The fabrication of nanofiller-reinforced intrinsic healable polymer composite films with both excellent mechanical robustness and highly efficient healability is challenging because the mobility of the polymer chains is suppressed by the incorporated nanofillers. In this study, we exploit the reversible host-guest interactions between nanofillers and the matrix polymer films and report the fabrication of intrinsically healable, reduced graphene oxide (RGO)-reinforced polymer composite films capable of conveniently and repeatedly healing cuts of several tens of micrometers wide. The healable films can be prepared via layer-by-layer assembly of poly(acrylic acid) (PAA) with complexes of branched poly(ethylenimine) grafted with ferrocene (bPEI-Fc) and RGO nanosheets modified with β-cyclodextrin (RGO-CD) (denoted as bPEI-Fc&RGO-CD). The as-prepared PAA/bPEI-Fc&RGO-CD films are mechanically robust with a Young's modulus of 17.2 ± 1.9 GPa and a hardness of 1.00 ± 0.30 GPa. The healing process involves two steps: (i) healing of cuts in an oxidation condition in which the host-guest interactions between bPEI-Fc and RGO-CD nanosheets are broken and the cuts on the films are healed; and (ii) reconstruction of host-guest interactions between bPEI-Fc and RGO-CD nanosheets via reduction to restore the original mechanical robustness of the films.
Robust, transparent, and birefringent inorganic films are demanded for polarization control of high‐power lasers. While single crystals or films obtained via glancing angle deposition exhibit desirable optical properties and laser damage resistance, these methods are limited by cost and scalability. Mesomorphic ceramics as inorganic solids with liquid crystalline superstructure offer appealing transparency and birefringence but lack mechanical robustness due to their high porosity. Here, the effect of sintering on optical and mechanical properties of mesomorphic ceramics is evaluated. Films prepared by blade coating are sintered under varying conditions. Constrained sintering accomplished crystallite growth, densification, and morphological changes including necking as well as cracking while preserving the crystallographic orientation. The extent of sintering as a function of thermal treatment is quantified by morphology, surface area loss, and crystallite growth. Moreover, activation energies for surface diffusion and grain growth are estimated by surface area analysis and X‐ray diffraction peak narrowing, respectively. After sintering, birefringence decreases while Young's modulus and hardness improve as the film densifies. Upon partial sintering, mesomorphic ceramics retain transparency, high birefringence, and enhanced modulus. Laser‐induced damage threshold is measured as well. The reported results represent an important step toward the assembly and sintering of robust waveplates with high laser damage resistance.
Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou’s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers.
Mesomorphic ceramics are broadly defined as solid systems with morphologies intermediate between isotropic materials and single crystals. To illustrate this materials concept, a class of mesomorphic ceramics with nematic-like superstructures has been synthesized via lyotropic liquid crystals of titanium dioxide, TiO2, in an isotropic solvent for a demonstration of uniaxially oriented solid films. A lyotropic dispersion of ligand-capped anatase nanorods at 60 wt % in chlorobenzene was calcined and sintered with prior manual shear to form an optically anisotropic, 2.3 ± 0.3 μm-thick solid film. In the course of sintering, nanorods fuse into low-aspect ratio grains that form nematic domains. Shear-induced alignment of nanorods followed by thermal treatment creates uniaxial orientation across millimeters, which exhibits high optical transparency and a nearly constant birefringence of 0.018 ± 0.002 from 650 to 1700 nm. Distinct from liquid-crystal templating, this new approach yields superstructures of nanoparticles with relative ease and at lower costs. The present study opens a pathway toward robust, ceramic-based solid films for diverse application.
ObjectiveIn order to provide reference for clinicians and bring convenience to clinical work, we seeked to develop and validate a risk prediction model for lymph node metastasis (LNM) of Ewing’s sarcoma (ES) based on machine learning (ML) algorithms.MethodsClinicopathological data of 923 ES patients from the Surveillance, Epidemiology, and End Results (SEER) database and 51 ES patients from multi-center external validation set were retrospectively collected. We applied ML algorithms to establish a risk prediction model. Model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis in external validation set. After determining the best model, a web-based calculator was made to promote the clinical application.ResultsLNM was confirmed or unable to evaluate in 13.86% (135 out of 974) ES patients. In multivariate logistic regression, race, T stage, M stage and lung metastases were independent predictors for LNM in ES. Six prediction models were established using random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR). In 10-fold cross-validation, the average area under curve (AUC) ranked from 0.705 to 0.764. In ROC curve analysis, AUC ranged from 0.612 to 0.727. The performance of the RF model ranked best. Accordingly, a web-based calculator was developed (https://share.streamlit.io/liuwencai2/es_lnm/main/es_lnm.py).ConclusionWith the help of clinicopathological data, clinicians can better identify LNM in ES patients. Risk prediction models established in this study performed well, especially the RF model.
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