Energy Loss Research Articles

Analysis of the Ag M4,5 EELS edge to study silver nanoparticle corrosion.

Electron energy loss spectra collected from fresh and corroded silver nanoparticles are compared with those from a number of reference materials, focusing on the M4,5 edge. Chemical shifts and changes in the energy loss near edge structure (ELNES) are described and found to be sufficient to distinguish metallic silver from chemically oxidised silver. The measurements, in conjunction with electron energy loss spectrum imaging, are used to assess the mechanisms for atmospheric corrosion of silver nanoparticles. We unambiguously assign the corrosion product under atmospheric conditions to be silver sulphide, but show the reaction process to be distinctly inhomogeneous, producing a variety of types of corroded particles. LAY DESCRIPTION: >Here, we use analytical electron microscopy to track the corrosion of silver nanoparticles and present chemical maps of the corrosion products. We show clear spectroscopic differences between metallic and corroded silver using the M4,5 electron energy loss spectral feature, which is not commonly studied. Our study shows that corrosion is due to interactions with sulphur in the atmosphere; and the corrosion is not uniform, but appears to develop from specific points on the surface of the nanoparticles.

Entropy optimization on EMHD Casson Williamson penta-hybrid nanofluid over porous exponentially vertical cone

Present inquiry explores steady electro magneto hydrodynamic flow on porous exponentially vertical cone of Casson Williamson nanofluid. In this novel research work, we have successfully developed a multipurpose and adaptable penta-hybrid nanofluid by mixing nanoparticles of silver (Ag), gold (Au), magnesium oxide (MgO), copper (Cu), titanium dioxide (TiO2) with base fluid and are used in many innovative applications, including coatings, sensors, energy storage, water purification, enhanced heat transfer, biological purposes and lubricants. Numerous industries can benefit considerably from the combined features of these nanoparticles, which can improve the base fluid’s functioning and performance. Further, it aims to determine thermal and mass transportation of penta-hybrid nanofluid. The controlling PDEs can be turned into non-linear ODEs via similarity conversions, solved using fifth order Runge-Kutta-Fehlberg technique via shooting technique. Penta-hybrid nanofluid/ C2H6O2is excelled in heat transport to greater extent than other hybrid nanofluids with 62.6 %. Moreover, entropy generation rate is performed, and irreversibility demonstrates that, compared to previous efforts, total entropy generation rate minimize energy loss by 247.16 % in present study. Estimating impact of flow patterns and transmission rates are useful application with varied industrial and engineering applications, environmental restoration, and biomedical applications such as targeted medication administration.

Workpiece surface laser regulation in laser-induced plasma electrolyte jet machining: Position, shape, and energy

Laser-induced plasma electrolyte jet machining is a new hybrid machining method. This machining method utilizes the plasma impact effect and plasma to increase the inter-pole conductivity for material removal, with low laser energy loss, high material removal rate (MRR), and good surface quality. The advantages of laser machining and electrolyte jet machining are fully utilized. However, it is difficult to avoid refraction of the laser in the electrolyte column, which affects the efficient coupling of the laser to the electrolyte jet machining. In this paper, to improve the localization and MRR, a study is carried out to address the problem of energy utilization efficiency in laser-induced plasma electrolyte jet machining. A compensation model for the refraction of the laser beam in the electrolyte is proposed. Compensation improves the shape and positional accuracy of the laser beam coupled to the electrolyte beam during aluminum alloy machining. Compared before and after compensation, the MRR was improved by 30.2 %, and the groove localization was improved by 37.4 %. Realization of high energy coupling for laser electrolyte jet hybrid machining.

Energy Efficiency for Faster-than-Nyquist Data Transmission Using Processing Algorithms with Decision Feedback

