an electronic blood‐vessel. The microtopography‐guided and self‐assembled conductive patterns offer highly promising methodology and tool for next‐generation biomedical devices and various flexible/stretchable (wearable) devices.     

Microgravity Effects on Chronoamperometric Ammonia Oxidation Reaction at Platinum Nanoparticles on Modified Mesoporous Carbon Supports

The effect of microgravity on the electrochemical oxidation of ammonia at platinum nanoparticles supported on modified mesoporous carbons (MPC) with three different pore diameters (64, 100, and 137 Å) was studied via the chronoamperometric technique in a half-cell. The catalysts were prepared by a H₂ reductive process of PtCl\(_{6}^{\mathrm {4-}}\) in presence of the mesoporous carbon support materials. A microgravity environment was obtained with an average gravity of less than 0.02 g created aboard an airplane performing parabolic maneuvers. Results show the chronoamperommetry of the ammonia oxidation reaction in 1.0 M NH₄OH at 0.60 V vs. RHE under microgravity conditions. The current density, in all three catalysts, decreased while in microgravity conditions when compared to ground based experiments. Under microgravity, all three catalysts yielded a decrease in ammonia oxidation reaction current density between 25 to 63% versus terrestrial experimental results, in time scales between 1 and 15 s. The Pt catalyst prepared with mesoporous carbon of 137 Å porous showed the smallest changes, between 25 to 48%. Nanostructuring catalyst materials have an effect on the level of current density decrease under microgravity conditions.     

Decentralized estimation of forest fire spread using reactive and cognitive mobile sensors

This paper presents a study of the ability to build an observer for a complex system using a decentralized multi-agent system for the coordination of mobile sensors. The environment is modeled using a CA model representing forest fire spread. The initial distribution for the different species in the vegetation is generated using a Perlin algorithm. Implementation is realized on GPGPU. A coherence measure for the observation error is defined. The observation itself is realized with mobile sensors and a decentralized coordination of the trajectories. We analyze the balance between individual and collective behaviours of agents which is required to achieve the best performance with respect to the chosen coherence measure. Two kinds of agent’ behaviour are studied: reactive and cognitive.     

Analysis of non-Markovian repairable fault trees through rare event simulation

Dynamic fault trees (DFTs) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components seldom fail. Rare event simulation (RES) provides techniques to reduce the number of samples in the case of rare events. In this article, we present a RES technique based on importance splitting to study failures in highly reliable DFTs, more precisely, on a variant of repairable fault trees (RFT). Whereas RES usually requires meta-information from an expert, our method is fully automatic. For this, we propose two different methods to derive the so-called importance function. On the one hand, we propose to cleverly exploit the RFT structure to compositionally construct such function. On the other hand, we explore different importance functions derived in different ways from the minimal cut sets of the tree, i.e., the minimal units that determine its failure. We handle RFTs with Markovian and non-Markovian failure and repair distributions—for which no numerical methods exist—and implement the techniques on a toolchain that includes the RES engine FIG, for which we also present improvements. We finally show the efficiency of our approach in several case studies.

     

Fe-doped LaCoO<Subscript>3</Subscript> perovskite catalyst for NO oxidation in the post-treatment of marine diesel engine’s exhaust emissions

New post-treatment process for marine diesel engine exhaust emissions was proposed by combining NO oxidation and wet scrubbing technology for the simultaneous removal of SO_X, NO_X and PM. NO, insoluble in aqueous scrubbing absorbent, is preferentially oxidized to NO₂, which then turns fully soluble in it. Fe substituted LaCo_1-xFe_xO₃ perovskite catalysts were developed for NO oxidation to NO₂. The catalysts were prepared by co-precipitation method and analyzed with XRD, XRF, BET, FT-IR, NO-TPD and XPS techniques. Crystal structure change from rhombohedral to orthorhombic was observed with the increased amount of Fe substituted in the B site of the perovskite by XRD analysis. From FT-IR and NO-TPD analysis, nitrate on perovskite species was found to be the active species for NO oxidation. Quantitative analysis was performed within the prepared catalysts. Catalytic activity was measured using a packed bed reactor operated at 150–400 °C, atmospheric pressure and with gas hourly space velocity (GHSV) of 20,000 h^-1 using a simulated exhaust gas composed of NO 400 ppm, O₂ 10% balanced with N2. Formation of Fe⁴⁺ cation enhanced the redox property as well as the mobility of the lattice oxygen present in the perovskite catalysts, confirmed by XPS analysis. Reaction mechanism of NO oxidation on Fe substituted LaCo_1-xFe_xO₃ was discussed based on Mars-van Krevelen mechanism.     

