Producing thick films of conducting polymers by a low-cost manufacturing technique would enable new applications. However, removing huge solvent volume from diluted suspension or dispersion (1–3 wt%) in which conducting polymers are typically obtained is a true manufacturing challenge. In this work, a procedure is proposed to quickly remove water from the conducting polymer poly(3,4-ethylenedioxythiophene:poly(4-styrene sulfonate) (PEDOT:PSS) suspension. The PEDOT:PSS suspension is first flocculated with 1 m H2SO4 transforming PEDOT nanoparticles (≈50–500 nm) into soft microparticles. A filtration process inspired by pulp dewatering in a paper machine on a wire mesh with apertures dimension between 60 µm and 0.5 mm leads to thick free-standing films (≈0.5 mm). Wire mesh clogging that hinders dewatering (known as dead-end filtration) is overcome by adding to the flocculated PEDOT:PSS dispersion carbon fibers that aggregate and form efficient water channels. Moreover, this enables fast formation of thick layers under simple atmospheric pressure filtration, thus making the process truly scalable. Thick freestanding PEDOT films thus obtained are used as electrocatalysts for efficient reduction of oxygen to hydrogen peroxide, a promising green chemical and fuel. The inhomogeneity of the films does not affect their electrochemical function. © 2021 The Authors.
Service assurance for cloud applications is a challenging task and is an active area of research for academia and industry. One promising approach is to utilize machine learning for service quality prediction and fault detection so that suitable mitigation actions can be executed. In our previous work, we have shown how to predict service-level metrics in real-time just from operational data gathered at the server side. This gives the service provider early indications on whether the platform can support the current load demand. This paper provides the logical next step where we extend our work by proposing an automated detection and diagnostic capability for the performance faults manifesting themselves in cloud and datacenter environments. This is a crucial task to maintain the smooth operation of running services and minimizing downtime. We demonstrate the effectiveness of our approach which exploits the interpretative capabilities of Self- Organizing Maps (SOMs) to automatically detect and localize different performance faults for cloud services.
Automated detection and diagnosis of the performance faults in cloud and datacenter environments is a crucial task to maintain smooth operation of different services and minimize downtime. We demonstrate an effective machine learning approach based on detecting metric correlation stability violations (CSV) for automated localization of performance faults for datacenter services running under dynamic load conditions.
The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG1) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.
This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care@home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care@home. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
Transiently-powered computers (TPCs) lay the basis for a battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption, which determine energy efficiency. We demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and develop EPIC, a compile-time energy analysis tool. We use EPIC to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, programmers avoid unnecessary program changes that hurt energy efficiency.
Low-power small-scale embedded sensing systems employing batteries generally impose high maintenance costs. To enable maintenance-free operation, they are powered from energy harvested from the environment thus making them batteryless. However, due to high variance of ambient energy, these batteryless embedded devices are unable to harvest enough energy from the environment required for continuous device operation thus hampering application progress and causing frequent loss of volatile program-state. Therefore, these batteryless devices have to employ state retention mechanisms to save the volatile program-state to non-volatile storage before interruption. These batteryless embedded sensing devices are known as transiently-powered systems (TPS). In this article, we survey existing literature to identify strategies and techniques used by each existing literature to decide what amount of volatile program-state needs to be saved and when to save it. We list the challenges in retaining program-state across periods of energy unavailability and how existing state-of-the-art solutions tackle them. We also describe different memory models and discuss factors governing the choice of each model for TPS deployment.
Intermittently powered embedded devices ensure forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to the execution times. To minimize this overhead, we present DICE, a system that renders differential checkpointing profitable on these devices. DICE is unique because it is a software-only technique and efficient because it only operates in volatile main memory to evaluate the differential. DICE may be integrated with reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems, and arbitrary code can be enabled with DICE using automatic code-instrumentation requiring no additional programmer effort. By reducing the cost of checkpoints, DICE cuts the peak energy demand of these devices, allowing operation with energy buffers that are one-eighth of the size originally required, thus leading to benefits such as smaller device footprints and faster recharging to operational voltage level. The impact on final performance is striking: with DICE, Hibernus requires one order of magnitude fewer checkpoints and one order of magnitude shorter time to complete a workload in real-world settings.
Embedded devices running on ambient energy perform computations intermittently, depending upon energy availability. System support ensures forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to execution times. To reduce this overhead, we present DICE, a system design that efficiently achieves differential checkpointing in intermittent computing. Distinctive traits of DICE are its software-only nature and its ability to only operate in volatile main memory to determine differentials. DICE works with arbitrary programs using automatic code instrumentation, thus requiring no programmer intervention, and can be integrated with both reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems. By reducing the cost of checkpoints, performance markedly improves. For example, using DICE, Hibernus requires one order of magnitude shorter time to complete a fixed workload in real-world settings.
