Metal–organic frameworks (MOFs) have been widely recognized as one of the most fascinating classes of materials from science and engineering perspectives, benefiting from their high porosity and ...well-defined and tailored structures and components at the atomic level. Although their intrinsic micropores endow size-selective capability and high surface area, etc., the narrow pores limit their applications toward diffusion-control and large-size species involved processes. In recent years, the construction of hierarchically porous MOFs (HP-MOFs), MOF-based hierarchically porous composites, and MOF-based hierarchically porous derivatives has captured widespread interest to extend the applications of conventional MOF-based materials. In this Review, the recent advances in the design, synthesis, and functional applications of MOF-based hierarchically porous materials are summarized. Their structural characters toward various applications, including catalysis, gas storage and separation, air filtration, sewage treatment, sensing and energy storage, have been demonstrated with typical reports. The comparison of HP-MOFs with traditional porous materials (e.g., zeolite, porous silica, carbons, metal oxides, and polymers), subsisting challenges, as well as future directions in this research field, are also indicated.
The exploration here intends to compensate for the traditional human motion recognition (HMR) systems' poor performance on large-scale datasets and micromotions. To this end, improvement is designed ...for the HMR in sports competition based on the deep learning (DL) algorithm. First, the background and research status of HMR are introduced. Then, a new HMR algorithm is proposed based on kernel extreme learning machine (KELM) multidimensional feature fusion (MFF). Afterward, a simulation experiment is designed to evaluate the performance of the proposed KELM-MFF-based HMR algorithm. The results showed that the recognition rate of the proposed KELM-MFF-based HMR is higher than other algorithms. The recognition rate at 10 video frame sampling points is ranked from high to low: the proposed KELM-MFF-based HMR, support vector machine (SVM)-MFF-based HMR, convolutional neural network (CNN) + optical flow (CNN-T)-based HMR, improved dense trajectory (IDT)-based HMR, converse3D (C3D)-based HMR, and CNN-based HMR. Meanwhile, the feature recognition rate of the proposed KELM-MFF-based HMR for the color dimension is higher than the time dimension, by up to 24%. Besides, the proposed KELM-MFF-based HMR algorithm's recognition rate is 92.4% under early feature fusion and 92.1% under late feature fusion, higher than 91.8 and 90.5% of the SVM-MFF-based HMR. Finally, the proposed KELM-MFF-based HMR algorithm takes 30 and 15 s for training and testing. Therefore, the algorithm designed here can be used to deal with large-scale datasets and capture and recognize micromotions. The research content provides a reference for applying extreme learning machine algorithms in sports competitions.
A highly stereocontrolled syn‐addition of silicon nucleophiles across cyclopropenes with two different geminal substituents at C3 is reported. Diastereomeric ratios are excellent throughout ...(d.r.≥98:2) and enantiomeric excesses usually higher than 90 %, even reaching 99 %. This copper‐catalyzed C−Si bond formation closes the gap of the direct synthesis of α‐chiral cyclopropylsilanes.
Silicon meets strain: A robust copper‐catalyzed C−Si bond formation enables the direct synthesis of α‐chiral cyclopropylsilanes from cyclopropenes. The syn‐addition across the strained alkene occurs highly diastereo‐ and enantioselectively with discrimination of the geminal substituents at C3 (see scheme; segphos=5,5′‐bis(diphenylphosphanyl)‐4,4′‐bi‐1,3‐benzodioxol).
The development of noble‐metal‐free heterogeneous catalysts is promising for selective oxidation of aromatic alcohols; however, the relatively low conversion of non‐noble metal catalysts under ...solvent‐free atmospheric conditions hinders their industrial application. Now, a holey lamellar high entropy oxide (HEO) Co0.2Ni0.2Cu0.2Mg0.2Zn0.2O material with mesoporous structure is prepared by an anchoring and merging process. The HEO has ultra‐high catalytic activity for the solvent‐free aerobic oxidation of benzyl alcohol. Up to 98 % conversion can be achieved in only 2 h, to our knowledge, the highest conversion of benzyl alcohol by oxidation to date. By regulating the catalytic reaction parameters, benzoic acid or benzaldehyde can be selectively optimized as the main product. Analytical characterizations and calculations provide a deeper insight into the catalysis mechanism, revealing abundant oxygen vacancies and holey lamellar framework contribute to the ultra‐high catalytic activity.
