The solid–liquid interface energy anisotropy of Zn alloys remains poorly understood. Recently, characteristic 14-arm dendritic growth has been observed using time-resolved X-ray computed tomography at SPring-8 during...
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The sandwich heterostructures(SHSs)are novel two-dimensional materials that hold great potential as efficient *** this work,we computationally designed the BC3/TM/Gr SHSs by intercalat-ing transition metal atoms into ...
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The sandwich heterostructures(SHSs)are novel two-dimensional materials that hold great potential as efficient *** this work,we computationally designed the BC3/TM/Gr SHSs by intercalat-ing transition metal atoms into the BC3/graphene *** the computational screening,only BC3/Sc/Gr,BC3/Ti/Gr,BC3/Y/Gr and BC3/Zr/Gr are validated as stable *** electron donation from the intercalated TM atom results in the formation of the negatively charged boron atom(Bδ-)and activation of the BC3 surface,making the BC3/TM/Gr SHSs highly promising as single-atom catalysts(SACs).The BC3/Sc/Gr and BC3/Y/Gr SHSs exhibit potential in carbon dioxide reduction reaction(CO2RR)and carbon monoxide reduction reaction(CORR)***,when BC3/Y/Gr SHS serves as CORR electro-catalyst,the step(*CHO→*CHOH)is a potential determining step,with an extremely low limiting potential(UL=-0.10 V).The BC3/Ti/Gr and BC3/Zr/Gr SHSs are suitable as hydrogen evolution reaction(HER)***,the BC3/Ti/Gr SHS serves as an ideal HER electro-catalyst in acid condi-tion,with close-to-zero adsorption free energy(ΔGH=0.006 eV)and fairly low overall activation barrier(0.20 eV).By analyzing the electronic properties,the unique adsorption activity of the Bδ-on H atom and unsaturated CO2RR intermediates is elucidated as the origin of excellent catalytic activity of BC3/TM/Gr SHSs,which is modulated by the intercalated TM *** work is instructive to rational design of SACs towards energy conversion based on non-metal elements.
Ring artifacts are common artifacts in X-ray Computed Tomography (XCT) scans and have a significant impact on subsequent feature/phase extractions due to the small grayscale gradients in XCT volume data of bulk materi...
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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 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.
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.
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).
Bio-inspired helicoidal composite laminates,inspired by the intricate helical structures found in nature,present a promising frontier for enhancing the mechanical properties of structural ***,this study provides a com...
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Bio-inspired helicoidal composite laminates,inspired by the intricate helical structures found in nature,present a promising frontier for enhancing the mechanical properties of structural ***,this study provides a comprehensive investigation into the nonlinear free vibration and nonlinear bending behavior of bio-inspired composite *** inverse hyperbolic shear deformation theory(IHSDT)of plates is employed to characterize the displacement field,with the incorporation of Green-Lagrange *** problem is modeled using the C0finite element method(FEM),and an in-house code is developed in the MATLAB environment to solve it *** helicoidal layup configurations including helicoidal recursive(HR),helicoidal exponential(HE),helicoidal semi-circular(HS),linear helicoidal(LH),and Fibonacci helicoidal(FH)with different layup sequences and quasi-isotropic configurations are *** model is validated,and parametric studies are *** studies investigate the effects of layup configurations,side-to-thickness ratio,modulus ratios,boundary conditions,and loading conditions at different load amplitudes on the nonlinear vibration and nonlinear bending behaviors of bio-inspired composite *** results show that the laminate sequence exerts a substantial impact on both nonlinear natural frequencies and nonlinear bending ***,this influence varies across different side-to-thickness ratios and boundary conditions of the bio-inspired composite plate.
This study systematically investigated the abundance, characteristics, and spatial and temporal variations of microplastics (MPs) in the lower Chao Phraya River, Thailand, during the COVID-19 pandemic. The study revea...
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Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,in...
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Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
Three sets of MXene(Ti_(3)C_(2)T_(x))@nano-Fe_(1)Co_(0.8)Ni_(1)composites with 15,45,and 90 mg MXene were prepared by in-situ liquid-phase deposition to effectively investigate the impact of the relationship between M...
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Three sets of MXene(Ti_(3)C_(2)T_(x))@nano-Fe_(1)Co_(0.8)Ni_(1)composites with 15,45,and 90 mg MXene were prepared by in-situ liquid-phase deposition to effectively investigate the impact of the relationship between MXene(Ti_(3)C_(2)T_(x))and nano-Fe_(1)Co_(0.8)Ni_(1)magnetic particles on the electromagnetic absorption properties of the *** microstructure,static magnetic properties,and electromag-netic absorption performance of these composites were *** indicate that the MXene@nano-Fe_(1)Co_(0.8)Ni_(1)composites were primarily composed of face-centered cubic crystal structure particles and MXene,with spherical Fe_(1)Co_(0.8)Ni_(1)particles uniformly distrib-uted on the surface of the multilayered *** alloy particles had an average particle size of approximately 100 nm and exhibited good dispersion without noticeable particle *** the increase in MXene content,the specific saturation magnetic and coer-civity of the composite initially decreased and then increased,displaying typical soft magnetic *** with those of the Fe_(1)Co_(0.8)Ni_(1)magnetic alloy particles alone,MXene addition caused an increasing trend in the real and imaginary parts of the dielectric constant of the ***,the real and imaginary parts of the magnetic permeability exhibit decreasing *** the in-crease in MXene addition,the material attenuation constant increased and the impedance matching *** minimum reflection loss increased,and the maximum effective absorption bandwidth *** the MXene addition was 90 mg,the composite exhib-ited a minimum reflection loss of-46.9 dB with a sample thickness of 1.1 mm and a maximum effective absorption bandwidth of 3.60 GHz with a sample thickness of 1.0 *** effective absorption bandwidth of the composites and their corresponding thicknesses showed a decreasing trend with the increase in MXene addition,reducing by 50%from 1.5 mm without MXene addition to 1 mm with 9
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