Chelating Metal Ions in a Metal-Organic Framework for Constructing a Biomimetic Catalyst Through Post-modification

Crystal engineering, as a burgeoning technology, has been widely used to construct metalloporphyrins biomimetic catalysts. Herein, a bimetallic metal-organic framework (MOF) was constructed by 4-(4-carboxyphenyl)-1,2,4-triazole ligand, Co²⁺ and Zr⁴⁺ metal ions by solvothermal reaction(named PFC-88). A N,N-chelation site was found between the two adjacent ligands in PFC-88, consequently a porphyrin-like structure was obtained through chelating Fe³⁺ in this site by post-modification, named PFC-88-Fe. The result of a single crystal X-ray technology verified that Fe ions were successfully metalated in the N,N-chelation site of PFC-88, which is assisted by the X-ray absorption near-edge structure(XANES) spectra. An o-phenylenediamine oxidation reaction was applied to assessing the catalytic activity of PFC-88-Fe, in which the absorbance increases of phenazine-2,3-diamine at λ=418 nm were recorded by absorption spectroscopy in kinetic mode, exhibiting the application potential as a biomimetic catalyst.     

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

     

A general framework for describing photofission observables of actinides at an average excitation energy below 30 MeV

A reasonable prediction of photofission observables plays a paramount role in understanding the photofission process and guiding various photofission-induced applications, such as short-lived isotope production, nuclear waste disposal, and nuclear safeguards. However, the available experimental data for photofission observables are limited, and the existing models and programs have mainly been developed for neutron-induced fission processes. In this study, a general framework is proposed for characterizing the photofission observables of actinides, including the mass yield distributions (MYD) and isobaric charge distributions (ICD) of fission fragments and the multiplicity and energy distributions of prompt neutrons (n_p) and prompt γ rays (γ_p). The framework encompasses various systematic neutron models and empirical models considering the Bohr hypothesis and does not rely on the experimental data as input. These models are then validated individually against experimental data at an average excitation energy below 30 MeV, which shows the reliability and robustness of the general framework. Finally, we employ this framework to predict the characteristics of photofission fragments and the emissions of prompt particles for typical actinides including ²³²Th, ^{235, 238}U and ²⁴⁰Pu. It is found that the ²³⁸U(γ, f) reaction is more suitable for producing neutron-rich nuclei compared to the ²³²Th(γ, f) reaction. In addition, the average multiplicity number of both n_p and γ_p increases with the average excitation energy.     

Quantum algorithm for neighborhood preserving embedding

Neighborhood preserving embedding (NPE) is an important linear dimensionality reduction technique that aims at preserving the local manifold structure. NPE contains three steps, i.e., finding the nearest neighbors of each data point, constructing the weight matrix, and obtaining the transformation matrix. Liang et al. proposed a variational quantum algorithm (VQA) for NPE [Phys. Rev. A 101 032323 (2020)]. The algorithm consists of three quantum sub-algorithms, corresponding to the three steps of NPE, and was expected to have an exponential speedup on the dimensionality n. However, the algorithm has two disadvantages: (i) It is not known how to efficiently obtain the input of the third sub-algorithm from the output of the second one. (ii) Its complexity cannot be rigorously analyzed because the third sub-algorithm in it is a VQA. In this paper, we propose a complete quantum algorithm for NPE, in which we redesign the three sub-algorithms and give a rigorous complexity analysis. It is shown that our algorithm can achieve a polynomial speedup on the number of data points m and an exponential speedup on the dimensionality n under certain conditions over the classical NPE algorithm, and achieve a significant speedup compared to Liang et al.'s algorithm even without considering the complexity of the VQA.     

Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization

Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type of filter. In this work, we propose a co-training model for visual object recognition using deep neural networks by adding layers of self-supervised neural networks as intermediate inputs to the views, where the views are diversified through the cross-entropy regularization of their outputs. Since the model merges the concepts of co-training and self-supervised learning by considering the differentiation of outputs, we called it Differential Self-Supervised Co-Training (DSSCo-Training). This paper presents some experiments using the DSSCo-Training model to well-known image datasets such as MNIST, CIFAR-100, and SVHN. The results indicate that the proposed model is competitive with the state-of-art models and shows an average relative improvement of 5% in accuracy for several datasets, despite its greater simplicity with respect to more recent approaches.     

紫色球杆菌视紫红质光谱特性的机器学习研究

近年来，机器学习等人工智能技术被应用于蛋白质工程，其在蛋白质结构、功能预测、催化活性等研究中具有独特优势。在未知蛋白质结构的情况下，将蛋白质序列和功能特性与机器学习相结合，基于序列-活性关系（innovative sequence-activity relationship，ISAR）算法，将蛋白质氨基酸序列数字化，用快速傅里叶变换（fast four transform，FFT）进行预处理，再进行偏最小二乘回归建模，可在数据集较少情况下拟合得到最佳模型。通过机器学习对紫色球杆菌视紫红质（gloeobacter violaceus rhodopsin，GR）的突变体蛋白质氨基酸序列与光谱最大吸收波长进行建模，获得了最佳模型。用最佳索引LEVM760106建模得到的确定系数R² 为0.944，均方误差E为11.64。用小波变换进行的预处理，其R² 虽也约为0.944，但E大于11.64，不及FFT进行的预处理。方法较好地解决了蛋白质序列与功能特性之间的数学建模问题，在蛋白质工程中可为预测更优的突变体提供支持。     

