Electric-Field-Induced Reversible Phase Transitions in a Spontaneously Ion-Intercalated 2D Metal Oxide.

Electric field driven reversible phase transitions in two-dimensional (2D) materials are appealing for their potential in switching applications. Here, we introduce potassium intercalated MnO2 as an exemplary case. We demonstrate the synthesis of large-area single-crystal layered MnO2 via chemical vapor deposition as thin as 5 nm. These crystals are spontaneously intercalated by potassium ions during the synthesis. We showed that the charge transport in 2D K-MnO2 is dominated by motion of hydrated potassium ions in the interlayer space. Under a few volts bias, separation of potassium and the structural water leads to formation of different phases at the opposite terminals, and at larger biases K-MnO2 crystals exhibit reversible layered-to-spinel phase transition. These phase transitions are accompanied by electrical and optical changes in the material. We used the electric field driven ionic motion in K-MnO2 based devices to demonstrate the memristive capabilities of two terminal devices.

High elasticity and strength of ultra-thin metallic transition metal dichalcogenides.

Mechanical properties of transition metal dichalcogenides (TMDCs) are relevant to their prospective applications in flexible electronics. So far, the focus has been on the semiconducting TMDCs, mostly MoX2 and WX2 (X = S, Se) due to their potential in optoelectronics. A comprehensive understanding of the elastic properties of metallic TMDCs is needed to complement the semiconducting TMDCs in flexible optoelectronics. Thus, mechanical testing of metallic TMDCs is pertinent to the realization of the applications. Here, we report on the atomic force microscopy-based nano-indentation measurements on ultra-thin 2H-TaS2 crystals to elucidate the stretching and breaking of the metallic TMDCs. We explored the elastic properties of 2H-TaS2 at different thicknesses ranging from 3.5 nm to 12.6 nm and find that the Young's modulus is independent of the thickness at a value of 85.9 ± 10.6 GPa, which is lower than the semiconducting TMDCs reported so far. We determined the breaking strength as 5.07 ± 0.10 GPa which is 6% of the Young's modulus. This value is comparable to that of other TMDCs. We used ab initio calculations to provide an insight into the high elasticity measured in 2H-TaS2. We also performed measurements on a small number of 1T-TaTe2, 3R-NbS2 and 1T-NbTe2 samples and extended our ab initio calculations to these materials to gain a deeper understanding on the elastic and breaking properties of metallic TMDCs. This work illustrates that the studied metallic TMDCs are suitable candidates to be used as additives in composites as functional and structural elements and for flexible conductive electronic devices.

Characterization of local SARS-CoV-2 isolates and pathogenicity in IFNAR-/- mice.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recently a global pandemic with unprecedented public health, economic and social impact. The development of effective mitigation strategies, therapeutics and vaccines relies on detailed genomic and biological characterization of the regional viruses. This study was carried out to isolate SARS-CoV-2 viruses circulating in Anatolia, and to investigate virus propagation in frequently-used cells and experimental animals. We obtained two SARS-CoV-2 viruses from nasopharngeal swabs of confirmed cases in Vero E6 cells, visualized the virions using atomic force and scanning electron microscopy and determined size distribution of the particles. Viral cytopathic effects on Vero E6 cells were initially observed at 72 h post-inoculation and reached 90% of the cells on the 5th day. The isolates displayed with similar infectivity titers, time course and infectious progeny yields. Genome sequencing revealed the viruses to be well-conserved, with less than 1% diversity compared to the prototype virus. The analysis of the viral genomes, along with the available 62 complete genomes from Anatolia, showed limited diversity (up to 0.2% on deduced amino acids) and no evidence of recombination. The most prominent sequence variation was observed on the spike protein, resulting in the substitution D614G, with a prevalence of 56.2%. The isolates produced non-fatal infection in the transgenic type I interferon knockout (IFNAR-/-) mice, with varying neutralizing antibody titers. Hyperemia, regional consolidation and subpleural air accumulation was observed on necropsy, with similar histopathological and immunohistochemistry findings in the lungs, heart, stomach, intestines, liver, spleen and kidneys. Peak viral loads were detected in the lungs, with virus RNA present in the kidneys, jejunum, liver, spleen and heart. In conclusion, we characterized two local isolates, investigated in vitro growth dynamics in Vero E6 cells and identified IFNAR-/- mice as a potential animal model for SARS-CoV-2 experiments.

Diagnosis of bearing defects under variable speed conditions using energy distribution maps of acoustic emission spectra and convolutional neural networks.

This letter proposes an efficient scheme for the early diagnosis of bearing defects using a convolutional neural network (CNN) and energy distribution maps (EDMs) of acoustic emission spectra. The CNN automates the process of feature extraction from the EDM. The features learned by the CNN are used by an ensemble classifier, that is, a combination of a multilayer perceptron that is integral to typical CNN architectures and a support vector machine to diagnose bearing defects. The experimental results confirm that the proposed scheme diagnoses bearing defects more effectively than existing methods under variable speed conditions.

A Reliable Health Indicator for Fault Prognosis of Bearings.

Estimation of the remaining useful life (RUL) of bearings is important to avoid abrupt shutdowns in rotary machines. An important task in RUL estimation is the construction of a suitable health indicator (HI) to infer the bearing condition. Conventional health indicators rely on features of the vibration acceleration signal and are predominantly calculated without considering its non-stationary nature. This often results in an HI with a trend that is difficult to model, as well as random fluctuations and poor correlation with bearing degradation. Therefore, this paper presents a method for constructing a bearing's HI by considering the non-stationarity of the vibration acceleration signals. The proposed method employs the discrete wavelet packet transform (DWPT) to decompose the raw signal into different sub-bands. The HI is extracted from each sub-band signal, smoothened using locally weighted regression, and evaluated using a gradient-based method. The HIs showing the best trends among all the sub-bands are iteratively accumulated to construct an HI with the best trend over the entire life of the bearing. The proposed method is tested on two benchmark bearing datasets. The results show that the proposed method yields an HI that correlates well with bearing degradation and is relatively easy to model.

Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm.

This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing's speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds.

Incipient fault diagnosis in bearings under variable speed conditions using multiresolution analysis and a weighted committee machine.

Incipient defects in bearings are traditionally diagnosed either by developing discriminative models for features that are extracted from raw acoustic emission (AE) signals, or by detecting peaks at characteristic defect frequencies in the envelope power spectrum of the AE signals. Under variable speed conditions, however, such methods do not yield the best results. This letter proposes a technique for diagnosing incipient bearing defects under variable speed conditions, by extracting features from different sub-bands of the inherently non-stationary AE signal, and then classifying bearing defects using a weighted committee machine, which is an ensemble of support vector machines and artificial neural networks. The proposed method also improves the generalization performance of the neural networks to enhance their classification accuracy, particularly with limited training data.

Rotational speed invariant fault diagnosis in bearings using vibration signal imaging and local binary patterns.

Structural vibrations of bearing housings are used for diagnosing fault conditions in bearings, primarily by searching for characteristic fault frequencies in the envelope power spectrum of the vibration signal. The fault frequencies depend on the non-stationary angular speed of the rotating shaft. This paper explores an imaging-based approach to achieve rotational speed independence. Cycle length segments of the rectified vibration signal are stacked to construct grayscale images which exhibit unique textures for each fault. These textures show insignificant variation with the rotational speed, which is confirmed by the classification results using their local binary pattern histograms.