Transiently powered computers (TPCs) form the foundation of the battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This kind of power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption. Such a deceptively minor observation is overlooked in existing literature. Systems are thus designed and parameterized in overly conservative ways, missing on a number of optimizations.We rather demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and prove its use in two settings. First, we develop EPIC, a compile-time energy analysis tool. We use it to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, it avoids unnecessary program changes that hurt energy efficiency. Next, we extend the MSPsim emulator and explore its use in parameterizing a different TPC system support. The improvements in energy efficiency yield up to more than 1000% time speedup to complete a fixed workload.
Developing high specific energy Lithium-ion (Li-ion) batteries is of vital importance to boost the production of efficient electric vehicles able to meet the customers’ expectation related to the electric range of the vehicle. One possible pathway to high specific energy is to increase the operating voltage of the Li-ion cell. Cathode materials enabling operation above 4.2 V are available. The stability of the positive electrode-electrolyte interface is still the main bottleneck to develop high voltage cells. Moreover, important research efforts are devoted to the substitution of graphite anodes with Li metal: this would improve the energy density of the cell dramatically. The use of metallic lithium is prevented by the dendrite growth during charge, with consequent safety problems. To suppress the formation of dendrites solid-state electrolytes are considered the most promising approach. For these reasons the present review summarizes the most recent research efforts in the field of high voltage solid-state electrolytes for high energy density Li-ion cells.
A new non-hydrolytic, alkoxide-based route was developed to synthesize iron oxide nanocrystals. Surfactant-free thermal decomposition of the iron 2-methoxy-ethoxide precursors results in the formation of uniform iron oxide nanocrystals with an average size of 5.6 nm. Transmission electron microscope study shows that the nanocrystals are protected against aggregation by a repulsive surface layer, probably originating from the alkoxy-alkoxide ligands. Addition of oleic acid resulted in monodisperse nanocrystals with an average size of 4 nm. Mössbauer analysis confirmed that the nanocrystals mainly consisted of maghemite. Analysis of the magnetic hysteresis loop measurements and the zero field and field cooled measurements displayed an excellent fit to established theories for single-domain superparamagnetic nanocrystals and the size of the magnetic domains correlated well to the crystallite size obtained from transmission electron microscope.
In this study we investigate the magnetic behavior of magnetic multi-core particles and the differences in the magnetic properties of multi-core and single-core nanoparticles and correlate the results with the nanostructure of the different particles as determined from transmission electron microscopy(TEM). We also investigate how the effective particle magnetic moment is coupled to the individual moments of the single-domain nanocrystals by using different measurement techniques: DC magnetometry, AC susceptometry, dynamic light scattering and TEM. We have studied two magnetic multi-core particle systems – BNF Starch from Micromod with a median particle diameter of 100 nm and FeraSpin R from nanoPET with a median particle diameter of 70 nm – and one single-core particle system – SHP25 from Ocean NanoTech with a median particle core diameter of 25 nm.
We use functionalized iron oxide magnetic multi-core particles of 100 nm in size (hydrodynamic particle diameter) and AC susceptometry (ACS) methods to measure the binding reactions between the magnetic nanoparticles (MNPs) and bio-analyte products produced from DNA segments using the rolling circle amplification (RCA) method. We use sensitive induction detection techniques in order to measure the ACS response. The DNA is amplified via RCA to generate RCA coils with a specific size that is dependent on the amplification time. After about 75 min of amplification we obtain an average RCA coil diameter of about 1 µm. We determine a theoretical limit of detection (LOD) in the range of 11 attomole (corresponding to an analyte concentration of 55 fM for a sample volume of 200 µL) from the ACS dynamic response after the MNPs have bound to the RCA coils and the measured ACS readout noise. We also discuss further possible improvements of the LOD.
The effect of crust temperature and water content on acrylamide formation was studied during the baking of white bread. To assess the effect of over-baking, we used a full factorial experimental design in which the baking time was increased by 5 and 10 min at each baking temperature. Additional experiments were performed with steam baking and falling temperature baking. Immediately after baking, the crust was divided into the outer and inner crust fractions, and the water content and acrylamide concentration of each fraction was measured. The outer crust had a significantly lower water content and higher acrylamide concentration than the inner crust did. Crust temperature in combination with water content had a significant effect on acrylamide formation, higher temperatures resulting in higher acrylamide concentrations. However, at very high temperatures and lower water contents, acrylamide concentration was observed to decrease, though the bread colour was then unacceptable for consumption. Steam and falling temperature baking, on the other hand, decreased the acrylamide content while producing bread crust with an acceptable colour. The lowest acrylamide values and an acceptable crust colour were produced by steam baking. © 2007 Swiss Society of Food Science and Technology.
