Topology isomerizable networks (TINs) can be programmed into numerous polymers exhibiting unique and spatially defined (thermo-) mechanical properties. However, capturing the dynamics in topological transformations and revealing the intrinsic mechanisms of mechanical property modulation at the microscopic level is a significant challenge. Here, we use a combination of coarse-grained molecular dynamics simulations and reaction kinetic theory to reveal the impact of dynamic bond exchange reactions on the topology of branched chains. We find that, the grafted units follow a geometric distribution with a converged uniformity, which depends solely on the average grafted units of branched chains. Furthermore, we demonstrate that the topological structure can lead to spontaneous modulation of mechanical properties. The theoretical framework provides a research paradigm for studying the topology and mechanical properties of TINs.

Intra-articular injection of drugs is an effective strategy for osteoarthritis (OA) treatment. However, the complex microenvironment and limited joint space result in rapid clearance of drugs. Herein, a nanogel-based strategy is proposed for prolonged drug delivery and microenvironment remodeling. Nanogel is constructed through the functionalization of hyaluronic acid (HA) by amide reaction on the surface of Kartogenin (KGN)-loaded zeolitic imidazolate framework-8 (denoted as KZIF@HA). Leveraging the inherent hydrophilicity of HA, KZIF@HA spontaneously forms nanogels, ensuring extended drug release in the OA microenvironment. KZIF@HA exhibits sustained drug release over one month, with low leakage risk from the joint cavity compared to KZIF, enhanced cartilage penetration, and reparative effects on chondrocytes. Notably, KGN released from KZIF@HA serves to promote extracellular matrix (ECM) secretion for hyaline cartilage regeneration. Zn2+ release reverses OA progression by promoting M2 macrophage polarization to establish an anti-inflammatory microenvironment. Ultimately, KZIF@HA facilitates cartilage regeneration and OA alleviation within three months. Transcriptome sequencing validates that KZIF@HA stimulates the polarization of M2 macrophages and secretes IL-10 to inhibit the JNK and ERK pathways, promoting chondrocytes recovery and enhancing ECM remodeling. This pioneering nanogel system offers new therapeutic opportunities for sustained drug release, presenting a significant stride in OA treatment strategies.

Secreted chemokines form concentration gradients in target tissues to control migratory directions and patterns of immune cells in response to inflammatory stimulation; however, how the gradients are formed is much debated. Heparan sulfate (HS) binds to chemokines and modulates their activities. In this study, we investigated the roles of HS in the gradient formation and chemoattractant activity of CCL5 that is known to bind to HS. CCL5 and heparin underwent liquid-liquid phase separation and formed gradient, which was confirmed using CCL5 immobilized on heparin-beads. The biological implication of HS in CCL5 gradient formation was established in CHO-K1 (wild-type) and CHO-677 (lacking HS) cells by Transwell assay. The effect of HS on CCL5 chemoattractant activity was further proved by Transwell assay of human peripheral blood cells. Finally, peritoneal injection of the chemokines into mice showed reduced recruitment of inflammatory cells either by mutant CCL5 (lacking heparin-binding sequence) or by addition of heparin to wild-type CCL5. Our experimental data propose that co-phase separation of CCL5 with HS establishes a specific chemokine concentration gradient to trigger directional cell migration. The results warrant further investigation on other heparin-binding chemokines and allows for a more elaborate insight into disease process and new treatment strategies.

Traditional convolutional neural networks (CNNs) share their kernels among all positions of the input, which may constrain the representation ability in feature extraction. Dynamic convolution proposes to generate different kernels for different inputs to improve the model capacity. However, the total parameters of the dynamic network can be significantly huge. In this article, we propose a lightweight dynamic convolution method to strengthen traditional CNNs with an affordable increase of total parameters and multiply-adds. Instead of generating the whole kernels directly or combining several static kernels, we choose to "look inside," learning the attention within convolutional kernels. An extra network is used to adjust the weights of kernels for every feature aggregation operation. By combining local and global contexts, the proposed approach can capture the variance among different samples, the variance in different positions of the feature maps, and the variance in different positions inside sliding windows. With a minor increase in the number of model parameters, remarkable improvements in image classification on CIFAR and ImageNet with multiple backbones have been obtained. Experiments on object detection also verify the effectiveness of the proposed method.

Variational auto-encoders (VAEs) have been widely used in soft sensing due to their ability to provide a probabilistic description of the hidden space. However, VAEs are static models that do not consider process dynamics, which can limit the ability of VAEs to accurately model complex industrial processes. To tackle this problem, this paper proposes a model called adaptive multi-head self-attention based supervised VAE (AMSA-SVAE). In AMSA-SVAE, an adaptive multi-head self-attention mechanism (AMSA) is proposed based on the multi-head self-attention mechanism (MSA). AMSA can dynamically extract different attention information depending on specific tasks. By adjusting the attention weights based on the input sequence, AMSA allows for more accurate and efficient modeling of complex industrial processes. Then, AMSA is used as the encoder and decoder of SVAE for soft sensing. Furthermore, with the data generation capabilities of VAE, an adaptive multi-head self-attention based VAE (AMSA-VAE) framework is proposed to address the issue of missing data. The AMSA-VAE is used to dynamically fill in missing data, thereby extending the capabilities of AMSA-SVAE. Finally, the performance of AMSA-SVAE is verified by a set of real industrial data, and the ability of AMSA-VAE framework is demonstrated by simulating different degrees of data missing rates. By combining the dynamic modeling capabilities of AMSA-SVAE with the data generation capabilities of AMSA-VAE, the proposed approach provides a robust solution to the challenges of incomplete data in soft sensing. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> — Soft sensors are widely used to measure key parameters in industrial processes, but missing values in the data are common due to sensor failures or transmission signal interference. This poses a significant challenge for traditional soft sensors, which require complete data to accurately model. Meanwhile, the dynamic nature of industrial process data further complicates the modeling process. To solve these challenges, this paper proposes an AMSA-SVAE model for soft sensing and an AMSA-VAE framework for filling in the missing values in the data, thereby extending the capabilities of AMSA-SVAE to handle missing data. When facing a dataset with missing values, AMSA-VAE framework is first used to fill in the missing values before the filled complete data is fed into AMSA-SVAE for modeling. Finally, the proposed approaches are evaluated through two sets of experiments using a real industrial dataset, showing the excellent performance of AMSA-SVAE and AMSA-VAE framework in modeling dynamic industrial process data and addressing the missing data problem.

