High-Performance Monolithic 3D Integrated Complementary Inverters Based on Monolayer n-MoS2 and p-WSe2.

Herein, the structure of integrated M3D inverters are successfully demonstrated where a chemical vapor deposition (CVD) synthesized monolayer WSe2 p-type nanosheet FET is vertically integrated on top of CVD synthesized monolayer MoS2 n-type film FET arrays (2.5 × 2.5 cm) by semiconductor industry techniques, such as transfer, e-beam evaporation (EBV), and plasma etching processes. A low temperature (below 250 °C) is employed to protect the WSe2 and MoS2 channel materials from thermal decomposition during the whole fabrication process. The MoS2 NMOS and WSe2 PMOS device fabricated show an on/off current ratio exceeding 106 and the integrated M3D inverters indicate an average voltage gain of ≈9 at VDD = 2 V. In addition, the integrated M3D inverter demonstrates an ultra-low power consumption of 0.112 nW at a VDD of 1 V. Statistical analysis of the fabricated inverters devices shows their high reliability, rendering them suitable for large-area applications. The successful demonstration of M3D inverters based on large-scale 2D monolayer TMDs indicate their high potential for advancing the application of 2D TMDs in future integrated circuits.

Invasiveness assessment by artificial intelligence against intraoperative frozen section for pulmonary nodules ≤ 3 cm.

PURPOSE: To investigate the performance of an artificial intelligence (AI) algorithm for assessing the malignancy and invasiveness of pulmonary nodules in a multicenter cohort. METHODS: A previously developed deep learning system based on a 3D convolutional neural network was used to predict tumor malignancy and invasiveness. Dataset of pulmonary nodules no more than 3 cm was integrated with CT images and pathologic information. Receiver operating characteristic curve analysis was used to evaluate the performance of the system. RESULTS: A total of 466 resected pulmonary nodules were included in this study. The areas under the curves (AUCs) of the deep learning system in the prediction of malignancy as compared with pathological reports were 0.80, 0.80, and 0.75 for all, subcentimeter, and solid nodules, respectively. Additionally, the AUC in the AI-assisted prediction of invasive adenocarcinoma (IA) among subsolid lesions (n = 184) was 0.88. Most malignancies that were misdiagnosed by the AI system as benign diseases with a diameter measuring greater than 1 cm (26/250, 10.4%) presented as solid nodules (19/26, 73.1%) on CT. In an exploratory analysis involving nodules underwent intraoperative pathologic examination, the concordance rate in identifying IA between the AI model and frozen section examination was 0.69, with a sensitivity of 0.50 and specificity of 0.97. CONCLUSION: The deep learning system can discriminate malignant diseases for pulmonary nodules measuring no more than 3 cm. The AI model has a high positive predictive value for invasive adenocarcinoma with respect to intraoperative frozen section examination, which might help determine the individualized surgical strategy.

Operando photoelectron spectroscopy analysis of graphene field-effect transistors.

In this study, operando photoelectron spectroscopy was used to characterize the performance of graphene field-effect transistors under working conditions. By sweeping the back-gate voltages, the carrier concentration of the graphene channel on the 150 nm Si3N4/Si substrate was tuned. From the C1s core level spectra acquired under the application of different gate voltages, the binding energy shifts caused by electric-field effects were obtained and analyzed. Together with the C1s peak shape information and the photoluminescence spectrum of the Si3N4/Si substrate, the presence of local potential across the x-ray beam spot associated with defects and gate leakage current in amorphous Si3N4was identified. The presence of defects in Si3N4/Si substrate could not only screen the partial electric field generated by the back gate but also serve as long-range scattering centers to the carriers, thus affecting charge transport in the graphene channel. Our findings will help further investigate the dielectric/graphene interface properties and accelerate the utilization of graphene in real device applications.

Meta analysis of XRCC3 Thr241Met polymorphism and lung cancer susceptibility of populations in East Asia.

OBJECTIVE: To assess the relation between XRCC3 Thr241Met polymorphism and lung cancer susceptibility of populations in East Asia. METHODS: Related studies of XRCC3 Thr241Met polymorphism and lung cancer susceptibility of populations in East Asia were collected through searching the Pubmed, Embase Library, SPRINGER, CNKI and CSSCI. RESULTS: According to the entry criteria, there were 8 case-control studies in the assessing system and there were 6 321 study cases, including 3 215 patients with lung cancer and 3 106 cases without cancers. Meta analysis results showed the combined OR value of the ratio of genotype Thr/Met+Met/Met and Thr/Thr was 1.03 (95%CI: 0.89-1.20) (P>0.05). CONCLUSIONS: XRCC3 Thr241Met polymorphism may not related to lung cancer susceptibility of populations in East Asia. Allele 241Met did not increase the risk of lung cancer.