Ring-Expansion Metathesis Polymerization: Catalyst-Dependent Polymerization Profiles

Ring-expansion metathesis polymerization (REMP) mediated by recently developed cyclic Ru catalysts has been studied in detail with a focus on the polymer products obtained under varied reaction conditions and catalyst architectures. Depending upon the nature of the catalyst structure, two distinct molecular weight evolutions were observed. Polymerization conducted with catalysts bearing six-carbon tethers displayed rapid polymer molecular weight growth which reached a maximum value at ca. 70% monomer conversion, resembling a chain-growth polymerization mechanism. In contrast, five-carbon-tethered catalysts led to molecular weight growth that resembled a step-growth mechanism with a steep increase occurring only after 95% monomer conversion. The underlying reason for these mechanistic differences appeared to be ready release of five-carbon-tethered catalysts from growing polymer rings, which competed significantly with propagation. Owing to reversible chain transfer and the lack of end groups in REMP, the final molecular weights of cyclic polymers was controlled by thermodynamic equilibria. Large ring sizes in the range of 60−120 kDa were observed at equilibrium for polycyclooctene and polycyclododecatriene, which were found to be independent of catalyst structure and initial monomer/catalyst ratio. While six-carbon-tethered catalysts were slowly incorporated into the formed cyclic polymer, the incorporation of five-carbon-tethered catalysts was minimal, as revealed by ICP-MS. Further polymer analysis was conducted using melt-state magic-angle spinning ^(13)C NMR spectroscopy of both linear and cyclic polymers, which revealed little or no chain ends for the latter topology

Heterogeneous ice nucleation correlates with bulk-like interfacial water

Establishing a direct correlation between interfacial water and heterogeneous ice nucleation (HIN) is essential for understanding the mechanism of ice nucleation. Here, we study the HIN efficiency of polyvinyl alcohol (PVA) surfaces with different densities of hydroxyl groups. We find that the HIN efficiency increases with the decrease of the hydroxyl group density. By explicitly considering that interfacial water molecules of PVA films consist of ‘tightly bound water’, ‘bound water’ and ‘bulk-like water’, we reveal that ‘bulk-like water’ can be correlated directly to the HIN efficiency of surfaces. As the density of hydroxyl groups decreases, ‘bulk-like water’ molecules can rearrange themselves with a reduced energy barrier into ice due to the diminishing constraint by the hydroxyl groups on the PVA surface. Our study not only provides a new strategy on experimentally controlling HIN efficiency but also gives another perspective in understanding the mechanism of ice nucleation, i.e., the phase change efficiency of ‘bulk-like’ interfacial water of a film is a predictor for the HIN efficiency of that film

Relation-aware Ensemble Learning for Knowledge Graph Embedding

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.Comment: This short paper has been accepted by EMNLP 202

Heterogeneous Distribution of Entanglements in a Nonequilibrium Polymer Melt of UHMWPE: Influence on Crystallization without and with Graphene Oxide

In the past, studies have been performed to follow chain dynamics in an equilibrium polymer melt using low molar mass polymers. Here we show that in linear ultrahigh molecular weight polyethylene entanglements formed during or after polymerization are influencing differently the overall chain topology of the polymer melt. When a disentangled UHMWPE sample is crystallized under isothermal conditions after melting, two endothermic peaks are observed. The high temperature peak is related to the melting of crystals obtained on crystallization from the disentangled domains of the heterogeneous (nonequilibrium) polymer melt, whereas the low melting temperature peak is related to the melting of crystals formed from entangled domains of the melt. On increasing the annealing time in melt, the enthalpy of the lower melting temperature peak increases at the expense of the high melting temperature peak due to the transformation of the disentangled nonequilibrium melt into the entangled equilibrium one. However, independent of the equilibrium or nonequilibrium melt state, the high melting temperature peak is observed when the disentangled samples are left to isothermally crystallize at a specific temperature, although with a decrease in bulk crystallinity. A commercial (entangled) sample, instead, shows both shift in the position of the melting temperature peak and drop in crystallinity. To ascertain that entanglements are the cause for the observed difference, experiments are performed in the presence of reduced graphene oxide (rGON): the melting response of disentangled UHMWPE crystallized from its heterogeneous melt state remains nearly independent of the annealing time in melt. This observation strengthens the concept that in the presence of a suitable filler, chain dynamics is arrested to an extent that the nonequilibrium melt state having lower entanglement density is retained

Unprecedented high-modulus high-strength tapes and films of ultrahigh molecular weight polyethylene via solvent-free route

Unprecedented high-modulus high-strength tapes and films of ultrahigh molecular weight polyethylene via solvent-free rout

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possible to acquire a complete set of MRIs, and the problem of missing modalities causes severe performance degradation in existing multimodal segmentation methods. In this work, we present the first attempt to exploit the Transformer for multimodal brain tumor segmentation that is robust to any combinatorial subset of available modalities. Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation. Besides, auxiliary regularizers are introduced in both encoder and decoder to further enhance the model's robustness to incomplete modalities. We conduct extensive experiments on the public BraTS $2018$ dataset for brain tumor segmentation. The results demonstrate that the proposed mmFormer outperforms the state-of-the-art methods for incomplete multimodal brain tumor segmentation on almost all subsets of incomplete modalities, especially by an average 19.07% improvement of Dice on tumor segmentation with only one available modality. The code is available at https://github.com/YaoZhang93/mmFormer.Comment: Accepted to MICCAI 202

Bottom-Up Enhancement of g-C 3

Disordered intermolecular interaction carbon nitride precursor prepared by water-assisted grinding of dicyandiamide was used for synthesis of g-C3N4. The final sample possesses much looser structure and provides a broadening optical window for effective light harvesting and charge separation efficiency, which exhibits significantly improved H2 evolution by photocatalytic water splitting. The bottom-up mechanochemistry method opens new vistas towards the potential applications of weak interactions in the photocatalysis field and may also stimulate novel ideas completely different from traditional ones for the design and optimization of photocatalysts

Unprecedented High-Modulus High-Strength Tapes and Films of Ultrahigh Molecular Weight Polyethylene via Solvent-Free Route

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Macromolocules, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see: http://dx.doi.org/10.1021/ma200667

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits

Fraudulent Financial Reporting in China: Evidence From Corporate Renaming

Using a sample of listed Chinese companies during 2010–2019, we examine whether corporate renaming is associated with fraudulent financial reporting. We find that companies that change their corporate names without making underlying changes to business fundamentals are more likely to commit financial reporting fraud. The positive association between corporate renaming and financial reporting fraud is more pronounced for non-state-owned enterprises and companies with a lower ownership concentration. There is further evidence that corporate renaming is more likely to be associated with disclosure-related fraud (e.g., failure to disclose or delayed disclosure) and that the likelihood of fraudulent behavior increases with the frequency of corporate renaming. Overall, the findings of this study provide evidence of a new red flag for regulators and investors investigating financial fraud. This study is timely and has policy implications for market regulators hoping to establish and improve emerging capital markets in which the information environment is generally considered weak and opaque