A reaction kinetic study of CO<Subscript>2</Subscript> gasification of petroleum coke,coals and mixture

Characteristics of Char-CO₂ gasification were compared in the temperature range of 1,100–1,400 °C using a thermogravimetric analyzer (TGA) for petroleum coke, coal chars and mixed fuels (Petroleum coke/coal ratios: 0, 0.25, 0.5, 0.75, 1). The results showed that reaction time decreased with increasing gasification temperature, BET surface area and alkali index of coal. Mixed fuels composed of petroleum coke/coal exhibited reduced activation energies. Modified volumetric reaction model and shrinking core model might be suitably matched with experimental data depending on coal type and petroleum coke/coal ratio. Rate equations were suggested by selecting gas-solid reaction rate models for each sample that could simulate CO₂ gasification behavior.     

Evaluating Bluetooth Low Energy Suitability for Time-Critical Industrial IoT Applications

In recent years, integration of wireless sensor networks in industrial environments has greatly increased. With this trend, new fields such as industrial IoT have arisen, which in turn have opened the doors to new possibilities that are shaping the future of industrial automation. In contrast to regular wireless networks, however, industrial applications of WSN are characterized for being time-critical systems with highly stringent requirements that challenge all available technologies. Because of its ultra-low energy properties, compatibility with most mobile units, reduced production costs, robustness and high throughput, Bluetooth low energy (BLE) is a potential candidate for these settings. This article explores the potential of BLE of meeting the real-time demands found in the domain of industrial process automation and industrial IoT. In order to evaluate the suitability of the protocol for these scenarios, the effect of adaptations in the retransmission scheme on the reliability and timeliness performance are thoroughly studied. Three retransmission schemes are evaluated and simulation results proved that by optimally modifying the BLE retransmission model, a maximum delay below 46 ms and a packet loss rate in the order of \(10^{-5}\) can be obtained, enabling BLE to fulfill the requirements of even the most demanding cases within the considered range of applications.     

Isobutene production from skeletal isomerization of 1-butene on WOx/ferrierite

Both potassium and ammonium ferrierite (FK and FA, respectively) were impregnated with tungsten species using either tungstic acid or ammonium metatungstate as precursors. The skeletal isomerization of 1-butene at 200–400°C, atmospheric pressure and 0.15 atm 1-butene partial pressure, was studied on samples with and without tungsten. Tungsten species on FK promote the isobutene formation while those species on FA generate a synergetic effect over the isobutene production. The presence of tungsten species improves the material stability and mainly, the isobutene yield. Starting the 1-butene feed over the catalytic bed at 200°C and then increasing the reaction temperature to 400°C, makes it possible to avoid the low isobutene selectivity at short times-on-stream. This revised version was published online in August 2006 with corrections to the Cover Date.     

Phenolic characterization and geographical classification of commercial Arbequina extra-virgin olive oils produced in southern Catalonia

The aim of this work was to characterize Arbequina extra-virgin olive oils (EVOOs) from different locations in southern Catalonia (Spain) in terms of their phenolic profile, to show the classification of oil samples with respect to geographical area. The phenolic compounds present in 32 olive-oil samples were analyzed by a rapid and effective HPLC–ESI-TOF/MS method, and 18 phenolic compounds belonging to different phenolic types were identified. The results showed no qualitative differences in the phenolic fractions among EVOO from different geographical region. However, quantitative differences were observed in a wide number of phenolic compounds. In all olive-oil samples studied, secoiridoids were the most abundant, followed by lignans, phenolic alcohols, and flavonoids, respectively. Multivariate data were analysed by canonical discriminant analyses. Seventeen variables were used without a variable reduction step. Phenolic content of extra-virgin olive oils was found to depend highly on geographical area.