The development of formulation engineering concepts in food manufacturing and the demand for diversity in food products has driven a substantial market increase for food ingredients. Most ingredients are supplied in powder form and therefore a better understanding of dispersed solid food systems is important both for food ingredient manufactures and food producers. © 2008 Springer Science+Business Media, LLC.
Acidification can be used to improve the quality of canned vegetables because it can decrease thermal processing requirements. Moreover, there is some evidence that acidified vegetables retain a better texture than non-acidified ones. Optimisation of the acidification processes requires the knowledge of the relationship between texture and thermal process at low/moderate temperature. The main objective of this work was the modelling of textural changes of vegetables during acidification under isothermal conditions, and the application of the model to predict textural changes in non-isothermal processes. Turnip was the vegetable used in the experiments. The effect of pre-treatments (blanching, freezing/thawing, calcium addition and vacuum infusion of water before acidification) on the kinetics of textural changes during acidification was also studied. Turnips were acidified in acetic acid under isothermal (20, 50, 70, 80, 90 and 100°C) and non-isothermal conditions (20 to 90°C). Texture was modelled by a two subtract first order kinetic model, assuming an Arrhenius-type dependence of the rate constants on temperature. The model parameters were estimated by nonlinear regression. At temperatures lower then 70°C no significant changes in texture were detected. At higher temperatures the model showed a good fit to the data for all the conditions tested. Acidification decreased the percentage of heat labile substrate from 96% to 62%, thus improving firmness retention. The parameters of the kinetic model estimated under isothermal conditions provided an adequate prediction of texture changes under non-isothermal conditions. The methodology developed in this work can be further applied to model the textural changes of vegetables during other thermal processes, such as drying, blanching, and frying.
Fruit and vegetable processors, faced with the challenge of gaining and maintaining a healthy position in the competitive fruit and vegetable sector, are optimising traditional processes towards product quality. Using frozen potatoes as a case study, the aim of this study was to evaluate the potential of improving the texture of potatoes by modifying the processing conditions. The texture of processed frozen potatoes is the result of the integral effect of the unit operations applied through the production chain. Production of frozen potatoes includes a blanching, a freezing, thawing/cooking step before being served. In this study, blanching temperatures from 70 to 97 °C up to 10 minutes were tested and combined with freezing by two freezing methods, impingement and air-blast freezing. The texture was measured after cooking of frozen potatoes in boiling water. Besides texture, water holding capacity, microstructural evaluation and pectinmethylesterase (PME) activity were determined. Blanching of potatoes prior to freezing can both improve water holding capacity and texture of potatoes, but this effect depends on the freezing rate and blanching temperature. Potatoes frozen with fastest freezing method are firmer and hold more water than the ones frozen by air-blast freezing. Blanching at 70 °C can lead to potatoes that after cooking retain a firmer texture, compared with blanching at 90 °C. The duration of the blanching treatment has also an important effect on texture changes. The studies of PME activity showed a good correlation between texture and PME for blanching at 90 °C, but for blanching at 70°C and longer times, the development of texture does not seem to be solely related to PME activity. Microstructural studies showed that the differences in texture are due to differences in the degree of starch gelatinisation and integrity of the cellular structure.
Microwave convective drying of plant foods is a promising process due to the shorter drying time and better product quality. High microwave power decreases the drying time but causes charring of the product. In this work, microwave drying under constant and variable microwave power were compared. Temperature-sensitive products, such as plant foods, are especially affected by microwave power during the final drying period. Therefore, drying at variable microwave power was found to be a more suitable drying process. Air (temperature and velocity) has an important role during microwave drying, not only as carrier of evaporated moisture but also as it contributes to a more homogeneous and faster drying.
The effects on drying rate and texture of treating two plant tissues with calcium, before drying in air with microwave assistance, were studied in this work. The two tissues, potato and apple cubes, which have different structures and composition, were pretreated by immersion in CaCl2 solutions at 20 or at 70 °C before microwave-assisted air dehydration at 50, 60 and 70 °C. The pretreatments with calcium influenced the strength of the plant tissue cell wall, producing products of varying hardness after rehydration. The effect of the two calcium pretreatments was quite different for apples and potatoes. For apples, calcium pretreatment at 20 °C increased the hardness of rehydrated apples compared with untreated apples, but calcium pretreatment at 70 °C had no effect on texture. For potatoes, both calcium pretreatments at 20 and at 70 °C significantly increased the hardness of rehydrated potatoes. The water diffusivity during drying varied mainly because of the type of plant tissue, with secondary effects caused by the drying temperature and the type of calcium pretreatment.