One of the ways to increase the volume of transmitted information is to increase the bit rate above the Nyquist barrier. However, an increase in bit rate in the case of FTN (Faster-Than-Nyquist) signals leads to an increase in energy costs for receiving information on channels with limited bandwidth, for example, in Digital Video Broadcasting satellite systems like DVB-S2/S2X. It is possible to minimize energy losses by using the processing algorithm “maximum likelihood sequence estimation”. However, the computational complexity of this algorithm is extremely high, which limits its use, especially in terrestrial mobile satellite terminals. We propose a new bit-by-bit decision feedback algorithm with maximum likelihood ratio estimation of subsequent symbols in the observation interval. This algorithm provides minimal energy costs comparable to the method “maximum likelihood sequence estimation” at speeds 2–3 times higher than the Nyquist barrier. At the same time, the complexity is two orders of magnitude less. It is shown by simulation for a channel with additive noise that energy losses in relation to the potential bit error rate (BER) are less than 4.5 dB. In the presence of Rayleigh fading, the application of the proposed algorithm makes it possible to provide the processing of FTN signals for double bit rates in urban areas with energy costs equal to 12 dB when using an equalizer. We give numerical estimations of the increase in computational complexity for the proposed processing algorithm. It is shown that an increase in the bit rate by 1.5 times leads to an increase in the computational complexity by more than an order of magnitude. The same conclusion can be reformulated in another form: for the proposed algorithm, each decibel of energy gain is achieved by increasing the number of computational operations by 1.5×105. It is experimentally shown that additional energy losses due to non-ideal phase and timing synchronization are no more than 1 dB when the proposed algorithm is applied in a fading channel. The energy costs in fading channels relative to a stationary channel for twice the Nyquist rate are equal to 13.8 dB when using an equalizer.

Learnable Dual Attention Fusion Network (LDAFNet) For Borehole DAS Data Reconstruction.

As the demand of important non-renewable oil and gas resources increase, distributed optical fiber acoustic sensing systems have been widely used in the acquisition of vertical seismic profile (VSP) data relying on its various advantages. The complicated underground environment produces many types of noise with strong energy. Meanwhile, the effective information is absorbed by the formation reflection and refraction, resulting in obvious energy loss and aliased frequency characteristics. Satisfactory processing results and quality cannot be achieved through traditional denoising methods. In recent years, rapidly developed deep learning methods with attention mechanism have greatly attracted researchers. Noise reduction and signals reconstruction technologies with multiple attention mechanism under the framework of joint-domain features is becoming an important development direction of DAS data processing. Attention mechanism can help the network ignore useless information and pay attention to useful information like humans. This paper presents a novel learnable dual attention fusion network (LDAFNet). It combines local and patch-based non-local attention mechanisms together based on the ResBlock modules and makes the extraction process of the network learnable and selective according to different signals or noise. In addition, the novel network can focus on the local complex information and those from the global level. Meanwhile, the residual learning strategy is used to simplify the training process to gradually establish the best mapping under the guidance of L2 loss function. All the experimental results of the simulation and actual records show that our novel network has better denoising and effective signals restoration ability. It can clearly recover the upward and downward wave fields and attenuate different types of noise with less effective energy loss.

Impact of Lean-Burn Combustor Flow on Nozzle Guide Vane Performance

Abstract In this paper we investigate the impact of lean-burn-representative swirl and temperature distortion on the aerothermal performance of fully-cooled high-pressure nozzle guide vanes (NGVs) from a modern aero-engine. Experiments were carried out in the Engine Component AeroThermal (ECAT) facility at the University of Oxford. This is a fully-annular warm-flow engine parts facility, designed to operate at engine-representative conditions of Reynolds and Mach number. Inlet profiles of swirl, turbulence, and non-dimensional total temperature were generated using a non-reacting combustor simulator. The NGV outlet flow was experimentally characterized at three downstream planes in experiments with and without lean-burn-representative inlet conditions. Area-survey measurements included distributions of whirl angle, kinetic energy (KE) loss, and non-dimensional total temperature. Experimental data is compared to computational fluid dynamics (CFD) simulations. Fully-featured NGV geometry (including film cooling holes and internal passages) was used, to account for internal cooling flow redistribution resulting from altered external loading. We show that lean-burn inlet conditions result in significant surface flow redistribution, relatively high levels of residual swirl in the downstream flow, and a small increase of integrated KE loss.

Self‐Powered Supercapacitor with Bifunctional Separator in Suppressing Self‐Discharge

AbstractIntegrating energy harvesters with energy storage devices helps enhance energy utilization efficiency. The requirement for external power sources in energy storage devices can also be addressed by the development of an all‐in‐one single system. Vegetable peels can be used as piezoelectric energy harvesters due to the generation of electric dipole within cellulose. The self‐discharge processes cause open circuit potential decay, resulting in a loss of energy stored within the cell. The self‐discharge rate can be controlled by using a piezoelectric separator. This work used garlic skins as a piezoelectric separator, which helped delay the self‐discharge rate. The supercapacitor using Ni−Mn oxide as positive and Fe−Ni sulfide/RGO as negative electrode materials was fabricated with garlic skin as a separator. The device was charged from an initial OCP of ~0.15–~0.37 V in ~ 4 sec by finger tapping. The OCP decay rate was reduced by~88 % compared to that of using the Whatmann separator. The fabricated Ni−Mn oxide RGO/garlic skin/FeS2 RGO exhibited ~72.2 W h kg−1 energy density at a power density of ~2 kW kg−1 in an operated potential window of ~2 V. This research provides a cost‐effective method for developing piezoelectric supercapacitor with low self‐discharge rate.

