Extracellular vesicles are a heterogeneous group of cell-derived membranous structures comprising exosomes and microvesicles, which originate from the endosomal system or which are shed from the plasma membrane, respectively They are present in biological fluids and are involved in multiple physiological and pathological processes Extracellular vesicles are now considered as an additional mechanism for intercellular communication, allowing cells to exchange proteins, lipids and genetic material Knowledge of the cellular processes that govern extracellular vesicle biology is essential to shed light on the physiological and pathological functions of these vesicles as well as on clinical applications involving their use and/or analysis However, in this expanding field, much remains unknown regarding the origin, biogenesis, secretion, targeting and fate of these vesicles

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Shedding light on the cell biology of extracellular vesicles.

The lithium metal battery is strongly considered to be one of the most promising candidates for high-energy-density energy storage devices in our modern and technology-based society. However, uncontrollable lithium dendrite growth induces poor cycling efficiency and severe safety concerns, dragging lithium metal batteries out of practical applications. This review presents a comprehensive overview of the lithium metal anode and its dendritic lithium growth. First, the working principles and technical challenges of a lithium metal anode are underscored. Specific attention is paid to the mechanistic understandings and quantitative models for solid electrolyte interphase (SEI) formation, lithium dendrite nucleation, and growth. On the basis of previous theoretical understanding and analysis, recently proposed strategies to suppress dendrite growth of lithium metal anode and some other metal anodes are reviewed. A section dedicated to the potential of full-cell lithium metal batteries for practical applicatio...

Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review.

Since the discovery of mechanically exfoliated graphene in 2004, research on ultrathin two-dimensional (2D) nanomaterials has grown exponentially in the fields of condensed matter physics, material science, chemistry, and nanotechnology. Highlighting their compelling physical, chemical, electronic, and optical properties, as well as their various potential applications, in this Review, we summarize the state-of-art progress on the ultrathin 2D nanomaterials with a particular emphasis on their recent advances. First, we introduce the unique advances on ultrathin 2D nanomaterials, followed by the description of their composition and crystal structures. The assortments of their synthetic methods are then summarized, including insights on their advantages and limitations, alongside some recommendations on suitable characterization techniques. We also discuss in detail the utilization of these ultrathin 2D nanomaterials for wide ranges of potential applications among the electronics/optoelectronics, electrocat...

Recent Advances in Ultrathin Two-Dimensional Nanomaterials

Nitrogen (N)-doped carbon materials exhibit high electrocatalytic activity for the oxygen reduction reaction (ORR), which is essential for several renewable energy systems. However, the ORR active site (or sites) is unclear, which retards further developments of high-performance catalysts. Here, we characterized the ORR active site by using newly designed graphite (highly oriented pyrolitic graphite) model catalysts with well-defined π conjugation and well-controlled doping of N species. The ORR active site is created by pyridinic N. Carbon dioxide adsorption experiments indicated that pyridinic N also creates Lewis basic sites. The specific activities per pyridinic N in the HOPG model catalysts are comparable with those of N-doped graphene powder catalysts. Thus, the ORR active sites in N-doped carbon materials are carbon atoms with Lewis basicity next to pyridinic N.

Active sites of nitrogen-doped carbon materials for oxygen reduction reaction clarified using model catalysts

The recent advances in electrocatalysis for oxygen reduction reaction (ORR) for proton exchange membrane fuel cells (PEMFCs) are thoroughly reviewed. This comprehensive Review focuses on the low- and non-platinum electrocatalysts including advanced platinum alloys, core–shell structures, palladium-based catalysts, metal oxides and chalcogenides, carbon-based non-noble metal catalysts, and metal-free catalysts. The recent development of ORR electrocatalysts with novel structures and compositions is highlighted. The understandings of the correlation between the activity and the shape, size, composition, and synthesis method are summarized. For the carbon-based materials, their performance and stability in fuel cells and comparisons with those of platinum are documented. The research directions as well as perspectives on the further development of more active and less expensive electrocatalysts are provided.

http://repository.ust.hk/ir/bitstream/1783.1-77094/1/acs.chemrev.5b00462_withlink.pdf