Manganese-rich deposits in the lower member of the Datangpo Formation (DTP) (ca. 663–654 Ma) in South China formed in the aftermath of the Cryogenian Sturtian glaciation. The Mn in the DTP occurs dominantly as rhodochrosite and Ca-rhodochrosite. A hydrothermal origin of the Mn2+ is shown by the rare earth element distribution and significantly high Mn/Fe ratios (3–19, average = 10.1). Previous studies suggested a microbially-mediated process for controlling the DTP black-shale hosted Mn carbonate deposits. However, detailed reports on the formation mechanisms of micro-scale (<2–5 μm) ooid-like Mn carbonates in the DTP have rarely been published. Systematic petrography and geochemical analyses in this study demonstrate the coexistence of two types of micro-scale ooidal-like Mn carbonates formed through two distinct mechanisms, either dominated by microbially-mediated or physiochemically-forced pathways. The Type I Mn carbonate has relatively larger grain size of 2–5 μm and exhibits a radial-concentric microfabric that shows signs of growth banding in the form of alternating light and dark laminae, which mainly express variation in Ca and Mn concentrations. The initial precipitation phase of the Type I Mn carbonate is interpreted to be Mn oxide/hydroxide, based on positive Ce anomalies and selective enrichments of particular trace elements. Novel evidence indicates that the capture of Mn as a carbonate phase directly from the water column by primarily precipitated calcite, which is referred to as the Type II Mn carbonate, has also contributed to the DTP Mn-rich deposits. Multiple roles of organic matter in Mn carbonate formation have been established: (1) catalysed Mn-redox cycling; (2) trapping and transportation of initial mineral precipitates to sediments; (3) serving as a carbon source; (4) regulating the morphology of the Mn carbonate. As a key link for understanding Cryogenian carbon and Mn cycling, specific formation pathways for the DTP Mn-carbonates are likely to have been controlled by given atmospheric-oceanic compositions (including oxygen level, pCO2, and redox conditions) in response to major geological and biological events during the interglacial period. In turn, massive storage of inorganic carbon and phosphorous in Mn carbonate phases would have had a substantial influence on biogeochemical carbon cycling during the Cryogenian.
Organic polymer thermoelectrics (TE) as well as transition metal (TM) silicides are two thermoelectric class of materials of interest because they are composed of atomic elements of high abundance; which is a prerequisite for mass implementation of thermoelectric (TE) solutions for solar and waste heat recovery. But both materials have drawbacks when it comes to finding low-cost manufacturing. The metal silicide needs high temperature (>1000 °C) for creating TE legs in a device from solid powder, but it is easy to achieve long TE legs in this case. On the contrary, organic TEs are synthesized at low temperature from solution. However, it is difficult to form long legs or thick films because of their low solubility. In this work, we propose a novel method for the room temperature synthesis of TE composite containing the microparticles of chromium disilicide; CrSi2 (inorganic filler) in an organic matrix of nanofibrillated cellulose- poly(3,4-ethyelenedioxythiophene)-polystyrene sulfonate (NFC-PEDOT:PSS). With this method, it is easy to create long TE legs in a room temperature process. The originality of the approach is the use of conducting polymer aerogel microparticles mixed with CrSi2 microparticles to obtain a composite solid at room temperature under pressure. We foresee that the method can be scaled up to fabricate and pattern TE modules. The composite has an electrical conductivity (Ï) of 5.4 ± 0.5 S/cm and the Seebeck coefficient (α) of 88 ± 9 ΌV/K, power factor (α2Ï) of 4 ± 1 ΌWmâ1Kâ2 at room temperature. At a temperature difference of 32 °C, the output power/unit area drawn across the load, with the resistance same as the internal resistance of the device is 0.6 ± 0.1 ΌW/cm2.
In current practices crawl spaces are typically ventilated with outdoor air. This leads very often to high relative humidity especially in the beginning of the summer, which can be problematic if the excess humidity cannot be ventilated efficiently enough. This paper introduces a crawl space concept where the crawl space is highly insulated and traditional ventilation openings are replaced by minimal mechanical exhaust ventilation set by pressure difference with the aim to prevent potential pollutants to penetrate indoors through the base floor. The concept that has been developed based on the simulation study is tested in this study with field measurements in four single family houses. Continuous measurements of relative humidity and temperature in crawl spaces and outdoor air were running more than one year in each building. The results revealed that all the crawl spaces had very low relative humidity, mostly below 75% and for very short periods close to 80% even though some of the buildings were new and construction phase moisture was drying out. The results revealed that the crawl space concept studied provided an ultimate moisture safety and can be recommended for all buildings with wooden floor. © The Authors
The hypotheses that reactive uncured, thermoset bicomponent fibres can be prepared and mixed with reinforcing fibres and ultimately used in preparation of a composite was tested and is described. It is thought that such fibres have the two potential advantages: (1) to enable manufacturing with particle doped resins e.g. nanocomposites which add functionality to composites and (2) increased efficiency of structural composite manufacturing by increasing the level of automation. The structure of the thermoset fibres comprises of a sheath of thermoplastic and a core of uncured thermoset resin. Once manufactured, the fibres were wound with a reinforced fibre onto a plate, consolidated and cured. The resulting composite was examined and compared to other composites made with the same manufacturing method from commercially available materials. The results show that a laminate can be produced using these reactive bicomponent fibres. The resin system successfully impregnates the reinforcing carbon fibres and that the thermoplastic separates from the epoxy resin system during consolidation. In comparison to reference material, the bicomponent laminate shows promising characteristics. However, the processes developed are currently on a lab-scale and considerable improvement of various bicomponent fibre properties, such as the strength, are required before the technology can be used on a larger scale.