Leading order track functions in a hot and dense QGP

We study the modifications to the fragmentation pattern of partons into charged particles in the presence of a hot and dense quark gluon plasma. To this end, we analyze the perturbative renormalization group equations of the track functions, which describe the energy fraction carried by charged hadrons. Focusing on pure Yang-Mills theory, we compute the lowest-order moments of the medium-modified track functions, which are found to be sensitive to the reduced phase space for emissions in the medium and to energy loss. We use the extracted moments to calculate the energy energy correlator (EEC) on tracks in the collinear limit. The EEC on medium-evolved tracks does not differ qualitatively from the EEC on vacuum tracks despite being sensitive to the color decoherence transition and suppressing the distribution due to quenching, as seen in other jet observables. Published by the American Physical Society 2024

Modeling the Effect of Material Properties on Liquid-Alkaline Water Electrolysis

Abstract Liquid-alkaline water electrolyzers (LAWEs) use electricity to drive the conversion of water to H2 and O2 gas. These devices benefit from the use of low-cost nickel electrodes and metal-oxide separators, but suffer from lower current densities and higher cell voltages than proton-exchange-membrane water electrolyzers. Identifying the inefficiencies that result in this poor performance is necessary to mitigate losses and optimize LAWEs. Here, we report an experimentally-validated 1-D continuum model of a LAWE that elucidates the gradients within the cell, simulates H2 crossover, and projects the energy improvements made possible by modulating the properties of the electrodes and separator. The model captures the Nernstian polarization losses and the distribution of gas- and liquid-phases within the electrodes, enabling quantification of energy losses associated with kinetic, ohmic, and bubble-induced (mass-transport) resistances. Simulations demonstrate that LAWE can achieve energy intensities of 50 kWh kg—1 of H2 at 1 A cm—2 using improved electrode and separator properties.

Enhanced energy storage properties in (Bi0.5Na0.5)0.5Sr0.5TiO3- modified lead-free NaNbO3 antiferroelectric ceramics

The lead-free antiferroelectric material NaNbO3 (NN) is highly regarded for its exceptional breakdown electric field strength (Eb) and substantial recoverable energy storage density (Wrec). However, the significant energy loss of NN reduces its Wrec and η under a strong electric field, constraining its application in energy storage domains. This study explores a novel approach by integrating the relaxation ferroelectric (Bi1/2Na1/2)1/2Sr1/2TiO3 (BNST) into NN to create NN-BNST relaxation anti-ferroelectrics, aiming to enhance their energy storage capabilities. The incorporation of BNST not only effectively reduces the grain size of NN but also disrupts its long-range ordered structure, forming polar nanoregions (PNRs) that mitigate energy density loss. Both Wrec and η of NN ceramic are significantly improved with increasing the concentration of BNST. Optimal results are achieved with the 0.75NN-0.25BNST ceramics, which demonstrate a high energy storage density (Wrec = 3.83 J/cm3) and conversion efficiency (η = 76 %) while achieving an Eb of 236 kV/cm. The analysis of Eb enhancement in NN-BNST ceramics is conducted through considerations of grain size, band-gap width, and activation energy, offering a robust framework for developing new NN-based antiferroelectrics for energy storage applications.

Fusing Science with Industry: Perovskite Photovoltaics Moving Rapidly into Industrialization.

The organic-inorganic lead halide per materials have emerged as highly promising contenders in the field of photovoltaic technology, offering exceptional efficiency and cost-effectiveness. The commercialization of perovskite photovoltaics hinges on successfully transitioning from lab-scale perovskite solar cells to large-scale perovskite solar modules (PSMs). However, the efficiency of PSMs significantly diminishes with increasing device area, impeding commercial viability. Central to achieving high-efficiency PSMs is fabricating uniform functional films and optimizing interfaces to minimize energy loss. This review sheds light on the path toward large-scale PSMs, emphasizing the pivotal role of integrating cutting-edge scientific research with industrial technology. By exploring scalable deposition techniques and optimization strategies, the advancements and challenges in fabricating large-area perovskite films are revealed. Subsequently, the architecture and contact materials of PSMs are delved while addressing pertinent interface issues. Crucially, efficiency loss during scale-up and stability risks encountered by PSMs is analyzed. Furthermore, the advancements in industrial efforts toward perovskite commercialization are highlighted, emphasizing the perspective of PSMs in revolutionizing renewable energy. By highlighting the scientific and technical challenges in developing PSMs, the importance of combining science and industry to drive their industrialization and pave the way for future advancements is stressed.

