Shanghai University

This paper presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 400–1200 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO2 emissions of the baseline scenarios range from about 25 GtCO2 to more than 120 GtCO2 per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: (1) the policy assumptions, (2) the socio-economic narrative, and (3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m2 that is consistent with a temperature change limit of 2 °C, differs in our analysis thus by about a factor of three across the SSP marker scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectoral extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6).

This article describes the implementation of real-space refinement in the phenix.real_space_refine program from the PHENIX suite. The use of a simplified refinement target function enables very fast calculation, which in turn makes it possible to identify optimal data-restraint weights as part of routine refinements with little runtime cost. Refinement of atomic models against low-resolution data benefits from the inclusion of as much additional information as is available. In addition to standard restraints on covalent geometry, phenix.real_space_refine makes use of extra information such as secondary-structure and rotamer-specific restraints, as well as restraints or constraints on internal molecular symmetry. The re-refinement of 385 cryo-EM-derived models available in the Protein Data Bank at resolutions of 6 Å or better shows significant improvement of the models and of the fit of these models to the target maps.

Water-soluble graphene quantum dots (GQDs, ca. 10 nm in diameter) that exhibit bright blue photoluminescence (PL) are prepared by hydrothermal (chemical) cutting of oxidized graphene sheets (see figure). The mechanisms of the cutting and luminescence are discussed. This discovery of PL of GQDs may extend the range of application of graphene-based materials to optoelectronics and biological labeling.

The SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China, and has since impacted a large portion of China and raised major global concern. Herein, we investigated the extent of molecular divergence between SARS-CoV-2 and other related coronaviruses. Although we found only 4% variability in genomic nucleotides between SARS-CoV-2 and a bat SARS-related coronavirus (SARSr-CoV; RaTG13), the difference at neutral sites was 17%, suggesting the divergence between the two viruses is much larger than previously estimated. Our results suggest that the development of new variations in functional sites in the receptor-binding domain (RBD) of the spike seen in SARS-CoV-2 and viruses from pangolin SARSr-CoVs are likely caused by natural selection besides recombination. Population genetic analyses of 103 SARS-CoV-2 genomes indicated that these viruses had two major lineages (designated L and S), that are well defined by two different SNPs that show nearly complete linkage across the viral strains sequenced to date. We found that L lineage was more prevalent than the S lineage within the limited patient samples we examined. The implication of these evolutionary changes on disease etiology remains unclear. These findings strongly underscores the urgent need for further comprehensive studies that combine viral genomic data, with epidemiological studies of coronavirus disease 2019 (COVID-19).

drug design cycle to generate large sets of novel molecules for drug discovery.

Selective catalytic reduction with NH3 (NH3–SCR) is the most efficient technology to reduce the emission of nitrogen oxides (NOx) from coal-fired industries, diesel engines, etc. Although V2O5–WO3(MoO3)/TiO2 and CHA structured zeolite catalysts have been utilized in commercial applications, the increasing requirements for broad working temperature window, strong SO2/alkali/heavy metal-resistance, and high hydrothermal stability have stimulated the development of new-type NH3–SCR catalysts. This review summarizes the latest SCR reaction mechanisms and emerging poison-resistant mechanisms in the beginning and subsequently gives a comprehensive overview of newly developed SCR catalysts, including metal oxide catalysts ranging from VOx, MnOx, CeO2, and Fe2O3 to CuO based catalysts; acidic compound catalysts containing vanadate, phosphate and sulfate catalysts; ion exchanged zeolite catalysts such as Fe, Cu, Mn, etc. exchanged zeolite catalysts; monolith catalysts including extruded, washcoated, and metal-mesh/foam-based monolith catalysts. The challenges and opportunities for each type of catalysts are proposed while the effective strategies are summarized for enhancing the acidity/redox circle and poison-resistance through modification, creating novel nanostructures, exposing specific crystalline planes, constructing protective/sacrificial sites, etc. Some suggestions are given about future research directions that efforts should be made in. Hopefully, this review can bridge the gap between newly developed catalysts and practical requirements to realize their commercial applications in the near future.