This work presents synthesis and spectroscopic characterization of a new metal-organic framework (MOF). The compound Fe-BDC-DMF was synthetized by the solvothermal method and prepared via a reaction between FeCl3.6H2O and benzene-1,4-dicarboxylic acid (H2BDC) or terephthalic acid using N,N-dimethylformamide (DMF) as solvent. The powder was characterized by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) and infrared spectroscopy (IR) analysis. The electrochemical properties were investigated in a typical lithium-ion battery electrolyte by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and galvanostatic charging and discharging. The synthetized Fe-BDC-DMF metal-organic framework (MOF) contains a mixture of three phases, identified by PXRD as: MOF-235, and MIL-53(Fe) monoclinic with C2/c and P21/c space groups. The structure of the Fe-BDC is built up from Fe3+ ions, terephalates (BDC) bridges and in-situ-generated DMF ligands. The electrochemical measurements conducted in the potential range of 0.5–3.5 V vs. Li+/Li0 show the voltage profiles of Fe-BDC and a plateau capacity of around 175 mAh/g. © 2022 The Author(s)
This work focuses on the synthesis of LiFePO4-PANI hybrid materials and studies their electrochem. properties (capacity, cyclability and rate capability) for use in lithium ion batteries. PANI synthesis and optimization was carried out by chem. oxidation (self-assembly process), using ammonium persulfate (APS) and H3PO4, obtaining a material with a high degree of crystallinity. For the synthesis of the LiFePO4-PANI hybrid, a thermal treatment of LiFePO4 particles was carried out in a furnace with polyaniline (PANI) and lithium acetate (AcOLi)-coated particles, using Ar/H2 atm. The pristine and synthesized powders were characterized by XRD, SEM, IR and TGA. The electrochem. characterizations were carried out by using CV, EIS and galvanostatic methods, obtaining a capacity of 95 mAhg-1 for PANI, 120 mAhg-1 for LiFePO4 and 145 mAhg-1 for LiFePO4-PANI, at a charge/discharge rate of 0.1 C. At a charge/discharge rate of 2 C, the capacities were 70 mAhg-1 for LiFePO4 and 100 mAhg-1 for LiFePO4-PANI, showing that the PANI also had a favorable effect on the rate capability.
The compound Ni3(C8H4O4)3(C3H7NO)3, poly-[tris(µ4-Benzene-1,4-dicarboxylato)-tetrakis(µ1-dimethylformamide-κ1O)-trinickel(II)], was synthesized by the solvothermal method prepared via reaction between NiCl2•6H2O and terephthalic acid using N,N-dimethylformamide (DMF) as solvent. The structure was characterized by powder X-ray diffraction and infrared spectroscopy analyses. The electrochemical properties as a potential active material in lithium-ion batteries were characterized by electrochemical impedance spectroscopy and galvanostatic charge-discharge curves in a battery half-cell. The characterization results show that the coordination network contains one independent structure in the asymmetric unit. It is constructed from Ni2+ ions, terephthalate bridges and in-situ-generated DMF ligands, forming two similar two-dimensional (2D) layer structures. These similar 2D layers are in an alternating arrangement and are linked with each other by dense H—H interactions (45%) to generate a three-dimensional (3D) supramolecular framework with ordered and disordered DMF molecules. The electrochemical measurements, conducted in the potential range of 0.5–3.5 V vs Li/Li+, show that Ni3(C8H4O4)3(C3H7NO)4 has good electrochemical properties and can work as anode in lithium-ion batteries. The material presents an initial specific capacity of ∼420 mAh g−1, which drops during consecutive scans but stabilizes at ∼50 mAh g−1. However, due to the wide potential range there are indications of a gradual collapse of the structure. The electrochemical impedance spectroscopy shows an increase of charge transfer resistance from 24 to 1190 Ohms after cycling likely due to this collapse.
