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Covalent adaptable network (CAN) polymers doped with conductive nanoparticles are an ideal candidate to create reshapeable, rehealable, and fully recyclable electronics. On the other hand, 3D printing as a deterministic manufacturing method has a significant potential to fabricate electronics with low cost and high design freedom. In this paper, we incorporate a conductive composite consisting of polyimine CAN and multi-wall carbon nanotubes into direct-ink-writing 3D printing to create polymeric sensors with outstanding reshaping, repairing, and recycling capabilities. The developed printable ink exhibits good printability, conductivity, and recyclability. The conductivity of printed polyimine composites is investigated at different temperatures and deformation strain levels. Their shape-reforming and Joule heating-induced interfacial welding effects are demonstrated and characterized. Finally, a temperature sensor is 3D printed with defined patterns of conductive pathways, which can be easily mounted onto 3D surfaces, repaired after damage, and recycled using solvents. The sensing capability of printed sensors is maintained after the repairing and recycling. Overall, the 3D printed reshapeable, rehealable, and recyclable sensors possess complex geometry and extend service life, which assist in the development of polymer-based electronics toward broad and sustainable applications.

Many traditional ceramic techniques are inscribed on UNESCO’s Intangible Cultural Heritage lists; yet, expert scarcity, long training cycles, and stylistic homogenization impede intergenerational transmission and innovation. Although large language models offer new opportunities, research tailored to ceramics remains limited. To address this gap, we first construct EvalCera, the first open-source domain large language model evaluation dataset for ceramic knowledge, image analysis, and generation, and conduct large-scale assessments of existing general large language models on ceramic tasks, revealing their limitations. We then release the first ceramics-focused training corpus for large language models and, using it, develop CeramicGPT, the first domain-specific large language model for ceramics. Finally, we built ChatCAS, a workflow multi-agent system built on CeramicGPT and GPT-4o. Experiments show that our model and agents achieve the best performance on EvalCera (A) and (B) text tasks as well as (C) image generation. The code is publicly available.

Cutting-edge technologies of stretchable, skin-mountable, and wearable electronics have attracted tremendous attention recently due to their very wide applications and promising performances. One direction of particular interest is to investigate novel properties in stretchable electronics by exploring multifunctional materials. Here, we report an integrated strain sensing system that is highly stretchable, rehealable, fully recyclable, and reconfigurable. This system consists of dynamic covalent thermoset polyimine as the moldable substrate and encapsulation, eutectic liquid metal alloy as the strain sensing unit and interconnects, and off-the-shelf chip components for measuring and magnifying functions. The device can be attached on different parts of the human body for accurately monitoring joint motion and respiration. Such a strain sensing system provides a reliable, economical, and ecofriendly solution to wearable technologies, with wide applications in health care, prosthetics, robotics, and biomedical devices.

Chemical recycling of polymers is critical for improving the circular economy of plastics and environmental sustainability. Traditional thermoset polymers have generally been considered permanently crosslinked materials that are difficult or impossible to recycle. Herein, we demonstrate that by activating ‘dormant’ covalent bonds, traditional polycyanurate thermosets can be recycled into the original monomers, which can be circularly reused for their original purpose. Through retrosynthetic analysis, we redirected the synthetic route from forming conventional C–N bonds via irreversible cyanate trimerization to forming the C–O bonds through reversible nucleophilic aromatic substitution of alkoxy-substituted triazine derivatives by alcohol nucleophiles. The new reversible synthetic route enabled the synthesis of previously inaccessible alkyl-polycyanurate thermosets, which exhibit excellent film properties with high chemical resistance, closed-loop recyclability and reprocessing capability. These results show that ‘apparently dormant’ dynamic linkages can be activated and utilized to construct fully recyclable thermoset polymers with a broader monomer scope and increased sustainability. Alkyl and aryl polycyanurate networks have now been prepared through polymerization of diols and substituted triazines via a dynamic S N Ar reaction. When treated with excess mono alcohol or phenol, the polycyanurate networks can be depolymerized into the starting monomers, which can be separated and reused, thus achieving closed-loop recycling.

Polymer networks are a class of macromolecules that are crosslinked by smaller components typically via covalent bonds. Their exceptional stability, scalable synthesis and relatively low cost have made them mass-produced and widely used. Since first developed in the early 20th century, polymer networks have become one of the most important materials in the world. After decades of development, additional features such as malleability, sustainability, porosity and catalytic properties have been demonstrated on polymer networks. This thesis is to bottom-up design and synthesize polymer networks with desired properties by developing and using novel dynamic covalent chemistries. In chapter 1, the basics of the two novel polymer networks – covalent adaptable networks and covalent organic frameworks are introduced. And the influence of dynamic covalent chemistry in these two fields is overviewed. In chapter 2, several functional materials based on dynamic imine chemistry are studied. Methods for convenient and fast molding and repairing polyimine and its composites are developed through proper utilization of imine exchange reaction. Polyimine based conductive materials and wearable electronics are developed. In chapter 3, phthalocyanine based covalent organic frameworks with superior stability constructed by irreversible nucleophilic aromatic substitution are reported. The highly stable frameworks can be directly used as efficient electrocatalysts for the oxygen reduction reaction without pyrolysis treatment that has commonly been used in previous studies. In chapter 4, we report a new dynamic nucleophilic aromatic substitution and use it to develop ductile polycyanurates and enable recyclability and malleability for this kind of polymer networks. The findings also revealed that retrosynthetic analysis could inspire the design and synthesis of existing or novel polymeric materials to uncover unprecedented features. In chapter 5, a series of highly crystalline cyanurate-linked covalent organic frameworks synthesized via dynamic nucleophilic aromatic substitution are developed. With the stable aromatic cyanurate linkages, the material shows good stability in non-acidic conditions and high CO2/N2 selectivity. In chapter 6, conclusions and summaries of the current works are presented. In addition, perspectives and future directions are also displayed.

