Search

Double helical conformation of polymer chains is widely observed in biomacromolecules and plays an essential role in exerting their biological functions, such as molecular recognition and information storage. It has remained challenging, however, to prepare synthetic helical polymers, and those that exist have mainly been limited to single-stranded polymers or short oligomeric double helices. Here, we report the synthesis of covalent helical polymers, with a high molecular weight, from the achiral monomer hexahydroxytriphenylene through to spiroborate formation. Polymerization and crystallization occurred simultaneously under solvothermal conditions to form single crystals of the resulting helical covalent polymers. Characterization by single-crystal X-ray diffraction showed that each crystal consisted of pairs of mechanically entwined polymers. No strong non-covalent interactions were observed between the two helical polymers that formed a pair; instead, each strand interacted with neighbouring pairs through hydrogen bonding. Each individual crystal was made up of helical polymers of the same handedness, but the crystallization process produced a racemic conglomerate, with equal amounts of right-handed and left-handed crystals.

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer. [ABSTRACT FROM AUTHOR]

This paper investigates the mean-square bounded consensus issue in a two-layer multi-agent network under deception attacks. The two-layer network is composed of the leader and follower layers with a switching topology. Employing an impulsive control method, the mean-square bounded consensus for the leader layer and the node-to-node mean-square bounded consensus of the two-layer network are analyzed. Based on the knowledge of graph theory, Lyapunov stability theory, and linear matrix inequalities, sufficient conditions for the mean-square bounded consensus of multi-agent systems in the two-layer network are derived. Finally, the practicability and efficacy of the theoretical outcomes are corroborated via the provided numerical simulations. [ABSTRACT FROM AUTHOR]

The electrocatalytic CO2 reduction reaction (eCO2RR) stands out as a highly promising approach to simultaneously resolving the issue of the elevated atmospheric CO2 concentration and its utilization to produce value-added products. One of the present challenges is the rational design of efficient catalysts toward the eCO2RR and the understanding of their structure–activity relationship. Covalent porous catalysts (CPCs) with atomically dispersed active sites embedded in the porous organic backbone, including polymer-based catalysts, such as covalent organic frameworks (COFs) and conjugated microporous polymers (CMPs), and molecule-based catalysts, such as porous organic cages (POCs) have received increasing attention recently. They feature extremely high atom utilization, customizable porosity, a customizable backbone, and distinct electronic properties. In this review, the design principles of CPCs, both in their selection of metal sites (metallic centers, heterometallic centers and their axial coordination) and optimization of organic backbones (chelating sites, their electronic modulation and microstructures) will be covered, aiming to provide guidance to tune their eCO2RR performance. Then representative examples will be discussed to reveal their structure–activity relationships. The perspective and key challenges in CPC based eCO2RR electrocatalysts will be finally proposed. [ABSTRACT FROM AUTHOR]

The increasing global energy consumption has led to the rapid development of renewable energy storage technologies. Lithium‐ion batteries (LIBs) have been extensively studied and utilized for reliable, efficient, and sustainable energy storage. Nevertheless, designing new materials for LIB applications with high capacity and long‐term stability is highly desired but remains a challenging task. Recently, covalent organic frameworks (COFs) have emerged as superior candidates for LIB applications due to their high porosity, well‐defined pores, highly customizable structure, and tunable functionalities. These merits enable the preparation of tailored COFs with predesigned redox‐active moieties and suitable porous channels that can improve the lithium‐ion storage and transportation. This review summarizes the recent progress in the development of COFs and their composites for a variety of LIB applications, including (quasi) solid‐state electrolytes, electrode materials, and separators. Finally, the challenges and potential future directions of employing COFs for LIBs are also briefly discussed, further promoting the foundation of this class of exciting materials for future advances in energy‐related applications. [ABSTRACT FROM AUTHOR]

This article investigates the mean-square bounded consensus problem for nonlinear multi-agent systems under hybrid attacks composed of deception attacks and denial-of-service attacks. The network consists of a leader and several followers. The attacks can disrupt network connections and inject false data. Using an impulsive control approach, the mean-square bounded consensus is analyzed. Meanwhile, the upper bound of error is also calculated. Based on the knowledge of the Lyapunov stability theory, graph theory, and linear matrix inequalities, sufficient conditions for the mean-square bounded consensus of multi-agent systems are obtained. Finally, the feasibility and validity of the theoretical results are verified by the provided numerical simulations. [ABSTRACT FROM AUTHOR]

Single-crystal polymers (SCPs) with unambiguous chemical structures at atomic-level resolutions have attracted great attention. Obtaining precise structural information of these materials is critical as it enables a deeper understanding of the potential driving forces for specific packing and long-range order, secondary interactions, and kinetic and thermodynamic factors. Such information can ultimately lead to success in controlling the synthesis or engineering of their crystal structures for targeted applications, which could have far-reaching impact. Successful synthesis of SCPs with atomic level control of the structures, especially for those with 2D and 3D architectures, is rare. In this review, we summarize the recent progress in the synthesis of SCPs, including 1D, 2D, and 3D architectures. Solution synthesis, topochemical synthesis, and extreme condition synthesis are summarized and compared. Around 70 examples of SCPs with unambiguous structure information are presented, and their synthesis methods and structural analysis are discussed. This review offers critical insights into the structure–property relationships, providing guidance for the future rational design and bottom-up synthesis of a variety of highly ordered polymers with unprecedented functions and properties. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Although polyimides (PIs) have shown great potential for a broad range of applications, it remains very challenging to achieve the malleability, rehealability and recyclability for PIs and their composites targeting various applications, particularly for the rapidly emerging flexible and stretchable electronics. Herein, malleable conductive poly(imide‐imine) hybrid (PIIH) vitrimer‐graphene aerogel (GA) composites have been prepared, for the first time, via simple sol‐gel film formation followed by heat‐press. The resulting PIIH‐GA composites exhibit not only the highly desired properties of thermosetting (strong mechanical strength) and thermoplastic (reprocessability) polymers, but also good conductivity enabled by the GA filler. PIIH3‐GA‐10 (with 10 wt% GA) showed one of the highest electrical conductivities (26.7 S/m) for PI‐based composites, as well as good electromagnetic interference (EMI) shielding performance. Moreover, the PIIH‐GA films could maintain good performance during stretching and even after chemical recycling, which opens new opportunities for flexible and sustainable electronics development. [ABSTRACT FROM AUTHOR]