The crystal structure of the title compound, [Ni3(C8H4O4)3(C3H7NO)4], is a two-dimensional coordination network formed by trinuclear linear Ni3(tp)3(DMF)4 units (tp = terephthalate = benzene-1,4-di-carboxyl-ate and DMF = di-methyl-formamide) displaying a characteristic coordination mode of acetate groups in polynuclear metal-organic compounds. Individual trinuclear units are connected through tp anions in a triangular network that forms layers. One of the DMF ligands points outwards and provides inter-actions with equivalent planes above and below, leaving the second ligand in a structural void much larger than the DMF mol-ecule, which shows positional disorder. Parallel planes are connected mainly through weak C-H⋯O, H⋯H and H⋯C inter-actions between DMF mol-ecules, as shown by Hirshfeld surface analysis.
Lead-free tin-based solder joints often have a single-grained structure with random orientation and highly anisotropic properties. These alloys are typically stiffer than lead-based solders, hence transfer more stress to printed circuit boards (PCBs) during thermal cycling. This may lead to cracking of the PCB laminate close to the solder joints, which could increase the PCB flexibility, alleviate strain on the solder joints, and thereby enhance the solder fatigue life. If this happens during accelerated thermal cycling it may result in overestimating the lifetime of solder joints in field conditions. In this study, the grain structure of SAC305 solder joints connecting ceramic resistors to PCBs was studied using polarized light microscopy and was found to be mostly single-grained. After thermal cycling, cracks were observed in the PCB under the solder joints. These cracks were likely formed at the early stages of thermal cycling prior to damage initiation in the solder. A finite element model incorporating temperature-dependant anisotropic thermal and mechanical properties of single-grained solder joints is developed to study these observations in detail. The model is able to predict the location of damage initiation in the PCB and the solder joints of ceramic resistors with reasonable accuracy. It also shows that the PCB cracks of even very small lengths may significantly reduce accumulated creep strain and creep work in the solder joints. The proposed model is also able to evaluate the influence of solder anisotropy on damage evolution in the neighbouring (opposite) solder joints of a ceramic resistor.
Residual stresses created during the packaging process can adversely affect the reliability of electronics components. We used incremental hole-drilling method, following the ASTM E 837-20 standard, to measure packaging induced residual stresses in discrete packages of power electronics components. For this purpose, we bonded a strain gauge on the surface of a Gallium Nitride (GaN) power component, drilled a hole through the thickness of the component in several incremental steps, recorded the relaxed strain data on the sample surface using the strain gauge, and finally calculated the residual stresses from the measured strain data. The recorded strains and the residual stresses are related by the compliance coefficients. For the hole drilling method in the isotropic materials, the compliance coefficients are calculated from the analytical solutions, and available in the ASTM standard. But for the orthotropic multilayered components typically found in microelectronics assemblies, numerical solutions are necessary. We developed a subroutine in ANSYS APDL to calculate the compliance coefficients of the hole drilling test in the molded and embedded power electronics components. This can extend the capability of the hole drilling method to determine residual stresses in more complex layered structures found in electronics.
Additive manufacturing (AM) of large-scale polymer and composite parts using robotic arms integrated with extruders has received significant attention in recent years. Despite the contributions of great technical progress and material development towards optimizing this manufacturing method, different failure modes observed in the final printed products have hindered its application in producing large engineering structures used in aerospace and automotive industries. We report failure modes in a variety of printed polymer and composite parts, including fuel tanks and car bumpers. Delamination and warpage observed in these parts originate mostly from thermal gradients and residual stresses accumulated during material deposition and cooling. Because printing large structures requires expensive resources, process simulation to recognize the possible failure modes can significantly lower the manufacturing cost. In this regard, accurate prediction of temperature distribution using thermal simulations is the first step. Finite element analysis (FEA) was used for process simulation of large-scale robotic AM. The important steps of the simulation are presented, and the challenges related to the modeling are recognized and discussed in detail. The numerical results showed reasonable agreement with the temperature data measured by an infrared camera. While in small-scale extrusion AM, the cooling time to the glassy state is less than 1 s, in large-scale AM, the cooling time is around two orders of magnitudes longer. © 2022 by the authors
Compared with silicon-based power devices, wide band gap (WBG) semiconductor devices operate at significantly higher power densities required in applications such as electric vehicles and more electric airplanes. This necessitates development of power electronics packages with enhanced thermal characteristics that fulfil the electrical insulation requirements. The present research investigates the feasibility of using ceramic additive manufacturing (AM), also known as three-dimensional (3D) printing, to address thermal and electrical requirements in packaging gallium nitride (GaN) based high-electron-mobility transistors (HEMTs). The goal is to exploit design freedom and manufacturing flexibility provided by ceramic AM to fabricate power device packages with a lower junction-to-ambient thermal resistance (<italic>R</italic>θJA). Ceramic AM also enables incorporation of intricate 3D features into the package structure in order to control the isolation distance between the package source and drain contact pads. Moreover, AM allows to fabricate different parts of the packaging assembly as a single structure to avoid high thermal resistance interfaces. For example, the ceramic package and the ceramic heatsink can be printed as a single part without any bonding layer. Thermal simulations under different thermal loading and cooling conditions show the improvement of thermal performance of the package fabricated by ceramic AM. If assisted by an efficient cooling strategy, the proposed package has the potential to reduce <italic>R</italic>θJA by up to 48%. The results of the preliminary efforts to fabricate the ceramic package by AM are presented, and the challenges that have to be overcome for further development of this manufacturing method are recognized and discussed.