This paper reviews key signal processing techniques in structural health monitoring (SHM), focusing on non-parametric time–frequency analysis, adaptive decomposition, and deconvolution methods. It examines the short-time Fourier transform (STFT), wavelet transform (WT), and Wigner–Ville distribution (WVD), highlighting their applications, advantages, and limitations in SHM. The review also explores adaptive techniques like empirical mode decomposition (EMD) and its variants (EEMD, MEEMD), as well as variational mode decomposition (VMD) and its improved versions (SVMD, AVMD), emphasizing their effectiveness in handling nonlinear and non-stationary signals. Additionally, deconvolution methods such as minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) are discussed for mechanical fault diagnosis. The paper aims to provide a comprehensive overview of these techniques, offering insights for future research into SHM signal processing.

Spatial analysis of microbiomes at single cell resolution with high multiplexity and accuracy has remained challenging. Here we present spatial profiling of a microbiome using sequential error-robust fluorescence in situ hybridization (SEER-FISH), a highly multiplexed and accurate imaging method that allows mapping of microbial communities at micron-scale. We show that multiplexity of RNA profiling in microbiomes can be increased significantly by sequential rounds of probe hybridization and dissociation. Combined with error-correction strategies, we demonstrate that SEER-FISH enables accurate taxonomic identification in complex microbial communities. Using microbial communities composed of diverse bacterial taxa isolated from plant rhizospheres, we apply SEER-FISH to quantify the abundance of each taxon and map microbial biogeography on roots. At micron-scale, we identify clustering of microbial cells from multiple species on the rhizoplane. Under treatment of plant metabolites, we find spatial re-organization of microbial colonization along the root and alterations in spatial association among microbial taxa. Taken together, SEER-FISH provides a useful method for profiling the spatial ecology of complex microbial communities in situ. Spatial analysis of microbiomes at single cell resolution is challenging. Here the authors report a highly multiplexed method for spatial profiling, sequential error-robust fluorescence in situ hybridisation (SEER-FISH), and show that this allows mapping of microbial communities at micron-scale.

Double-line spectroscopic binaries (SB2s) are a vital class of spectroscopic binaries for studying star formation and evolution. Searching for SB2s has been a hot topic in astronomy. Although considerable efforts have been made with fruitful outcomes, limitations in automation and accuracy still persist. In this study, we developed a convolutional neural network model to search for SB2 candidates in LAMOST medium-resolution survey (MRS) data release (DR) 9 v1.0 by detecting double peaks in the cross-correlation function (CCF). We first generated a large number of spectra of single stars and binaries using the iSpec spectral synthesis software. The CCFs of these synthesized spectra were then calculated to form our training set. To efficiently detect the peaks of the CCFs, we applied a Softmax function-based noise reduction method. After testing and validation, the model achieved an accuracy of 97.76% in the testing set and was validated for more than 90% of the sample in several published SB2 catalogs. Finally, by applying the model to examine approximately 1.59 million LAMOST-MRS DR9 spectra, we identified 728 candidate SB2s, including 281 newly discovered ones.

Human gut Bacteroides and Parabacteroides species play crucial roles in human health and are known for their capacity to utilize diverse polysaccharides. Understanding how these bacteria utilize medicinal polysaccharides is foundational for developing polysaccharides-based prebiotics and drugs. Here, we systematically mapped the utilization profiles of 20 different medicinal polysaccharides by 28 human gut Bacteroides and Parabacteroides species. The growth profiles exhibited substantial variation across different bacterial species and medicinal polysaccharides. Ginseng polysaccharides promoted the growth of multiple Bacteroides and Parabacteroides species; in contrast, Dendrobium polysaccharides selectively promoted the growth of Bacteroides uniformis . This distinct utilization profile was associated with genomic variation in carbohydrate-active enzymes, rather than monosaccharides composition variation among medicinal polysaccharides. Through comparative transcriptomics and genetical manipulation, we validated that the polysaccharide utilization locus PUL34_ Bu enabled Bacteroides uniformis to utilize Dendrobium polysaccharides (i.e. glucomannan). In addition, we found that the GH26 enzyme in PUL34_ Bu allowed Bacteroides uniformis to utilize multiple plant-derived mannan. Overall, our results revealed the selective utilization of medicinal polysaccharide by Bacteroides and Parabacteroides species and provided insights into the use of polysaccharides in engineering the human gut microbiome. Here, the authors characterize the utilization of 20 medicinal polysaccharides by 28 human gut Bacteroides and Parabacteroides species, revealing substantial variability in bacterial growth responses, which they link to genomic differences in carbohydrate-active enzymes.