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.

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Unidirectional magnetoresistance (UMR) in a ferromagnetic bilayer due to the spin Hall effects (SHEs) provides a facile means of probing in-plane magnetization to avoid complex magnetic tunnel junctions.However, the UMR signal is very weak and usually requires a lock-in amplifier for detection even in the bilayer involving Ta or Pt with a large spin Hall angle (SHA). Here we report a type of UMR, termed as the anomalous UMR (AUMR), in a single CoFeB layer without any adjacent SHE layers, where the UMR signal is about 10 times larger than that in Ta/CoFeB structures and can be detected by using conventional dc multimeters in the absence of lock-in amplifiers. We further demonstrate that the extracted AUMR by excluding thermal contributions shows reversal signs for the CoFeB and NiFe single layers with opposite SHAs, indicating that the AUMR may originate from the self-generated spin accumulation interacting with magnetization through the giant magnetoresistance-like mechanism. These results suggest that the AUMR contributes UMR signals larger than the interfacial spin Hall UMR in the CoFeB-involved systems, providing a convenient and reliable approach to detect in-plane magnetization for the two-terminal spintronic devices.

The discovery of ferromagnetism in two-dimensional (2D) monolayers has stimulated growing research interest in both spintronics and material science. However, these 2D ferromagnetic layers are mainly prepared through an incompatible approach for large-scale fabrication and integration, and moreover, a fundamental question of whether the observed ferromagnetism actually correlates with the 2D crystalline order has not been explored. Here, we choose a typical 2D ferromagnetic material, Fe3GeTe2, to address these two issues by investigating its ferromagnetism in an amorphous state. We have fabricated nanometer thick amorphous Fe3GeTe2 films approaching the monolayer thickness limit of crystallized Fe3GeTe2 (0.8 nm) through magnetron sputtering. Compared to crystallized Fe3GeTe2, we found that the basic ferromagnetic attributes, such as the Curie temperature which directly reflects magnetic exchange interactions and local anisotropic energy, do not change significantly in the amorphous states. This is attributed to the short-range atomic order, as confirmed by valence state analysis, being almost the same for both phases. The persistence of ferromagnetism in the ultrathin amorphous counterpart has also been confirmed through magnetoresistance measurements, where two unconventional switching dips arising from electrical transport within domain walls are clearly observed in the amorphous Fe3GeTe2 single layer. These results indicate that the long-range ferromagnetic order of crystallized Fe3GeTe2 may not correlate to the 2D crystalline order, and the corresponding ferromagnetic attributes can be utilized in an amorphous state which suits large-scale fabrication in a semiconductor technology-compatible manner for spintronics applications.

Fabrication of perpendicularly magnetized ferromagnetic films on various buffer layers, especially on numerous newly discovered spin–orbit torque (SOT) materials to construct energy-efficient spin-orbitronic devices, is a long-standing challenge. Even for the widely used CoFeB/MgO structures, perpendicular magnetic anisotropy (PMA) can only be established on limited buffer layers through post-annealing above 300 °C. Here, we report that the PMA of CoFeB/MgO films can be established reliably on various buffer layers in the absence of post-annealing. Further results show that precise control of MgO thickness, which determines oxygen diffusion in the underneath CoFeB layer, is the key to obtain the as-deposited PMA. Interestingly, contrary to the previous understanding, post-annealing does not significantly influence the well-established as-deposited PMA but indeed enhances unsaturated PMA with a thick MgO layer by modulating oxygen distributions, rather than crystallinity or Co– and Fe–O bonding. Moreover, our results indicate that oxygen diffusion also plays a critical role in PMA degradation at high temperatures. These results provide a practical approach to build spin-orbitronic devices based on various high-efficient SOT materials.

Objectives Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID‐19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID‐19 discrimination. Methods A three dimensional algorithm that combined multi‐instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID‐19 from community acquired pneumonia (CAP) while logistic regression (LR), k‐nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID‐19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID‐19 and 183 CAP cases) and relatively small datasets (17 COVID‐19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID‐19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G‐mean were utilized for performance evaluation. Results In the external test cohort, the relatively large data‐based 3DMTM‐LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM‐SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM‐SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN‐MMSD, LR‐MMSD, SVM‐MMSD, and 3DCM‐MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID‐19 discrimination. 3DMTM, trained with either CT or multi‐modal data, presented comparably excellent performance in COVID‐19 discrimination. Conclusions The 3DMTM algorithm presented excellent robustness for COVID‐19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID‐19 discrimination with that trained with multi‐modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID‐19 discrimination, especially in the scenario with limited data for training.