Gas pulsation is an efficient enhancing way for fluidized bed drying *** this work,the influence of gas pulsation on mass and heat transfer performance in a fluidized bed with non-spherical wet particles is numericall...
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Gas pulsation is an efficient enhancing way for fluidized bed drying *** this work,the influence of gas pulsation on mass and heat transfer performance in a fluidized bed with non-spherical wet particles is numerically investigated via the computational fluid dynamics−discrete element method(CFD-DEM)approach,where the liquid transfer between particles and the heat transfer by liquid bridge are *** aspect ratio effect of non-spherical particle on drying process is *** is found that the increase of aspect ratio can weaken the overall drying *** influence of gas pulsation on the drying of non-spherical particle is *** results reveal that adjusting a suitable gas pulsation mode can efficiently regulate the drying process of non-spherical wet particles with greater aspect ratios.
For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven mode...
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For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven models and physical models, each offering unique advantages but also facing ***-informed neural networks(PINNs) provide a robust framework to integrate data-driven models with physical principles, ensuring consistency with underlying physics while enabling generalization across diverse operational conditions. This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV) curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity, electrode capacities, and lithium inventory capacity. The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs) and is validated using a public dataset. The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests, with errors in reconstructed OCV curves remaining within 15 mV. This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level, advancing the potential for precise and efficient battery health management.
Based on the principles of thermodynamics, we elucidate the fundamental reasons behind the hysteresis of spontaneous polarization in ferroelectric materials during heating and cooling processes. By utilizing the effec...
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Based on the principles of thermodynamics, we elucidate the fundamental reasons behind the hysteresis of spontaneous polarization in ferroelectric materials during heating and cooling processes. By utilizing the effective Hamiltonian method in conjuction with the phase-field model, we have successfully reproduced the thermal hysteresis observed in ferroelectric materials during phase transitions. The computational results regarding the electrocaloric effect from these two different computational scales closely align with experimental measurements. Furthermore, we analyze how the first-order ferroelectric phase transition gradually diminishes with an increasing applied electric field, exhibiting characteristics of second-order-like phase transition. By employing the characteristic parameters of thermal hysteresis, we have established a pathway for calculations across different computational scales, thereby providing theoretical support for further investigations into the properties of ferroelectric materials through concurrent multiscale simulations.
Semitransparent organic photovoltaics(ST-OPVs) for building integration represent a pivotal direction in the development of photovoltaic industry. Solution-processed silver nanowires(AgNWs) are considered promising ca...
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Semitransparent organic photovoltaics(ST-OPVs) for building integration represent a pivotal direction in the development of photovoltaic industry. Solution-processed silver nanowires(AgNWs) are considered promising candidates for transparent electrodes in semitransparent devices due to their high transparency-conductivity-efficiency merit, large-scale processability, and low cost. In this work, we develop two solution-processed organic–inorganic hybrid electrodes, named AgNWs-PD and AgNWsPC, utilizing AgNWs as the conductive framework and aliphatic amine-functionalized perylene-diimide(PDINN) as the sandwiched material, while AgNWs-PC exhibits significantly improved electrical conductivity and enhanced contact area with the underlying electron transport layer. The optimized device achieves a power conversion efficiency of 9.45% with an open circuit voltage of 0.846 V, a high filling factor of 75.4%, and an average visible transmittance(AVT) of 44.0%, delivering an outstanding light utilization efficiency(LUE) of 4.16%, which is the highest reported value for all solution-processed ST-OPVs. In addition, by coupling a 30-nm tellurium dioxide atop AgNWs-PC, the bifaciality factor of derivative devices improves from 73.7% to 99.4%, while maintaining a high bifacial LUE over 3.7%. Our results emphasize the superiority and effectiveness of PDINN-sandwiched AgNWs electrodes for highperformance and all solution-processed ST-OPVs.
To predict the lithium-ion(Li-ion) battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries pr...
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To predict the lithium-ion(Li-ion) battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles' degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM) network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.
Cu-Mn co-doped CeO_(2) photocatalyst was successfully synthesized by the sol-gel method to assess its capability in degrading *** and TEM results showed that Cu and Mn were successfully co-doped into CeO_(2) without f...
