Soft-Hard Janus Nanoparticles Triggered Hierarchical Conductors with Large Stretchability, High Sensitivity, and Superior Mechanical Properties.

There is a long-standing conflict between the large stretchability and high sensitivity for strain sensors, a strategy of decoupling the mechanical/electrical module by constructing the hierarchical conductor has been developed in this study. The hierarchical conductor, consisting of a mechanically stretchable layer, a conductive network layer, and a strongly bonded interface, can be produced in a simple one-step process with the aid of soft-hard Janus nanoparticles (JNPs). The introduction of JNPs in the stretchable layer can evenly distribute stress and dissipate energy due to forming the rigid-flexible homogeneous networks. Specifically, JNPs can drive graphene nanosheets (GNS) to fold or curl, creating the unique JNPs-GNS building block that can further construct the conductive network. Due to its excellent deformability to hinder crack propagation, the flexible conductive network could be stretched continuously and the local conductive pathways could be reconstructed. Consequently, the hierarchical conductor could detect both subtle strain of 0-2% and large strain of up to 370%, with a gauge factor (GF) from 66.37 to 971.70, demonstrating outstanding stretchability and sensitivity. And it also owns large tensile strength (5.28 MPa) and high deformation stability. This hierarchical design will give graphene-based sensors a major boost in emerging applications.

Quantitative analysis of chest MRI images for benign malignant diagnosis of pulmonary solid nodules.

Background: In this study, we developed and validated machine learning (ML) models by combining radiomic features extracted from magnetic resonance imaging (MRI) with clinicopathological factors to assess pulmonary nodule classification for benign malignant diagnosis. Methods: A total of 333 consecutive patients with pulmonary nodules (233 in the training cohort and 100 in the validation cohort) were enrolled. A total of 2,824 radiomic features were extracted from the MRI images (CE T1w and T2w). Logistic regression (LR), Naïve Bayes (NB), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) classifiers were used to build the predictive models, and a radiomics score (Rad-score) was obtained for each patient after applying the best prediction model. Clinical factors and Rad-scores were used jointly to build a nomogram model based on multivariate logistic regression analysis, and the diagnostic performance of the five prediction models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: A total of 161 women (48.35%) and 172 men (51.65%) with pulmonary nodules were enrolled. Six important features were selected from the 2,145 radiomic features extracted from CE T1w and T2w images. The XGBoost classifier model achieved the highest discrimination performance with AUCs of 0.901, 0.906, and 0.851 in the training, validation, and test cohorts, respectively. The nomogram model improved the performance with AUC values of 0.918, 0.912, and 0.877 in the training, validation, and test cohorts, respectively. Conclusion: MRI radiomic ML models demonstrated good nodule classification performance with XGBoost, which was superior to that of the other four models. The nomogram model achieved higher performance with the addition of clinical information.