112 research outputs found
Preparation and Mechanical Properties of Continuous Carbon Nanotube Networks Modified C f
Continuous carbon nanotube (CNT) networks were formed in Cf/SiC composites via freeze-drying method. Composites were fabricated by precursor infiltration and pyrolysis (PIP) process afterwards. The different distribution morphologies of CNTs in the preforms originating from the different CNT contents were analyzed while the influence of the distribution of CNTs was discussed in detail. Compared to composites without CNTs, the interfacial shear strength (ILSS) and the flexural strength of Cf/1%CNTs/SiC were increased by 31% and 27%, respectively, but the values of Cf/2.5%CNTs/SiC decreased as a result of lots of defects caused by excess CNTs. With the analysis of ILSS, the flexural strengths, and the fracture morphologies, CNTs effectively improved the weak interfacial strength between T700SC carbon fibers and SiC matrix
Enhancing GAN-Based Vocoders with Contrastive Learning Under Data-limited Condition
Vocoder models have recently achieved substantial progress in generating
authentic audio comparable to human quality while significantly reducing memory
requirement and inference time. However, these data-hungry generative models
require large-scale audio data for learning good representations. In this
paper, we apply contrastive learning methods in training the vocoder to improve
the perceptual quality of the vocoder without modifying its architecture or
adding more data. We design an auxiliary task with mel-spectrogram contrastive
learning to enhance the utterance-level quality of the vocoder model under
data-limited conditions. We also extend the task to include waveforms to
improve the multi-modality comprehension of the model and address the
discriminator overfitting problem. We optimize the additional task
simultaneously with GAN training objectives. Our result shows that the tasks
improve model performance substantially in data-limited settings. Our analysis
based on the result indicates that the proposed design successfully alleviates
discriminator overfitting and produces audio of higher fidelity
Lightweight conductive graphene/thermoplastic polyurethane foams with ultrahigh compressibility for piezoresistive sensing
Lightweight conductive porous graphene/thermoplastic polyurethane (TPU) foams with ultrahigh compressibility were successfully fabricated by using the thermal induced phase separation (TISP) technique. The density and porosity of the foams were calculated to be about 0.11 g cm−3 and 90% owing to the porous structure. Compared with pure TPU foams, the addition of graphene could effectively increase the thickness of the cell wall and hinder the formation of small holes, leading to a robust porous structure with excellent compression property. Meanwhile, the cell walls with small holes and a dendritic structure were observed due to the flexibility of graphene, endowing the foam with special positive piezoresistive behaviors and peculiar response patterns with a deflection point during the cyclic compression. This could effectively enhance the identifiability of external compression strain when used as piezoresistive sensors. In addition, larger compression sensitivity was achieved at a higher compression rate. Due to high porosity and good elasticity of TPU, the conductive foams demonstrated good compressibility and stable piezoresistive sensing signals at a strain of up to 90%. During the cyclic piezoresistive sensing test under different compression strains, the conductive foam exhibited good recoverability and reproducibility after the stabilization of cyclic loading. All these suggest that the fabricated conductive foam possesses great potential to be used as lightweight, flexible, highly sensitive, and stable piezoresistive sensors
A method for determining unsaturated strength parameters in stability analysis of loess slope
In recent years, with the rapid development of social economy and progress of human activities in loess area, the Loess Plateau become one of the areas with the most serious soil erosion and the most frequent occurrence of geological disasters in the world. Landslide, collapse, debris flow and ground subsidence are common geological disasters in the Loess Plateau, resulting in a more fragile ecological environment. Therefore, it is very important to accurately predict the stability of loess slope for engineering safety and ecological protection in loess region. But loess is a typical unsaturated soil. the formula of unsaturated strength is seldom used in practical applications. The reason is that the matric suction is difficult to measure. And it cannot be applied in engineering practice. In this paper, based on unsaturated soil shear strength formula of Fredlund, the direct shear test under different moisture content is conducted with the samples of Q1 loess. The effective cohesion and the effective internal friction angle are obtained. Through the matric suction test, the soil-water characteristic curve is plotted. Combined cohesion and matric suction, the strength parameters of unsaturated loess in formula of Fredlund can be informed
Magnetic vortex and unsaturated magnetization components in highly oriented pyrolytic graphite