Abstract A passivation layer called the solid electrolyte interphase (SEI) is formed on electrode surfaces from decomposition products of electrolytes. The SEI allows Li + transport and blocks electrons in order to prevent further electrolyte decomposition and ensure continued electrochemical reactions. The formation and growth mechanism of the nanometer thick SEI films are yet to be completely understood owing to their complex structure and lack of reliable in situ experimental techniques. Significant advances in computational methods have made it possible to predictively model the fundamentals of SEI. This review aims to give an overview of state-of-the-art modeling progress in the investigation of SEI films on the anodes, ranging from electronic structure calculations to mesoscale modeling, covering the thermodynamics and kinetics of electrolyte reduction reactions, SEI formation, modification through electrolyte design, correlation of SEI properties with battery performance, and the artificial SEI design. Multi-scale simulations have been summarized and compared with each other as well as with experiments. Computational details of the fundamental properties of SEI, such as electron tunneling, Li-ion transport, chemical/mechanical stability of the bulk SEI and electrode/(SEI/) electrolyte interfaces have been discussed. This review shows the potential of computational approaches in the deconvolution of SEI properties and design of artificial SEI. We believe that computational modeling can be integrated with experiments to complement each other and lead to a better understanding of the complex SEI for the development of a highly efficient battery in the future.

Over the last decade, organic photothermal therapy (PTT) agents have attracted increasing attention as a potential complement for, or alternative to, classical drugs and sensitizers involving inorganic nanomaterials. In this tutorial review, we provide a structured description of the main classes of organic photothermal agents and their characteristics. Representative agents that have been studied in the context of photothermal therapy since 2000 are summarized and recent advances in using PTT agents to address various cancers indications are highlighted.

Existing knowledge on remote working can be questioned in an extraordinary pandemic context. We conducted a mixed-methods investigation to explore the challenges experienced by remote workers at this time, as well as what virtual work characteristics and individual differences affect these challenges. In Study 1, from semi-structured interviews with Chinese employees working from home in the early days of the pandemic, we identified four key remote work challenges (work-home interference, ineffective communication, procrastination, and loneliness), as well as four virtual work characteristics that affected the experience of these challenges (social support, job autonomy, monitoring, and workload) and one key individual difference factor (workers' self-discipline). In Study 2, using survey data from 522 employees working at home during the pandemic, we found that virtual work characteristics linked to worker's performance and well-being via the experienced challenges. Specifically, social support was positively correlated with lower levels of all remote working challenges; job autonomy negatively related to loneliness; workload and monitoring both linked to higher work-home interference; and workload additionally linked to lower procrastination. Self-discipline was a significant moderator of several of these relationships. We discuss the implications of our research for the pandemic and beyond.

Methylation at the N6 position of adenosine (m(6)A) is the most abundant RNA modification within protein-coding and long noncoding RNAs in eukaryotes and is a reversible process with important biological functions. YT521-B homology domain family (YTHDF) proteins are the readers of m(6)A, the binding of which results in the alteration of the translation efficiency and stability of m(6)A-containing RNAs. However, the mechanism by which YTHDF proteins cause the degradation of m(6)A-containing RNAs is poorly understood. Here we report that m(6)A-containing RNAs exhibit accelerated deadenylation that is mediated by the CCR4-NOT deadenylase complex. We further show that YTHDF2 recruits the CCR4-NOT complex through a direct interaction between the YTHDF2 N-terminal region and the SH domain of the CNOT1 subunit, and that this recruitment is essential for the deadenylation of m(6)A-containing RNAs by CAF1 and CCR4. Therefore, we have uncovered the mechanism of YTHDF2-mediated degradation of m(6)A-containing RNAs in mammalian cells.

It was found and recently reported that small carbon nanoparticles can be surface-passivated by organic or biomolecules to become strongly fluorescent. These fluorescent carbon nanoparticles, dubbed "carbon dots", can be successfully used for in vitro cell imaging with both one- and two-photon excitations, as already demonstrated in the literature. Here we report the first study using carbon dots for optical imaging in live mice. The results suggest that the carbon dots remain strongly fluorescent in vivo, which, coupled with their biocompatibility and nontoxic characteristics, might offer great potential for imaging and related biomedical applications.

Microglia activation is observed in various neurodegenerative diseases. Recent advances in single-cell technologies have revealed that these reactive microglia were with high spatial and temporal heterogeneity. Some identified microglia in specific states correlate with pathological hallmarks and are associated with specific functions. Microglia both exert protective function by phagocytosing and clearing pathological protein aggregates and play detrimental roles due to excessive uptake of protein aggregates, which would lead to microglial phagocytic ability impairment, neuroinflammation, and eventually neurodegeneration. In addition, peripheral immune cells infiltration shapes microglia into a pro-inflammatory phenotype and accelerates disease progression. Microglia also act as a mobile vehicle to propagate protein aggregates. Extracellular vesicles released from microglia and autophagy impairment in microglia all contribute to pathological progression and neurodegeneration. Thus, enhancing microglial phagocytosis, reducing microglial-mediated neuroinflammation, inhibiting microglial exosome synthesis and secretion, and promoting microglial conversion into a protective phenotype are considered to be promising strategies for the therapy of neurodegenerative diseases. Here we comprehensively review the biology of microglia and the roles of microglia in neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, multiple system atrophy, amyotrophic lateral sclerosis, frontotemporal dementia, progressive supranuclear palsy, corticobasal degeneration, dementia with Lewy bodies and Huntington's disease. We also summarize the possible microglia-targeted interventions and treatments against neurodegenerative diseases with preclinical and clinical evidence in cell experiments, animal studies, and clinical trials.