Silicon carbide (SiC) power devices are steadily increasing their market share in various power electronics applications. However, they require low-inductive packaging in order to realize their full potential. In this research, low-inductive layouts for half-bridge power modules, using a direct bonded copper (DBC) substrate, that are suitable for SiC power devices, were designed and tested. To reduce the negative effects of the switching transients on the gate voltage, flexible printed circuit boards (PCBs) were used to interconnect the gate and source pins of the module with the corresponding pads of the power chips. In addition, conductive springs were used as low inductive, solder-free contacts for the module power terminals. The module casing and lid were produced using additive manufacturing, also known as 3D printing, to create a compact design. It is shown that the inductance of this module is significantly lower than the commercially available modules.
Soft composite actuators can be fabricated by embedding shape memory alloy (SMA) wires into soft polymer matrices. Shape retention and recovery of these actuators are typically achieved by incorporating shape memory polymer segments into the actuator structure. However, this requires complex manufacturing processes. This work uses multimaterial 3D printing to fabricate composite actuators with variable stiffness capable of shape retention and recovery. The hinges of the bending actuators presented here are printed from a soft elastomeric layer as well as a rigid shape memory polymer (SMP) layer. The SMA wires are embedded eccentrically over the entire length of the printed structure to provide the actuation bending force, while the resistive wires are embedded into the SMP layer of the hinges to change the temperature and the bending stiffness of the actuator hinges via Joule heating. The temperature of the embedded SMA wire and the printed SMP segments is changed sequentially to accomplish a large bending deformation, retention of the deformed shape, and recovery of the original shape, without applying any external mechanical force. The SMP layer thickness was varied to investigate its effect on shape retention and recovery. A nonlinear finite element model was used to predict the deformation of the actuators.
Four-dimensional (4D) printed structures fabricated from shape memory polymers (SMPs) are typically one-way actuators, that is, for each actuation cycle, they must be programmed to deform from the original (as-printed) shape to a secondary (programmed) shape. This is done by applying a combination of thermal and mechanical loads. Then, they restore the initial shape during the actuation process by applying a thermal load. Here, we generalize this concept to fabricate two-way actuators by embedding shape memory alloy (SMA) wires into the printed SMP structures. To explain this in greater detail, we describe the printing process of a two-way bending actuator whose bilayer hinges consist of stiff SMPs as well as elastomers with low modulus. Joule heating was employed to modulate the hinges bending stiffness. To this end, electrical current was applied to the resistive wires inserted into the hinges SMP layer to control their temperature. On the other hand, thermomechanical programming of the SMA wires, which were integrated into the actuator, provided the bending actuation force. The fabricated actuator was able to bend, maintain the deformed shape, and recover the as-fabricated shape in a fully automated manner. Further potentials of this design methodology were assessed using a nonlinear finite element model. The model incorporated user-defined subroutines to incorporate complex material behaviors of SMAs and SMPs.
Shape memory alloys (SMAs) have been widely used to fabricate soft actuators by embedding SMA wires into various soft matrices manufactured by conventional moulding methods or novel three-dimensional (3D) printing techniques. However, soft matrices of SMA based actuators are typically fabricated from only one or two different materials. Here, we exploit the great manufacturing flexibility of multimaterial 3D printing to fabricate various bending, twisting and extensional actuators by precisely controlling the spatial arrangements of different printing materials with different stiffnesses. In order to achieve a broad range of deformations, ten different printing materials were characterized and used in the actuators design. In addition, we developed a finite element model to simulate complex deformations of the printed actuators and facilitate the design process. The model incorporates a user defined material subroutine that describes the nonlinear temperature dependant behavior of SMAs. The results show the efficiency and flexibility of multimaterial 3D printing in tailoring the deformed shape of the SMA based soft actuators, which cannot be accomplished using conventional manufacturing methods such as moulding.
