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Molybdenum dioxide (MoO2) is regarded as a potential anode for lithium-ion batteries due to its highly theoretical specific capacity. However, its further application in lithium-ion battery is largely limited by insufficient practical discharge capacity and cyclic performance. Here, MoO2 nanoparticles are in-situ grown on three-dimensional nitrogen doped carbon nanotubes (NCNTs) on nickel foam substrate homogeneously using a simple electro-deposition method. The unique structural features are favorable for lithium ions insertion and extraction and charge transfer dynamics at electrode/electrolyte interface. As a proof of concept, the as-synthesized nanocomposites have been employed as anode for lithium-ion battery, exhibiting a reversible and significantly improved discharge capacity of ∼517 mA h g−1 at the current density of 150 mA g−1 as well as superior cycle and rate performance. The first-principle calculations based on density functional theory and electrochemical impedance spectroscopy results demonstrate a reduced energy barrier of lithium ions diffusion, improved lithium storage behavior, reduced structure collapse, and significantly enhanced charge transfer kinetics in MoO2/NCNTs nanocomposites with respect to MoO2 powder. The excellent performance makes as-prepared MoO2/NCNTs nanocomposites promising binder-free anode for high performance lithium-ion batteries. This work also provides important theoretical insights for other state-of-the-art batteries design. [ABSTRACT FROM AUTHOR]

Isothermal constant-strain-rate compression experiments of S280 ultrahigh-strength stainless steel were conducted at 800–1150 °C, 0.001–10 s−1, and 70% height reduction. The flow stress behaviors were analyzed based on the compression data. The strain compensation Arrhenius constitutive relation, multiple linear regression constitutive relation, and back-propagation (BP) neural network constitutive relation of this alloy were established for the first time. The S280 ultrahigh-strength stainless steel is characterized by a positive strain rate and negative temperature sensitivity. Its flow stress at high temperature (1000–1150 °C) and low temperature (800–950 °C) is generally at the steady state and the softening state, respectively. The three new flow stress constitutive relations all meet the requirements for engineering applications in terms of predictive precision. The BP neural network constitutive relation shows the highest predictive precision, with correlation coefficient R of 0.999 and average absolute relative error AARE of 1.04%. The strain compensation Arrhenius constitutive relation shows the lowest predictive precision, with R of 0.994 and AARE of 14.748%. The multiple linear regression constitutive relation shows the modest predictive precision, with R of 0.994 and AARE of 6.24%. [ABSTRACT FROM AUTHOR]

Background: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear. Purpose: To distinguish primary site of BM and identify the best DL models. Study Type: Retrospective. Population: A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non‐small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included. Field Strength/Sequence: A 3‐T, T1 turbo spin echo (T1‐TSE), T2‐TSE, T2FLAIR‐TSE, DWI echo‐planar imaging (DWI‐EPI) and contrast‐enhanced T1‐TSE (CE T1‐TSE). Assessment: Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three‐dimensional residual network (3D‐ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2‐FLAIR + DWI, CE‐T1WI + DWI, CE‐T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non‐lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non‐lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient‐weighted class activation mapping (Grad‐CAM) was used to visualize the model by heatmaps. Statistical Tests: The area under the receiver operating characteristics curve (AUC) assess each classification performance. Results: 3D ResNet18 with Grad‐CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non‐lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE‐T1WI performed best for lung cancer vs. non‐lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE‐T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2‐FLAIR + DWI, CE‐T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non‐lung cancer, SCLC vs. NSCLC. Grad‐CAM helped for model visualization by heatmaps that focused on tumor regions. Data Conclusion: DL models may help to distinguish the origins of BM based on MRI data. Evidence Level: 3 Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]