Abstract Zn‐based batteries are safe, low cost, and environmentally friendly, as well as delivering the highest energy density of all aqueous battery systems. However, the application of Zn‐based batteries is being seriously hindered by the uneven electrostripping/electroplating of Zn on the anodes, which always leads to enlarged polarization (capacity fading) or even cell shorting (low cycling stability). How a porous nano‐CaCO 3 coating can guide uniform and position‐selected Zn stripping/plating on the nano‐CaCO 3 ‐layer/Zn foil interfaces is reported here. This Zn‐deposition‐guiding ability is mainly ascribed to the porous nature of the nano‐CaCO 3 ‐layer, since similar functionality (even though relatively inferior) is also found in Zn foils coated with porous acetylene black or nano‐SiO 2 layers. Furthermore, the potential application of this strategy is demonstrated in Zn|ZnSO 4 +MnSO 4 |CNT/MnO 2 rechargeable aqueous batteries. Compared with the ones with bare Zn anodes, the battery with a nano‐CaCO 3 ‐coated Zn anode delivers a 42.7% higher discharge capacity (177 vs 124 mAh g −1 at 1 A g −1 ) after 1000 cycles.

The screening of novel materials with good performance and the modelling of quantitative structure-activity relationships (QSARs), among other issues, are hot topics in the field of materials science. Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations. Thus, it is imperative to develop a new method of accelerating the discovery and design process for novel materials. Recently, materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy. In this review, we first outline the typical mode of and basic procedures for applying machine learning in materials science, and we classify and compare the main algorithms. Then, the current research status is reviewed with regard to applications of machine learning in material property prediction, in new materials discovery and for other purposes. Finally, we discuss problems related to machine learning in materials science, propose possible solutions, and forecast potential directions of future research. By directly combining computational studies with experiments, we hope to provide insight into the parameters that affect the properties of materials, thereby enabling more efficient and target-oriented research on materials discovery and design. Machine learning provides a new means of screening novel materials with good performance, developing quantitative structure-activity relationships (QSARs) and other models, predicting the properties of materials, discovering new materials and performing other materials-relateds studies. • The typical mode of and basic procedures for applying machine learning in materials science are summarized and discussed. • For various points of application, the machine learning methods used for different purposes are comprehensively reviewed. • Existing problems are discussed, possible solutions are proposed and potential directions of future research are suggested.

A coronavirus (HCoV-19) has caused the novel coronavirus disease (COVID-19) outbreak in Wuhan, China. Preventing and reversing the cytokine storm may be the key to save the patients with severe COVID-19 pneumonia. Mesenchymal stem cells (MSCs) have been shown to possess a comprehensive powerful immunomodulatory function. This study aims to investigate whether MSC transplantation improves the outcome of 7 enrolled patients with COVID-19 pneumonia in Beijing YouAn Hospital, China, from Jan 23, 2020 to Feb 16, 2020. The clinical outcomes, as well as changes of inflammatory and immune function levels and adverse effects of 7 enrolled patients were assessed for 14 days after MSC injection. MSCs could cure or significantly improve the functional outcomes of seven patients without observed adverse effects. The pulmonary function and symptoms of these seven patients were significantly improved in 2 days after MSC transplantation. Among them, two common and one severe patient were recovered and discharged in 10 days after treatment. After treatment, the peripheral lymphocytes were increased, the C-reactive protein decreased, and the overactivated cytokine-secreting immune cells CXCR3+CD4+ T cells, CXCR3+CD8+ T cells, and CXCR3+ NK cells disappeared in 3-6 days. In addition, a group of CD14+CD11c+CD11b^mid regulatory DC cell population dramatically increased. Meanwhile, the level of TNF-α was significantly decreased, while IL-10 increased in MSC treatment group compared to the placebo control group. Furthermore, the gene expression profile showed MSCs were ACE2^- and TMPRSS2^- which indicated MSCs are free from COVID-19 infection. Thus, the intravenous transplantation of MSCs was safe and effective for treatment in patients with COVID-19 pneumonia, especially for the patients in critically severe condition.