Structural composites with a high content of renewable material were produced from natural fibres and an acrylated epoxidized soybean oil resin. Composites were prepared by spray impregnation followed by compression moulding at elevated temperature. The resulting composites had good mechanical properties in terms of tensile strength and flexural strength. Tensile testing as well as dynamical mechanical thermal analysis showed that increasing the fibre content, increased the mechanical properties. The resin can be reinforced with up to 70 wt % fibre without sacrifice in processability. The tensile modulus ranged between 5.8 and 9.7 GPa depending on the type of fibre mat. The study of the adhesion by low vacuum scanning electron microscopy shows that the fibres are well impregnated in the matrix. The aging properties were finally evaluated. This study shows that composites with a very high content of renewable constituents can be produced from soy bean oil resins and natural fibres. © 2009 Wiley Periodicals, Inc.
Commuting by car from home to work can be very time consuming. We have conducted a study to explore what people are doing, and want to do, while commuting. People use their time in the car on a wide variety of activities with great innovation. There was no unanimous activity that everyone wanted, rather a wide variety of activities were requested. Three different categories of activity were identified which we refer to as mundane, vocational and traffic related. To demonstrate a possible IT service supporting commuters, a prototype based on speech output and a simple input mechanism from a wheel was developed. This service moves sampling of music from the conventional shop into the car. The prototype was informally tested with users, which resulted in a number of improvements. Preliminary user results indicate good functionality, a comprehensive interaction interface.
Vast majority of wireless appliances used in household, industry and medical field share the ISM frequency band. These devices need to coexist and thus are challenged to tolerate their mutual interference. One way of dealing with this is by using frequency hopping; where the device changes its radio channel periodically. Consequently, communications will not suffer from the same interference each time; instead, it should be fairer and more stable. This thesis investigates the aforementioned problem in the field of low power wireless sensor networks and Internet of Things where Contiki OS is used. We introduce a low-power pseudo-random frequency-hopping MAC protocol which is specifically characterized as a duty cycled asynchronous sender-initiated LPL style protocol. We illustrate two flavors of the protocol; one that does not use any dedicated channel and another which allows dedicated broadcast channels that can implement frequency-hopping as well. We implement the protocol in C for real hardware and extensively test and evaluate it in a simulated environment which runs Contiki. It proved to work with Contiki's IPv6 stack running RPL (the standardized routing protocol for low power and lossy wireless networks). We compare the performance of the implemented protocol to the singlechannel ContikiMAC with varying levels of interference. Results show a reduction down to 56% less radio-on time (1.50% vs. 3.4%) and 85% less latency (306 ms vs. 2050 ms) in the presence of noise, while keeping a good basecost in noise-free environments with 1.29% radio duty cycle when using 9 channels with no dedicated broadcast channels (vs. 0.80% for single channel) and 252 ms average latency(vs. 235 ms). Moreover, the results show that the multichannel protocol performance metrics converge to almost the same values regardless of the noise level. Therefore, it is recommended as a good alternative to single channel ContikiMAC in realworld deployments where noise presence is anticipated.
Exploiting multiple radio channels for communication has been long known as a practical way to mitigate interference in wireless settings. In Wireless Sensor Networks, however, multi-channel solutions have not reached their full potential: the MAC layers included in TinyOS or the Contiki OS for example are mostly single-channel. The literature offers a number of interesting solutions, but experimental results were often too few to build confidence. We propose a practical extension of low-power listening, MiCMAC, that performs channel hopping, operates in a distributed way, and is independent of upper layers of the protocol stack. The above properties make it easy to deploy in a variety of scenarios, without any extra configuration/scheduling/channel selection hassle. We implement our solution in Contiki and evaluate it in a 97-node testbed while running a complete, out-of-the-box low-power IPv6 communication stack (UDP/RPL/6LoWPAN). Our experimental results demonstrate increased resilience to emulated WiFi interference (e.g., data yield kept above 90% when ContikiMAC drops in the 40% range). In noiseless environments, MiCMAC keeps the overhead low in comparison to ContikiMAC, achieving performance as high as 99% data yield along with sub-percent duty cycle and sub-second latency for a 1-minute inter-packet interval data collection.
Many protocols in low-power wireless networks require a leader to bootstrap and maintain their operation. For example, Chaos and Glossy networks need an initiator to synchronize and initiate the communication rounds. Commonly, these protocols use a fixed, compile-time defined node as the leader. In this work, we tackle the challenge of dynamically bootstrapping the network and electing a leader in low-power wireless scenarios.
Many protocols in low-power wireless networks require a root nodeor a leader to bootstrap and maintain its operation. For example,Chaos and Glossy networks need an initiator to synchronize andinitiate the communications rounds. Commonly, these protocolsuse a xed, compile-time dened node as the leader. In this work,we tackle the challenge of dynamically bootstrapping the networkand electing a leader in low-power wireless scenarios, and we focuson Chaos-style networks
The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.