Abundance of Low-Energy Oxygen Vacancy Pairs Dictates the Catalytic Performance of Cerium-Stabilized Zirconia

Cerium-stabilized zirconia (Ce1-xZrxOy, CZO) is renowned for its superior oxygen storage capacity (OSC), a key property long believed to be beneficial to catalytic oxidation reactions. However, 50% Ce-containing CZO recorded with the highest OSC has disappointingly poor performance in catalytic oxidation reactions compared to those with higher Ce contents but lower OSC ability. Here, we employ global neural network (G-NN)-based potential energy surface exploration methods to establish the first ternary phase diagram for bulk structures of CZO, which identifies three critical compositions of CZO, namely, 50, 60, and 80% Ce-containing CZO that are thermodynamically stable under typical synthetic conditions. 50% Ce-containing CZO, although having the highest OSC, exhibits the lowest O vacancy (Ov) diffusion rate. By contrast, 60% Ce-containing CZO, despite lower OSC (33.3% OSC compared to that of 50% Ce-containing CZO), reaches the highest Ov diffusion ability and thus offers the highest CO oxidation catalytic performance. The physical origin of the high performance of 60% Ce-containing CZO is the abundance of energetically favorable Ov pairs along the ⟨110⟩ direction, which reduces the energy barrier of Ov diffusion in the bulk and promotes O2 activation on the surface. Our results clarify the long-standing puzzles on CZO and point out that 60% Ce-containing CZO is the most desirable composition for typical CZO applications.

Amorphous Chloride Solid Electrolytes with High Li-Ion Conductivity for Stable Cycling of All-Solid-State High-Nickel Cathodes

Solid electrolytes (SEs) are central components that enable high-performance, all-solid-state lithium batteries (ASSLBs). Amorphous SEs hold great potential for ASSLBs because their grain-boundary-free characteristics facilitate intact solid-solid contact and uniform Li-ion conduction for high-performance cathodes. However, amorphous oxide SEs with limited ionic conductivities and glassy sulfide SEs with narrow electrochemical windows cannot sustain high-nickel cathodes. Herein, we report a class of amorphous Li-Ta-Cl-based chloride SEs possessing high Li-ion conductivity (up to 7.16 mS cm-1) and low Young's modulus (approximately 3 GPa) to enable excellent Li-ion conduction and intact physical contact among rigid components in ASSLBs. We reveal that the amorphous Li-Ta-Cl matrix is composed of LiCl43-, LiCl54-, LiCl65- polyhedra, and TaCl6- octahedra via machine-learning simulation, solid-state 7Li nuclear magnetic resonance, and X-ray absorption analysis. Attractively, our amorphous chloride SEs exhibit excellent compatibility with high-nickel cathodes. We demonstrate that ASSLBs comprising amorphous chloride SEs and high-nickel single-crystal cathodes (LiNi0.88Co0.07Mn0.05O2) exhibit ∼99% capacity retention after 800 cycles at ∼3 C under 1 mA h cm-2 and ∼80% capacity retention after 75 cycles at 0.2 C under a high areal capacity of 5 mA h cm-2. Most importantly, a stable operation of up to 9800 cycles with a capacity retention of ∼77% at a high rate of 3.4 C can be achieved in a freezing environment of -10 °C. Our amorphous chloride SEs will pave the way to realize high-performance high-nickel cathodes for high-energy-density ASSLBs.

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface

The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoOx, TiO2 with defects, and a series of organic reactions.

A Solution-Processable Porphyrin-Based Hydrogen-Bonded Organic Framework for Photoelectrochemical Sensing of Carbon Dioxide

Detecting CO2 in complex gas mixtures is challenging due to the presence of competitive gases in the ambient atmosphere. Photoelectrochemical (PEC) techniques offer a solution, but material selection and specificity remain limiting. Here, we constructed a hydrogen-bonded organic framework material based on a porphyrin tecton decorated with diaminotriazine (DAT) moieties. The DAT moieties on the porphyrin molecules not only facilitate the formation of complementary hydrogen bonds between the tectons but also function as recognition sites in the resulting porous HOF materials for the selective adsorption of CO2 . In addition, the in-plane growth of FDU-HOF-2 into anisotropic molecular sheets with large areas of up to 23000 μm2 and controllable thickness between 0.298 and 2.407 μm were realized in yields of over 89 % by a simple solution-processing method. The FDU-HOF-2 can be directly grown and deposited onto different substrates including silica, carbon, and metal oxides by self-assembly in situ in formic acid. As a proof of concept, a screen-printing electrode deposited with FDU-HOF-2 was fabricate as a label-free photoelectrochemical (PEC) sensor for CO2 detection. Such a signal-off PEC sensor exhibits low detection limit for CO2 (2.3 ppm), reusability (at least 30 cycles), and long-term working stability (at least 30 days).