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Cu-Mn co-doped CeO_(2) photocatalyst was successfully synthesized by the sol-gel method to assess its capability in degrading *** and TEM results showed that Cu and Mn were successfully co-doped into CeO_(2) without forming heterostructure,XPS and photoelectrochemical results revealed that Mn ions doping amplified the generation of photo-induced charge carriers,while Cu ions doping significantly facilitated the interfacial charge transfer ***,the optimized Cu3Mn2CeO_(2) nanoparticles exhibited the highest TC removal efficiency,achieved a rate of 78.18%and maintained a stable cycling performance.
The implementation of ultrahigh-Ni cathodes in high-energy lithium-ion batteries（LIBs） is constrained by significant structural and interfacial degradation during *** this study,doping-induced surface restructuring ...
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The implementation of ultrahigh-Ni cathodes in high-energy lithium-ion batteries（LIBs） is constrained by significant structural and interfacial degradation during *** this study,doping-induced surface restructuring in ultrahigh-nickel cathode materials is rapidly facilitated through an ultrafast Joule heating *** functional theory（DFT） calculations,synchrotron X-ray absorption spectroscopy（XAS）,and single-particle force test confirmed the establishment of a stable crystal framework and lattice oxygen,which mitigated H2-H3 phase transitions and improved structural ***,the Sc doping process exhibits a pinning effect on the grain boundaries,as shown by scanning transmission electron microscopy（STEM）,enhancing Li+diffusion kinetics and decreasing mechanical strain during *** in situ development of a cation-mixing layer at grain boundaries also creates a robust cathode/electrolyte interphase,effectively reducing interfacial parasitic reactions and transition metal dissolution,as validated by STEM and time-of-flight secondary ion mass spectrometry（TOF-SIMS）.These synergistic modifications reduce particle cracking and surface/interface degradation,leading to enhanced rate capability,structural integrity,and thermal ***,the optimized Sc-modified ultrahigh-Ni cathode（Sc-1） exhibits 93.99% capacity retention after 100 cycles at 1 C（25 ℃） and87.06% capacity retention after 100 cycles at 1 C（50℃）,indicating excellent cycling and thermal *** presenting a one-step multifunctional modification approach,this research delivers an extensive analysis of the mechanisms governing the structure,microstructure,and interface properties of nickel-rich layered cathode materials（NCMs）.These results underscore the potential of ultrahigh-Ni cathodes as viable candidates for advanced lithium-ion batteries（LIBs） in next-generation electric vehicles（EVs）.
As an important and effective indicator of contact heat transfer,thermal contact resistance is a widespread phenomenon in *** can directly affect product reliability,full-load performance,power consumption and even li...
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As an important and effective indicator of contact heat transfer,thermal contact resistance is a widespread phenomenon in *** can directly affect product reliability,full-load performance,power consumption and even life cycle in energy,aerospace,electronic packaging,cryogenic refrigeration,***,enhancing the interface heat transfer and suppressing thermal contact resistance have become increasingly *** this background,this paper seeks to elaborate on conceptions of thermal contact resistance and the ways to reduce *** reviewing the existing methods of measuring thermal contact resistance and characterizing the interface morphology,we highlight the theoretical underpinnings of thermal contact resistance,including the two-dimensional mathematic characteristics of the contact interface and the theoretical and empirical models for quantifying *** categories of influencing factors,i.e.,thermal,geometrical and mechanical states,are then *** on the macroscopic formation mechanism,the paper summarizes the existing methods for suppressing thermal contact resistance,with close attention paid to polymer composite thermal interfacial materials and metal interfacial materials filled with high thermal conductivity *** light of the findings,this review provides five promising directions for future research on thermal contact *** suggests that the failure modes and service life of interface materials are essential to apply such technologies to suppress thermal contact resistance in *** review will be a guide for future research in thermal contact resistance and for the widespread use of composite interface materials.
People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces....
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People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.
Aided by device-to-device(D2D) connections, unmanned aerial vehicle(UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2...
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Aided by device-to-device(D2D) connections, unmanned aerial vehicle(UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2D, where two fixed-wing UAVs as aerial base stations cooperatively serve the ground devices. To accommodate more devices, we propose two effective algorithms to establish the multi-hop D2D connections. Then, the user scheduling, UAV trajectory, and transmit power are jointly optimized to maximize the energy efficiency, which is a non-convex problem. Accordingly, we decompose it into three subproblems. The scheduling optimization is first converted into a linear programming. Then, the trajectory design and the transmit power optimization are reformulated as two convex problems by the Dinkelbach method. Finally, an iterative algorithm is proposed to effectively solve the original problem. Simulation results are presented to verify the effectiveness of the proposed scheme.
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