Recent Advances in Electrocatalysts for Oxygen Reduction Reaction

At this stage, in order to ensure the effective work of the gravitational reference sensor, it’s necessary to ensure the residual velocity of the test mass(mass = 1.9 kg) after being released by the grabbing positioning and release mechanism is less than $5~\mu \text{m}$ /s, so that it can be normally captured by the electrode housing. Therefore, based on mathematical model of the release mechanism electro-mechanical dynamics, this paper conducts related research on the relationship between the residual speed of the test mass and the system parameters of the grabbing positioning and release mechanism. In order to solve the problem of difficulty in identifying model parameters, this paper uses the cuckoo search algorithm to identify model parameters, and improves the search efficiency and success rate by improving the step-length control factor and search strategy. Simulation experiments show that under the same number of iterations, the improved algorithm has achieved an effective improvement in the identification success rate and convergence accuracy of the original algorithm. Through the identification results, the residual speed of the test mass can be derived, verify whether the designed system meets the requirements, and establish an evaluation and guidance system for the system design.

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An Improved Cuckoo Search Algorithm of Boundary Search and Variable Step-Size for Gravitational Reference Sensor Parameters Identification

Synthesis of Enantiomerically Enriched β-Amino Alcohol Derivatives via Chiral Base Mediated-Asymmetric Deprotonation of (Benzyl Methyl Ether) Tricarbonylchromium(0) Complex and Electrophilic Trapping with Imines.

The invention discloses a hydrological dependent structure modeling method based on mutual information and vine copula. Firstly, mutual information and conditional mutual information are used to measure the correlation and uncertainty of hydrological variables, in combination with the principle of the strongest correlation and the least uncertainty, the structure of vine copula is selected, starting from the first tree, the mutual information of pairs of paired variables is calculated, the pairing mode that maximizes the sum of mutual information is selected as the edge of the tree, the conditional mutual information of possible pairing variables is calculated, and the pairing mode that maximizes the sum of conditional mutual information is selected as tree 2, and the pairing mode is repeated until the structure of the whole tree is determined. Secondly, according to the tree structure, fitting of edge distribution is carried out, a goodness-of-fit test is carried out, starting from tree 1, the AIC criterion is utilized to determine the copula type of the edge, parameters are estimated, a goodness-of-fit test is performed, then the conditional edge distribution of variables is calculated, and the determination of copula type, estimating parameters and testing steps are repeated until all the trees are determined. All trees and edges are connected to complete the modeling of hydrological dependent structures.

Hydrological dependent structure modeling method based on mutual information and vine copula

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Skraup—Doebner—Von Miller Quinoline Synthesis Revisited: Reversal of the Regiochemistry for γ‐Aryl‐β,γ‐unsaturated α‐Ketoesters.

In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data. These algorithms often incorporate sophisticated techniques, such as noise modeling, label correction, and co-training. In this study, we demonstrate that a simple baseline using cross-entropy loss, combined with widely used regularization strategies like learning rate decay, model weights average, and data augmentations, can outperform state-of-the-art methods. Our findings suggest that employing a combination of regularization strategies can be more effective than intricate algorithms in tackling the challenges of learning with noisy labels. While some of these regularization strategies have been utilized in previous noisy label learning research, their full potential has not been thoroughly explored. Our results encourage a reevaluation of benchmarks for learning with noisy labels and prompt reconsideration of the role of specialized learning algorithms designed for training with noisy labels.

Dong Wang

Papers

An Improved Cuckoo Search Algorithm of Boundary Search and Variable Step-Size for Gravitational Reference Sensor Parameters Identification

Synthesis of Enantiomerically Enriched β-Amino Alcohol Derivatives via Chiral Base Mediated-Asymmetric Deprotonation of (Benzyl Methyl Ether) Tricarbonylchromium(0) Complex and Electrophilic Trapping with Imines.

Hydrological dependent structure modeling method based on mutual information and vine copula

Skraup—Doebner—Von Miller Quinoline Synthesis Revisited: Reversal of the Regiochemistry for γ‐Aryl‐β,γ‐unsaturated α‐Ketoesters.

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels