Abstract
Two-dimensional (2D) molybdenum ditelluride (MoTe
2
) exhibits an intriguing polymorphic nature, showing stable semiconducting 2H and metallic 1T′ phases at room temperature. Polymorphism in ...MoTe
2
presents new opportunities in developing phase-change memory, high- performance transistors, and spintronic devices. However, it also poses challenges in synthesizing homogeneous MoTe
2
with a precisely controlled phase. Recently, a new yet simple method using sputtering and 2D solid-phase crystallization (SPC) is proposed for synthesizing high-quality and large-area MoTe
2
. This study investigates the polymorphism control of MoTe
2
synthesis using 2D SPC. The Te/Mo ratio and oxygen content in the as-sputtered films correlate strongly with the final phase and electrical properties of SPC MoTe
2
. Furthermore, the SPC thermal budget may be exploited for stabilizing a deterministic phase. The comprehensive experiments presented in this work demonstrate the versatile and precise controllability on the MoTe
2
phase by using the simple 2D SPC technique.
Machine learning with artificial neural network (ANN)-based methods is a powerful tool for the prediction and exploitation of the subtle relationships between the composition and properties of ...materials. This work utilizes an ANN to predict the composition of high-entropy alloys (HEAs) based on non-equimolar AlCoCrFeMnNi in order to achieve the highest hardness in the system. A simulated annealing algorithm is integrated with the ANN to optimize the composition. A bootstrap approach is adopted to quantify the uncertainty of the prediction. Without any guidance, the design of new compositions of AlCoCrFeMnNi-based HEAs would be difficult by empirical methods. This work successfully demonstrates that, by applying the machine learning method, new compositions of AlCoCrFeMnNi-based HEAs can be obtained, exhibiting hardness values higher than the best literature value for the same alloy system. The correlations between the predicted composition, hardness, and microstructure are also discussed.
Breast cancer is a heterogeneous disease, and the survival rate of patients with breast cancer strongly depends on their stage and clinicopathological features. Chemoradiation therapy is commonly ...employed to improve the survivability of patients with advanced breast cancer. However, the treatment process is often accompanied by the development of drug resistance, which eventually leads to treatment failure. Metabolism reprogramming has been recognized as a mechanism of breast cancer resistance. In this study, we established a doxorubicin-resistant MCF-7 (MCF-7-D500) cell line through a series of long-term doxorubicin in vitro treatments. Our data revealed that MCF-7-D500 cells exhibited increased multiple-drug resistance, cancer stemness, and invasiveness compared with parental cells. We analyzed the metabolic profiles of MCF-7 and MCF-7-D500 cells through liquid chromatography−mass spectrometry. We observed significant changes in 25 metabolites, of which, 21 exhibited increased levels (>1.5-fold change and p < 0.05) and 4 exhibited decreased levels (<0.75-fold change and p < 0.05) in MCF-7 cells with doxorubicin resistance. These results suggest the involvement of metabolism reprogramming in the development of drug resistance in breast cancer, especially the activation of glycolysis, the tricarboxylic acid (TCA) cycle, and the hexamine biosynthesis pathway (HBP). Furthermore, most of the enzymes involved in glycolysis, the HBP, and the TCA cycle were upregulated in MCF-7-D500 cells and contributed to the poor prognosis of patients with breast cancer. Our findings provide new insights into the regulation of drug resistance in breast cancer, and these drug resistance-related metabolic pathways can serve as targets for the treatment of chemoresistance in breast cancer.
We examined fatigue-crack-growth behaviors of CoCrFeMnNi high-entropy alloys (HEAs) under as-fatigued and tensile-overloaded conditions using neutron-diffraction measurements coupled with diffraction ...peak-profile analyses. We applied both high-resolution transmission electron microscopy (HRTEM) and neutron-diffraction strain mapping for the complementary microstructure examinations. Immediately after a single tensile overload, the crack-growth-retardation period was obtained by enhancing the fatigue resistance, as compared to the as-fatigued condition. The combined mechanisms of the overload-induced larger plastic deformation, the enlarged compressive residual stresses and plastic-zone size, the crack-tip blunting ahead of the crack tip, and deformation twinning governed the pronounced macroscopic crack-growth-retardation behavior following the tensile overload. A remarkable fracture surface of highly-periodic serrated features along the crack-propagation direction was found in the crack-growth region immediately after the tensile overload. Moreover, a transition of plastic deformation from planar dislocation slip-dominated to twinning-dominated microstructures in the extended plastic zone was clearly observed at room temperature in the overloaded condition, in accordance with the simulated results by a finite element method (FEM). The above tensile overload-induced simultaneously combined effects in the coarse-grained CoCrFeMnNi shed light on the improvement of fatigue resistance for HEAs applications.
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In this study, conventional CMOS and complementary field-effect transistor (CFET) inverters based on a vertically stacked-nanosheet (NS) structure were fabricated. The NS below 8-nm channel layer ...thickness (<inline-formula> <tex-math notation="LaTeX">{T}_{{\text {Si}}} </tex-math></inline-formula>) was obtained by dry etching and wet etching processes. The channel thickness is controlled by dry etching, and the channel width was shrunk down by wet etching. Compared to single nanowire field-effect transistors (NSFETs), stacked NSFETs exhibit higher ON-current performance. For the CMOS inverter, the voltage transfer characteristics (VTCs) could be matched much better by adjusting the channel widths and layers for N-channel MOSFET (NFET) and P-channel MOSFET (PFET), respectively. For the CFET inverter, layout areas could be reduced and requires less number of lithographic and ion implantation steps contrary to the CMOS inverter. However, we observe that the VTCs of the CFET inverters still show asymmetric behavior due to the difficulties of adjustment in NS layers and systematic behavior of threshold voltages for NFETs/PFETs. This work experimentally demonstrates the CMOS and CFET inverters on the vertically stacked NS structure, which is promising for system-on-panel (SoP) and 3-D-ICs applications.
A synergistic approach for optimizing devices, circuits, and neural network architectures was used to abate junction-temperature-change-induced performance degradation of an Fe-FinFET-based ...artificial neural network. We demonstrated that the digital nature of the binarized neural network, with the "0" state programmed deep in the subthreshold and the "1" state in strong inversion, is crucial for robust deep neural network inference. The performance of a purely software-based binary neural network (BNN), with 96.1% accuracy for Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition, was used as a baseline. The Fe-FinFET-based BNN (including device-to-device variation at 300 K) achieved 95.7% inference accuracy on the MNIST dataset. Although substantial inference accuracy degradation with temperature change was observed in a nonbinary neural network, the BNN with optimized Fe-FinFETs as synaptic devices had excellent resistance to temperature change effects, and maintained a minimum inference accuracy of 95.2% within a temperature range of −40 to 125 °C after gate stack and bias optimization. However, reprogramming to adjust device conductance was necessary for temperatures higher than 125 °C.
According to quantum transport simulations, source-to-drain tunneling (SDT) has been recognized as the main cause leading to subthreshold swing (SS) saturation and degradation of short-channel ...MOSFETs at cryogenic temperatures. Generally, at a given low temperature, the steeper constant SS of thermionic currents may be overwhelmed by less-steep SDT currents at lower gate bias, degrading the average SS. Our simulations show that the SS of SDT currents is insensitive to temperatures for a MOSFET with a given channel length, accounting for SS saturation as lowering temperature. This work reveals the key points differentiating the possible reasons (interface traps, band tail and SDT) of SS saturation at cryogenic temperatures. We also study the impacts of carrier effective masses, gate-SD underlapping and a tunneling barrier at the source junction on SS saturation. SDT may pose a potential challenge and limit for scaling cryogenic CMOS of a quantum processor.
This study established a two-stage dynamic game strategy to analyze how the planned quota and price of masks were set and why mask manufacturing firms on the national mask team (NMT) in Taiwan evaded ...the plan. Plan evasion occurred when the NMT decided to produce less than the quota set by the government, even though they were incentivized and able to produce more. Taiwan's experience shows that through the collection of masks and the Name-Based Mask Rationing System, the people's right to procure masks can be guaranteed; however, to promote market transaction efficiency, the government should adopt a lower quota for the collection of masks and allow firms to freely sell them in the market after they complete their plans. The self-interest of the government played a key role in inducing plan evasion.
Crude oil price volatility impacts the global economy in general, as well as the economies of Europe and the United States in particular; it is supremely difficult to describe its tendency precisely, ...hence it leads to a forecasting methodology. This study aims to use the autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) approaches to cope with this problem in the United States and Europe. The data was gathered from the U.S. Energy Information Administration and federal research economic data (FRED) from January 2017 to September 2021. Simultaneously, values from January 2017 to March 2021, with 51 observations accounting for 90% of the total samples, were employed for the training phase, and the rest were used for the testing phase. The forecast result also indicated that the root mean square error (RMSE) and mean absolute percentage error (MAPE) values, applied by ARIMA models in Europe and the United States, have higher accurate indicators than SARIMA models. As a result, the ARIMA model achieved the best accuracy in both Europe and the USA, with MAPEEurope−ARIMA = 0.05, and MAPEUSA−ARIMA=0.05. Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies.
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