Observation of ferromagnetic and granular superconductive features in highly-oriented-pyrolytic-graphite (HOPG) has recently attracted an important attention. We report a novel temperature dependent XRD and SQUID investigation of HOPG in the temperature range from 300.15 to 77.15 K. Unusual hysteresis features indicate the possible presence of vortex states in conditions of magnetic field approximately perpendicular to the HOPG layers. This interpretation is further supported by additional measurements performed on intermediate lamellae extracted by exfoliation. Evidence of a possible structural-transition in the c-axis of HOPG in the temperature range between 77 K and 100K is also provided by using the Rietveld refinement method. ZFC and FC measurements performed at high field values of 5000-10000 Oe, together with mFC-mZFC subtraction, highlight absence of a sharp depletion of the difference between magnetization signals towards zero. These observations may indicate the possible presence of additional unsaturated weak features, which are ascribed to superconductive signals as previously predicted by Scheike et al. [8]
Evidence of spin density waves in LaNiO
The recently discovered superconductivity with critical temperature
up to 80 K in the Ruddlesden-Popper phases LaNiO under
pressure has drawn great attention. Here we report the positive muon spin
relaxation (SR) study of polycrystalline LaNiO
under ambient pressure. The zero-field SR experiments reveal the
existence of static long range magnetic order in LaNiO,
and the the muon spin depolarization spectra are consistent with the spin
density wave internal field distribution. The weak transverse field SR
measurements suggest the bulk magnetic transition near K. This
is the first research which discovers the existence of the spin density wave in
LaNiO microscopically
Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process
A digital twin (DT) is a virtual representation of physical process, products
and/or systems that requires a high-fidelity computational model for continuous
update through the integration of sensor data and user input. In the context of
laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the
manufacturing process can offer predictions for the produced parts, diagnostics
for manufacturing defects, as well as control capabilities. This paper
introduces a parameterized physics-based digital twin (PPB-DT) for the
statistical predictions of LPBF metal additive manufacturing process. We
accomplish this by creating a high-fidelity computational model that accurately
represents the melt pool phenomena and subsequently calibrating and validating
it through controlled experiments. In PPB-DT, a mechanistic reduced-order
method-driven stochastic calibration process is introduced, which enables the
statistical predictions of the melt pool geometries and the identification of
defects such as lack-of-fusion porosity and surface roughness, specifically for
diagnostic applications. Leveraging data derived from this physics-based model
and experiments, we have trained a machine learning-based digital twin
(PPB-ML-DT) model for predicting, monitoring, and controlling melt pool
geometries. These proposed digital twin models can be employed for predictions,
control, optimization, and quality assurance within the LPBF process,
ultimately expediting product development and certification in LPBF-based metal
additive manufacturing.Comment: arXiv admin note: text overlap with arXiv:2208.0290
Corrigendum: Therapy of spinal cord injury by folic acid polyethylene glycol amine-modified zeolitic imidazole framework-8 nanoparticles targeted activated M/Ms
Infrared Imaging of Magnetic Octupole Domains in Non-collinear Antiferromagnets
Magnetic structure plays a pivotal role in the functionality of
antiferromagnets (AFMs), which not only can be employed to encode digital data
but also yields novel phenomena. Despite its growing significance, visualizing
the antiferromagnetic domain structure remains a challenge, particularly for
non-collinear AFMs. Currently, the observation of magnetic domains in
non-collinear antiferromagnetic materials is feasible only in MnSn,
underscoring the limitations of existing techniques that necessitate distinct
methods for in-plane and out-of-plane magnetic domain imaging. In this study,
we present a versatile method for imaging the antiferromagnetic domain
structure in a series of non-collinear antiferromagnetic materials by utilizing
the anomalous Ettingshausen effect (AEE), which resolves both the magnetic
octupole moments parallel and perpendicular to the sample surface. Temperature
modulation due to the AEE originating from different magnetic domains is
measured by the lock-in thermography, revealing distinct behaviors of octupole
domains in different antiferromagnets. This work delivers an efficient
technique for the visualization of magnetic domains in non-collinear AFMs,
which enables comprehensive study of the magnetization process at the
microscopic level and paves the way for potential advancements in applications.Comment: National Science Review in pres
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