AbstractWe report the synthesis of a longitudinally helical molecular nanocarbon, hexabenzoheptacene (HBH), along with its dimethylated derivative (HBH‐Me), which are composed of six benzene rings periodically benzannulated to both zigzag edges of a heptacene core. This benzannulation pattern endows the resulting nanocarbons with a helical heptacene core and local aromaticity, imparting enhanced solubility and stability to the system. The chiral HBH‐Me adopts a more highly twisted conformation with an end‐to‐end twist angle of 95°, enabling the separation of the enantiomers. Both HBH and HBH‐Me can be facilely oxidized into their corresponding dications, which exhibit enhanced planarity and aromaticity upon loss of electrons. Notably, both longitudinally helical nanocarbons readily promote solid state packing into two‐dimensional (2D) arrangement. Single‐crystal microbelts of HBH‐Me show hole mobility up to 0.62 cm2 V−1 s−1, illustrating the promising potential of these longitudinally helical molecules for organic electronic devices.

We report the synthesis of a longitudinally helical molecular nanocarbon, hexabenzoheptacene (HBH), along with its dimethylated derivative (HBH-Me), which are composed of six benzene rings periodically benzannulated to both zigzag edges of a heptacene core. This benzannulation pattern endows the resulting nanocarbons with a helical heptacene core and local aromaticity, imparting enhanced solubility and stability to the system. The chiral HBH-Me adopts a more highly twisted conformation with an end-to-end twist angle of 95°, enabling the separation of the enantiomers. Both HBH and HBH-Me can be facilely oxidized into their corresponding dications, which exhibit enhanced planarity and aromaticity upon loss of electrons. Notably, both longitudinally helical nanocarbons readily promote solid state packing into two-dimensional (2D) arrangement. Single-crystal microbelts of HBH-Me show hole mobility up to 0.62 cm2 V-1 s-1, illustrating the promising potential of these longitudinally helical molecules for organic electronic devices.

AbstractThe single‐atom Fe−N−C catalyst has shown great promise for the oxygen reduction reaction (ORR), yet the intrinsic activity is not satisfactory. There is a pressing need to gain a deeper understanding of the charge configuration of the Fe−N−C catalyst and to develop rational modulation strategies. Herein, we have prepared a single‐atom Fe catalyst with the co‐coordination of N and O (denoted as Fe−N/O−C) and adjacent defect, proposing a strategy to optimize the d‐orbital spin‐electron filling of Fe sites by fine‐tuning the first coordination shell. The Fe−N/O−C exhibits significantly better ORR activity compared to its Fe−N−C counterpart and commercial Pt/C, with a much more positive half‐wave potential (0.927 V) and higher kinetic current density. Moreover, using the Fe−N/O−C catalyst, the Zn‐air battery and proton exchange membrane fuel cell achieve peak power densities of up to 490 and 1179 mW cm−2, respectively. Theoretical studies and in situ electrochemical Raman spectroscopy reveal that Fe−N/O−C undergoes charge redistribution and negative shifting of the d‐band center compared to Fe−N−C, thus optimizing the adsorption free energy of ORR intermediates. This work demonstrates the feasibility of introducing an asymmetric first coordination shell for single‐atom catalysts and provides a new optimization direction for their practical application.

The single-atom Fe-N-C catalyst has shown great promise for the oxygen reduction reaction (ORR), yet the intrinsic activity is not satisfactory. There is a pressing need to gain a deeper understanding of the charge configuration of the Fe-N-C catalyst and to develop rational modulation strategies. Herein, we have prepared a single-atom Fe catalyst with the co-coordination of N and O (denoted as Fe-N/O-C) and adjacent defect, proposing a strategy to optimize the d-orbital spin-electron filling of Fe sites by fine-tuning the first coordination shell. The Fe-N/O-C exhibits significantly better ORR activity compared to its Fe-N-C counterpart and commercial Pt/C, with a much more positive half-wave potential (0.927 V) and higher kinetic current density. Moreover, using the Fe-N/O-C catalyst, the Zn-air battery and proton exchange membrane fuel cell achieve peak power densities of up to 490 and 1179 mW cm-2, respectively. Theoretical studies and in situ electrochemical Raman spectroscopy reveal that Fe-N/O-C undergoes charge redistribution and negative shifting of the d-band center compared to Fe-N-C, thus optimizing the adsorption free energy of ORR intermediates. This work demonstrates the feasibility of introducing an asymmetric first coordination shell for single-atom catalysts and provides a new optimization direction for their practical application.