A high‐entropy oxide material with mesoporous structure is prepared by an anchoring and merging process. It exhibits ultra‐high catalytic activity for the oxidation of benzyl alcohol. Benzoic acid or benzaldehyde can be selectively optimized as the main product by rationally regulating the catalysis parameters.
Metal–organic frameworks (MOFs) are an emerging class of porous materials with potential applications in gas storage, separations, catalysis, and chemical sensing. Despite numerous advantages, ...applications of many MOFs are ultimately limited by their stability under harsh conditions. Herein, the recent advances in the field of stable MOFs, covering the fundamental mechanisms of MOF stability, design, and synthesis of stable MOF architectures, and their latest applications are reviewed. First, key factors that affect MOF stability under certain chemical environments are introduced to guide the design of robust structures. This is followed by a short review of synthetic strategies of stable MOFs including modulated synthesis and postsynthetic modifications. Based on the fundamentals of MOF stability, stable MOFs are classified into two categories: high‐valency metal–carboxylate frameworks and low‐valency metal–azolate frameworks. Along this line, some representative stable MOFs are introduced, their structures are described, and their properties are briefly discussed. The expanded applications of stable MOFs in Lewis/Brønsted acid catalysis, redox catalysis, photocatalysis, electrocatalysis, gas storage, and sensing are highlighted. Overall, this review is expected to guide the design of stable MOFs by providing insights into existing structures, which could lead to the discovery and development of more advanced functional materials.
Stable metal–organic frameworks (MOFs) with high resistance to harsh chemical environments are reviewed with regard to recent progress in their research and development. Fundamental mechanisms of MOF stability, the design and synthesis of stable MOF architectures, and their latest applications are summarized, providing a fundamental outline for the discovery of new stable MOFs.
Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate ...multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.
A silylation of vinyliodonium salts using zinc-based silicon reagents as nucleophiles is reported. This cross-coupling is catalyzed by copper, and vinylsilanes are obtained in high yield likely ...following a Cu(I)/Cu(III) reaction mechanism. The procedure is operationally simple, neither air- nor moisture-sensitive, and tolerant of a range of functional groups. The new method is an addition to the still limited number of transition-metal-catalyzed vinyl C(sp2)–Si cross-coupling reactions.
Rapid developments in domestic metro construction could inevitably result in cross-engineering conflicts between metro lines and existing bridge pile foundations. Pile underpinning technology has ...been successfully applied to such projects to ensure the operating safety of viaducts and synchronous construction of metro lines. One section tunnel of Jinan Metro Line R2 is selected as the research object in this study. First, the processes of pile underpinning and tunnel crossing construction are numerically simulated using finite element software. Next, the deformation law of underpinning structures is studied and the engineering risk points are determined. Finally, the design scheme of pile underpinning is proven to be correct and reasonable. The results are as follows: (1) The underpinning structure presents a trend of overall settlement. The horizontal displacement of the pile body occurs in the form of a bow. Maximum deformation occurs at the underpinning beam and top of the pile. (2) The influence area of the soil surface is approximately 4 m outside the underpinning beam, and the deformation values of the underpinning system meet construction requirements. (3) The following construction steps are recommended. Step 1: Pre-jacking constructing. Step 2: Pile cutting. Step 3: Jack removal and underpinning beam and pile cap construction. Step 4: Road surface backfilling and repair. The shield machine should then be allowed to pass. (4) This paper provides targeted risk management measures. For example, isolation piles should be set on both sides of the tunnel.
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