Recently, semiconducting nanofiber networks (NFNs) have been considered as one of the most promising platforms for large-area and low-cost electronics applications. However, the high contact resistance among stacking nanofibers remained to be a major challenge, leading to poor device performance and parasitic energy consumption. In this report, a controllable welding technique for NFNs was successfully demonstrated via a bioinspired capillary-driven process. The interfiber connections were well-achieved via a cooperative concept, combining localized capillary condensation and curvature-induced surface diffusion. With the improvements of the interfiber connections, the welded NFNs exhibited enhanced mechanical property and high electrical performance. The field-effect transistors (FETs) based on the welded Hf-doped InO (InHfO) NFNs were demonstrated for the first time. Meanwhile, the mechanisms involved in the grain-boundary modulation for polycrystalline metal-oxide nanofibers were discussed. When the high-k ZrO dielectric thin films were integrated into the FETs, the field-effect mobility and operating voltage were further improved to be 25 cm V s and 3 V, respectively. This is one of the best device performances among the reported nanofibers-based FETs. These results demonstrated the potencies of the capillary-driven welding process and grain-boundary modulation mechanism for metal-oxide NFNs, which could be applicable for high-performance, large-scale, and low-power functional electronics.

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ObjectiveTo assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy.MethodsQuantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2nd-3rd immunotherapy cycles (time point 1, TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment. In addition, a prediction model integrating the clinicopathologic risk characteristics and phenotypic signature was developed for the prediction of PFS.ResultsThe C-index of TP0, TP1 and delta radiomics models in the training and validation cohort were 0.64, 0.75, 0.80, and 0.61, 0.68, 0.78, respectively. The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (P<0.05), with a C-index of 0.83 and 0.70, respectively. Additionally, the delta radiomics model (C-index of 0.86) had a higher predictive accuracy compared to PD-L1 expression (C-index of 0.50) (P<0.0001).ConclusionsThe combined prediction model including clinicopathologic characteristics (tumour anatomical classification and brain metastasis) and the delta radiomics signature could achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.