Characterization and gas adsorption of a novel three‐dimensional metal–organic framework (MOF) generated from a new polydentate fluorene‐bridged ligand

A polydentate ligand bridged by a fluorene group, namely 9,9‐bis(2‐hydroxyethyl)‐2,7‐bis(pyridin‐4‐yl)fluorene (L), has been prepared under solvothermal conditions in acetonitrile. Crystals of the three‐dimensional metal–organic framework (MOF) poly[[[μ₃‐9,9‐bis(2‐hydroxyethyl)‐2,7‐bis(pyridin‐4‐yl)fluorene‐κ³N:N′:O]bis(methanol‐κO)(μ‐sulfato‐κ²O:O′)nickel(II)] methanol disolvate], {[Ni(SO₄)(C₂₇H₂₄N₂O₂)(CH₃OH)]·2CH₃OH}_n, (I), were obtained by the solvothermal reaction of L and NiSO₄ in methanol. The ligand L forms a two‐dimensional network in the crystallographic bc plane via two groups of O—H…N hydrogen bonds and neighbouring two‐dimensional planes are completely parallel and stack to form a three‐dimensional structure. In (I), the Ni^II ions are linked by sulfate ions through Ni—O bonds to form inorganic chains and these Ni‐containing chains are linked into a three‐dimensional framework via Ni—O and Ni—N bonds involving the polydentate ligand L. With one of the hydroxy groups of L coordinating to the Ni^II atom, the torsion angle of the hydroxyethyl group changes from that of the uncoordinated molecule. In addition, the adsorption properties of (I) with carbon dioxide were investigated.     

Optimal time delays in a class of reaction-diffusion equations

ABSTRACT

A class of semilinear parabolic reaction diffusion equations with multiple time delays is considered. These time delays and corresponding weights are to be optimized such that the associated solution of the delay equation is the best approximation of a desired state function. The differentiability of the mapping is proved that associates the solution of the delay equation to the vector of weights and delays. Based on an adjoint calculus, first-order necessary optimality conditions are derived. Numerical test examples show the applicability of the concept of optimizing time delays.     

Switching from linear to cyclic δ‐Polyvalerolactone synthesized via zeolitic imidazolate framework as a catalyst: A promising approach

A series of spray dried zeolitic imidazolate frameworks (ZIFs = ZIF‐8, ZIF‐67, and Zn/Co‐ZIF) are used as a catalyst for the bulk ring‐opening polymerization of δ‐valerolactone without any co‐catalyst to generate polyvalerolactone. Interestingly, using the same catalyst under the same reaction conditions could manipulate the structure of the product polymer, and thus its physical properties. Thus, using a dried substrate leads to the formation of the cyclic polymer while a linear polymer was formed on using the commercially available substrate. An activated monomer mechanism has been suggested where the propagating zinc alkoxide undergoes an intramolecular transesterification to release cyclic or linear polyvalerolactone. The ROP of δ‐VL without drying shows that the polymeric zwitterions have little tendency to cyclize in the presence of moisture. At 140 °C, ZIF‐8 shows a superior catalytic activity resulting in the production of cyclic polyvalerolactone having a high molecular weight as compared to ZIF‐67 or Zn/Co‐ZIF due to the presence of highly active sites. The catalyst could be recycled and reused without any significant loss of catalytic activity.     

A stable pillared metal–organic framework constructed by H4TCPP ligand as luminescent sensor for selective detection of TNP and Fe3+ ions

A novel luminescent metal–organic framework ( Zn‐TCPP/BPY ) with pillared structure based on 2,3,5,6‐tetrakis(4‐carboxyphenyl)pyrazine (H₄TCPP) and 4,4′‐bipyridine (BPY) has been designed and synthesized through a solvothermal reaction. The [Zn₂(COO)₄] paddlewheel units are linked by TCPP^4? ligands to form two‐dimensional layers and further connected by BPY ligands as pillars to construct the twofold interpenetrating three‐dimensional framework. Interestingly, Zn‐TCPP/BPY possesses outstanding stability in organic solvents and water as well as maintains its structural rigidity in aqueous solutions of different pH values (3–12). After activation, Zn‐TCPP/BPY possesses permanent porosity with Brunauer–Emmett–Teller surface area of 630 m² g^–1. Remarkably, Zn‐TCPP/BPY displays excellent fluorescent property in virtue of the aggregation‐induced emission effect of the H₄TCPP ligand, which can be highly active and quenched by small amounts of 2,4,6‐trinitrophenol (TNP) and Fe³⁺ ions. Furthermore, the detection effect of Zn‐TCPP/BPY remains basically the same even after five cycles. The excellent stability, high sensitivity, and recyclability of Zn‐TCPP/BPY make it an outstanding chemical sensor for detecting TNP and Fe³⁺ ions.