Abstract Potassium-ion batteries are a promising alternative to lithium-ion batteries. However, it is challenging to achieve fast charging/discharging and long cycle life with the current electrode materials because of the sluggish potassiation kinetics. Here we report a soft carbon anode, namely highly nitrogen-doped carbon nanofibers, with superior rate capability and cyclability. The anode delivers reversible capacities of 248 mAh g –1 at 25 mA g –1 and 101 mAh g –1 at 20 A g –1 , and retains 146 mAh g –1 at 2 A g –1 after 4000 cycles. Surface-dominated K-storage is verified by quantitative kinetics analysis and theoretical investigation. A full cell coupling the anode and Prussian blue cathode delivers a reversible capacity of 195 mAh g –1 at 0.2 A g –1 . Considering the cost-effectiveness and material sustainability, our work may shed some light on searching for K-storage materials with high performance.

In this study a total of 186 complex halide systems were collected; the formabilities of ABX3 (X = F, Cl, Br and I) halide perovskites were investigated using the empirical structure map, which was constructed by Goldschmidt's tolerance factor and the octahedral factor. A model for halide perovskite formability was built up. In this model obtained, for all 186 complex halides systems, only one system (CsF-MnF2) without perovskite structure and six systems (RbF-PbF2, CsF-BeF2, KCl-FeCl2, TlI-MnI2, RbI-SnI2, TlI-PbI2) with perovskite structure were wrongly classified, so its predicting accuracy reaches 96%. It is also indicated that both the tolerance factor and the octahedral factor are a necessary but not sufficient condition for ABX3 halide perovskite formability, and a lowest limit of the octahedral factor exists for halide perovskite formation. This result is consistent with our previous report for ABO3 oxide perovskite, and may be helpful to design novel halide materials with the perovskite structure.

The strength-ductility trade-off has been a long-standing dilemma in materials science. This has limited the potential of many structural materials, steels in particular. Here we report a way of enhancing the strength of twinning-induced plasticity steel at no ductility trade-off. After applying torsion to cylindrical twinning-induced plasticity steel samples to generate a gradient nanotwinned structure along the radial direction, we find that the yielding strength of the material can be doubled at no reduction in ductility. It is shown that this evasion of strength-ductility trade-off is due to the formation of a gradient hierarchical nanotwinned structure during pre-torsion and subsequent tensile deformation. A series of finite element simulations based on crystal plasticity are performed to understand why the gradient twin structure can cause strengthening and ductility retention, and how sequential torsion and tension lead to the observed hierarchical nanotwinned structure through activation of different twinning systems.

Herein, the authors demonstrate a heterostructured NiFe LDH‐NS@DG10 hybrid catalyst by coupling of exfoliated Ni–Fe layered double hydroxide (LDH) nanosheet (NS) and defective graphene (DG). The catalyst has exhibited extremely high electrocatalytic activity for oxygen evolution reaction (OER) in an alkaline solution with an overpotential of 0.21 V at a current density of 10 mA cm −2 , which is comparable to the current record (≈0.20 V in Fe–Co–Ni metal‐oxide‐film system) and superior to all other non‐noble metal catalysts. Also, it possesses outstanding kinetics (Tafel slope of 52 mV dec −1 ) for the reaction. Interestingly, the NiFe LDH‐NS@DG10 hybrid has also exhibited the high hydrogen evolution reaction (HER) performance in an alkaline solution (with an overpotential of 115 mV by 2 mg cm −2 loading at a current density of 20 mA cm −2 ) in contrast to barely HER activity for NiFe LDH‐NS itself. As a result, the bifunctional catalyst the authors developed can achieve a current density of 20 mA cm −2 by a voltage of only 1.5 V, which is also a record for the overall water splitting. Density functional theory calculation reveals that the synergetic effects of highly exposed 3d transition metal atoms and carbon defects are essential for the bifunctional activity for OER and HER.

Graphene has aroused intensive interest because of its unique structure, superior properties, and various promising applications. Graphene nanostructures with significant disorder and defects have been considered to be poor materials because disorder and defects lower their electrical conductivity. In this paper, we report that highly disordered graphene nanosheets can find promising applications in high-capacity Li ion batteries because of their exceptionally high reversible capacities (794−1054 mA h/g) and good cyclic stability. To understand the Li storage mechanism of graphene nanosheets, we have prepared graphene nanosheets with structural parameters tunable via different reduction methods including hydrazine reduction, low-temperature pyrolysis, and electron beam irradiation. The effects of these parameters on Li storage properties were investigated systematically. A key structural parameter, Raman intensity ratio of D bands to G bands, has been identified to evaluate the reversible capacity. The greatly enhanced capacity in disordered graphene nanosheets is suggested to be mainly ascribed to additional reversible storage sites such as edges and other defects.