Investigation of Dead Time Losses in Inverter Switching Leg Operation: GaN FET vs. MOSFET Comparison

This paper investigates the commutation transients of MOSFET and GaN FET devices in motor drive applications during hard-switching and soft-switching commutations at dead time operation. This study compares the switching behaviors of MOSFETs and GaN FETs, focusing on their performance during dead time in inverter legs for voltage source inverters. Experimental tests at various phase current levels reveal distinct switching characteristics and energy dissipation patterns. A validated simulation model estimates the experimental energy exchanged and dissipated during switching transients. The results demonstrate that GaN FETs exhibit lower overall losses at shorter dead times compared to MOSFETs, despite higher reverse conduction voltage drops. The study provides a quantitative framework for selecting optimal dead times to minimize energy losses, enhancing the efficiency of GaN FET-based inverters in low-voltage motor drive applications. Finally, a dead time optimization strategy is proposed and described.

Embedded Hybrid-Dimensional Heterointerface for Filament Modulation in 2D Material-Based Artificial Nociceptor.

Nociceptors are key sensory receptors that transmit warning signals to the central nervous system in response to painful stimuli. This fundamental process is emulated in an electronic device by developing a novel artificial nociceptor with an ultrathin, nonstoichiometric gallium oxide (GaOx)-silicon oxide heterostructure. A large-area 2D-GaOx film is printed on a substrate through liquid metal printing to facilitate the production of conductive filaments. This nociceptive structure exhibits a unique short-term temporal response following stimulation, enabling a facile demonstration of threshold-switching physics. The developed heterointerface 2D-GaOx film enables the fabrication of fast-switching, low-energy, and compliance-free 2D-GaOx nociceptors, as confirmed through experiments. The accumulation and extrusion of Ag in the oxide matrix are significant for inducing plastic changes in artificial biological sensors. High-resolution transmission electron microscopy and electron energy loss spectroscopy demonstrate that Ag clusters in the material dispersed under electrical bias and regrouped spontaneously when the bias is removed owing to interfacial energy minimization. Moreover, 2D nociceptors are stable; thus, heterointerface engineering can enable effective control of charge transfer in 2D heterostructural devices. Furthermore, the diffusive 2D-GaOx device and its Ag dynamics enable the direct emulation of biological nociceptors, marking an advancement in the hardware implementation of artificial human sensory systems.

Insulating Polymer Mediated Stability and Photovoltaic Performance of Organic Solar Cells: Influence of Molecular Weight

AbstractInsulating polymers are promising components for constructing high performance organic solar cells (OSCs) due to their advantages including low cost, excellent mechanical properties, and feasibility in morphology modulation. However, few studies have been conducted on the effect of molecular weight of insulating polymers on the performance of the corresponding OSCs. In this work, polymethyl methacrylate (PMMA) with different molecular weight (named as PMMAL and PMMAH) are incorporated into a range of polymer:nonfullerene photovoltaic systems. It is found that although both PMMAL and PMMAH can suppress the energetic disorder and the nonradiative energy loss, and lead to enhanced open‐circuit voltage in their corresponding OSCs, distinct mechanical property, operational stability as well as photovoltaic performance are observed. PMMAL can modulate the molecular packing of the host components more effectively due to its superior chain segment mobility during the film‐forming process, which can drive the host components to form more ordered molecular packing and therefore superior photovoltaic performance. On the other hand, due to the better miscibility of PMMAH with the host system, especially the C5‐16 acceptor, PMMAH can be well dispersed in the host and form a stable framework that provides superior mechanical properties and operational stability.

Effect of Molecular Conformation on Intermolecular Interactions and Photovoltaic Performances of Giant Molecule Acceptors

AbstractThe molecular conformation of giant molecule acceptors (GMAs) plays a significant role in regulating the intermolecular interactions and their photovoltaic performances in organic solar cells (OSCs). For the linear GMA GT‐l, the stronger homo‐molecular interaction causes its aggregation being weakly affected by the donor, thus forming an ordered molecular stacking and proper phase separation in its blend film. The star‐shaped GMA GT‐s‐based blend film shows a dominant hetero‐molecular interaction that suppresses the aggregation of the donor and acceptor, resulting in smaller phase separation and more uniform vertical phase distribution. While for another star‐shaped GMA GTs, the weakest hetero‐molecular interaction causes its blend film to form larger phase separation. Therefore, the GT‐l based OSC with PM6 as donor shows the highest charge mobilities, the fastest charge transfer (CT) process, reduced energy loss and less charge recombination, contributing to a higher power conversion efficiency (PCE) of 19.03%. Comparatively, the PCEs of the OSCs based on GTs and GT‐s are 18.05% and 17.58% respectively. Notably, all the three GMAs based OSCs show excellent thermal stability and long‐term storage stability. This study provides a facile strategy by tuning the linking unit and its connecting mode for designing highly efficient and stable organic photovoltaic materials.

Photon energy loss in ternary polymer solar cells based on nonfullerene acceptor as a third component

Photon energy loss in ternary polymer solar cells based on nonfullerene acceptor as a third component

A hybrid chaotic bat algorithm for optimal placement and sizing of dg units in radial distribution networks

Appropriate location and sizing of distributed generation (DG) units in a radial distribution system (RDS) play a significant role in the improvement of its overall performance. Indeed, proper DG placement and sizing help in maintaining the balance between power supply and demand, decreasing energy losses and enhancing voltage profile. Within this context, this paper presents an effective approach for the determination of the optimal location and the appropriate capacity of a photovoltaic distributed generation (PVDG) unit in RDSs. In the suggested approach, the best position of the PVDG is selected using the loss sensitivity factor (LSF). Meanwhile, a new optimization technique incorporating chaos, bats’ self-adaptive compensation, and Doppler effect into the original bat algorithm (BA) is developed to find the optimal size of the PVDG unit. The optimal PVDG size is optimally determined in such a way that total active power losses of the RDS is reduced and voltage profile is enhanced. The performance of the proposed optimization technique, symbolized by (CSA-DC)BA, is evaluated using various benchmark functions. Moreover, the applicability and effectiveness of the suggested approach are verified on the IEEE 33-bus and the IEEE 69-bus RDSs. Obtained results revealed that the proposed (CSA-DC)BA outperforms the comparison optimization techniques.

Photovoltaics Energy Potential in the Largest Greek Cities: Atmospheric and Urban Fabric Effects, Climatic Trends Influences and Socio-Economic Benefits

This comprehensive study explores the influence of aerosols and clouds on solar radiation in the urban environments of nine of Greece’s largest cities over the decade from 2014 to 2023. Utilizing a combination of Earth Observation data, radiative transfer models, and geographic information systems, the research undertook digital surface modeling and photovoltaic simulations. The study meticulously calculated the optimal rooftop areas for photovoltaic installation in these cities, contributing significantly to their energy adequacy and achieving a balance between daily electricity production and demand. Moreover, the research provides an in-depth analysis of energy and economic losses, while also highlighting the environmental benefits. These include a reduction in pollutant emissions and a decrease in the carbon footprint, aligning with the global shift towards local energy security and the transformation of urban areas into green, smart cities. The innovative methodology of this study, which leverages open access data, sets a strong foundation for future research in this field. It opens up possibilities for similar studies and has the potential to contribute to the creation of an updated, comprehensive solar potential map for continental Greece. This could be instrumental in climate change mitigation and adaptation strategies, thereby promoting sustainable urban development and environmental preservation.

Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning

In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.

Eco-Friendly Polyurethane Foams Enriched with Waste from the Food and Energy Industries

In recent years, there has been considerable focus on ensuring that energy is used in the most efficient manner possible. This is due to the fact that globally, over 70% of energy is generated from fossil fuels. Consequently, the matter of designing and utilizing materials that will negate energy losses within the construction industry is of paramount importance. Simultaneously, the necessity for a sustainable approach to the design and production of materials is strongly emphasized. This paper presents an innovative approach to the use of a combination of mineral and plant-based fillers in polyurethane foam technology as a thermal insulation material with the potential to be used in construction to reduce energy consumption. Polyurethane composites containing fly ash from biomass combustion and the addition of rice, sunflower, and buckwheat husks as plant fillers were proposed. The structure of the obtained materials was studied, and the most important physical properties were analyzed. These included apparent density, dimensional stability, water absorption, and the effects of UV radiation and water influence on the carbon, hydrogen, nitrogen, and oxygen content. Moreover, the mechanical properties of the materials were investigated, including compressive strength and brittleness. Additionally, the foams were subjected to flammability tests using a cone calorimeter. Furthermore, additional parameters were determined, including the limiting oxygen index and the vertical and horizontal flammability tests. The results demonstrate the beneficial effects of combining mineral and vegetable fillers in polyurethane foam.