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Molecular weaving is the interlacing of covalently linked threads to make extended structures. Although weaving based on 3D networks has been reported, the 2D forms remain largely unexplored. Reticular chemistry uses mutually embracing tetrahedral metal complexes as crossing points, which, when linked, typically lead to 3D woven structures. Realizing 2D weaving patterns requires crossing points with an overall planar geometry. We show that polynuclear helicates composed of multiple metal-complex units, and therefore multiple turns, are well suited in this regard. By reticulating helicate units, we successfully obtained 2D weaving structures based on the familiar chicken-wire pattern.

Abstract Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm−3 at an electric field of 5104 kV cm−1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time, leading to significant deep uncertainty. Due to deep uncertainty, decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future, thus making it difficult to specify appropriate models and parameters to describe the system of interest. In this paper, we propose a dynamic exploratory hybrid modeling framework that fits data, models, and computational experiments together to simulate complex systems with deep uncertainty. In the framework, one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experiments to explore the diversity of future scenarios. Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system. This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated. Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions. The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

The type, surface texture, metamorphic degree, and developmental stage of coal and gangue significantly influence their emissivity. Accurate settings for emissivity parameters are essential for infrared temperature measurements and the identification of coal and gangue in infrared images. This study proposed a method for measuring the emissivity of coal and gangue based on the matching method. The approach integrated surface thermocouples with infrared thermography to assess emissivity. Samples were uniformly heated in a closed electric furnace, and once the temperature stabilized, a surface thermocouple measured the actual temperature of a selected area (denoted as t1). Concurrently, the infrared thermography system measured the temperature of the same area (denoted as t2). The emissivity of the infrared thermography system was calibrated until t2 equaled t1. At this point, the calculated emissivity reflected the true emissivity of the coal and gangue at that temperature. The experimental results indicated that: ① Under isothermal conditions, greater surface roughness of coal and gangue correlated with higher emissivity values, suggesting that surface roughness is a fundamental factor restricting the emissivity of these materials. ② The emissivity of four different types of coal and gangue decreased with increasing temperature, following a power function, with the fitting function's correlation coefficient (R2) exceeding 0.98, thereby confirming the feasibility of the matching method for measuring emissivity. ③ The inverse method revealed that the error rates between the measured and theoretical values under varying temperature conditions were all below 3%, validating the accuracy of the measured emissivity of coal and gangue.

Objective To observe the level of exercise tolerance among maintenance hemodialysis (MHD) patients using the cardiopulmonary exercise test (CPET) and analysis the factors influencing it. Methods From December 2019 to April 2021, 121 maintenance hemodialysis (MHD) patients were recruited for cardiopulmonary exercise testing. General profiles, medical history, laboratory indicators and parameters of CPET were recorded to establish a data set. Take peak respiratory uptake as the dependent variable and other indicators of CPET as independent variables for multiple liner regressions analysis. Results A total of 121 patients were recruited. There were 85 males and 36 females with an age range of (28-78) years. Peak respiratory uptake was (12.17±2.56)mL·kg−1·min−1 . Multiple linear regression model as constructed by “stepwise method” indicated that the fitted multiple liner regression equation was statistically significant (F=44.345, P

Two entangled 2D square covalent organic frameworks (COFs) have been synthesized from 4,4',4″,4‴-(9,9'-spirobi[fluorene]-2,2',7,7'-tetrayl)-tetrabenzaldehhyde (SFTB) and p-phenylenediamine (PPA) and benzidine (BZD) to form COF-38, [(SFTB)(PPA)2]imine, and its isoreticular form COF-39, [(SFTB)(BZD)2]imine. We also report the single-crystal electron diffraction structure of COF-39 and find that it is composed of mutually entangled 2D square nets (sql). These COFs represent the first examples of entangled 2D COF structures, which, as we illustrate, were made possible by our strategy of using the distorted tetrahedral SFTB building unit. SFTB overcomes the propensity of 2D COFs to stack through π-π stacking and allows entanglements to form. This work significantly adds to the design principles of COFs.