An optimal Fe-C coordination ensemble for hydrocarbon chain growth: a full Fischer-Tropsch synthesis mechanism from machine learning

Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) because of its great significance in industry has attracted huge attention since its discovery. For Fe-based catalysts, after decades of efforts, even the product distribution remains poorly understood due to the lack of information on the active site and the chain growth mechanism. Herein powered by a newly developed machine-learning-based transition state (ML-TS) exploration method to treat properly reaction-induced surface reconstruction, we are able to resolve where and how long-chain hydrocarbons grow on complex in situ-formed Fe-carbide (FeCx) surfaces from thousands of pathway candidates. Microkinetics simulations based on first-principles kinetics data further determine the rate-determining and the selectivity-controlling steps, and reveal the fine details of the product distribution in obeying and deviating from the Anderson-Schulz-Flory law. By showing that all FeCx phases can grow coherently upon each other, we demonstrate that the FTS active site, namely the A-P5 site present on reconstructed Fe3C(031), Fe5C2(510), Fe5C2(021), and Fe7C3(071) terrace surfaces, is not necessarily connected to any particular FeCx phase, rationalizing long-standing structure-activity puzzles. The optimal Fe-C coordination ensemble of the A-P5 site exhibits both Fe-carbide (Fe4C square) and metal Fe (Fe3 trimer) features.

Site-specific anisotropic assembly of amorphous mesoporous subunits on crystalline metal-organic framework

As an important branch of anisotropic nanohybrids (ANHs) with multiple surfaces and functions, the porous ANHs (p-ANHs) have attracted extensive attentions because of the unique characteristics of high surface area, tunable pore structures and controllable framework compositions, etc. However, due to the large surface-chemistry and lattice mismatches between the crystalline and amorphous porous nanomaterials, the site-specific anisotropic assembly of amorphous subunits on crystalline host is challenging. Here, we report a selective occupation strategy to achieve site-specific anisotropic growth of amorphous mesoporous subunits on crystalline metal-organic framework (MOF). The amorphous polydopamine (mPDA) building blocks can be controllably grown on the {100} (type 1) or {110} (type 2) facets of crystalline ZIF-8 to form the binary super-structured p-ANHs. Based on the secondary epitaxial growth of tertiary MOF building blocks on type 1 and 2 nanostructures, the ternary p-ANHs with controllable compositions and architectures are also rationally synthesized (type 3 and 4). These intricate and unprecedented superstructures provide a good platform for the construction of nanocomposites with multiple functionalities and understanding of the structure-property-function relationships.

Methanol Synthesis from CO2/CO Mixture on Cu-Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search

Methanol synthesis on industrial Cu/ZnO/Al₂O₃ catalysts via the hydrogenation of CO and CO₂ mixture, despite several decades of research, is still puzzling due to the nature of the active site and the role of CO₂ in the feed gas. Herein, with the large-scale machine learning atomic simulation, we develop a microkinetics-guided machine learning pathway search to explore thousands of reaction pathways for CO₂ and CO hydrogenations on thermodynamically favorable Cu-Zn surface structures, including Cu(111), Cu(211), and Zn-alloyed Cu(211) surfaces, from which the lowest energy pathways are identified. We find that Zn decorates at the step-edge at Cu(211) up to 0.22 ML under reaction conditions with the Zn-Zn dimeric sites being avoided. CO₂ and CO hydrogenations occur exclusively at the step-edge of the (211) surface with up to 0.11 ML Zn coverage, where the low coverage of Zn (0.11 ML) does not much affect the reaction kinetics, but the higher coverages of Zn (0.22 ML) poison the catalyst. It is CO₂ hydrogenation instead of CO hydrogenation that dominates methanol synthesis, agreeing with previous isotope experiments. While metallic steps are identified as the major active site, we show that the [-Zn-OH-Zn-] chains (cationic Zn) can grow on Cu(111) surfaces under reaction conditions, which suggests the critical role of CO in the mixed gas for reducing the cationic Zn and exposing metal sites for methanol synthesis. Our results provide a comprehensive picture on the dynamic coupling of the feed gas composition, the catalyst active site, and the reaction activity in this complex heterogeneous catalytic system.

In Situ Active Site for Fe-Catalyzed Fischer-Tropsch Synthesis: Recent Progress and Future Challenges

Fischer-Tropsch synthesis (FTS) that converts syngas into long-chain hydrocarbons is a key technology in the chemical industry. As one of the best catalysts for FTS, the Fe-based composite develops rich solid phases (metal, oxides, and carbides) in the catalytic reaction, which triggered the quest for the true active site in catalysis in the past century. Recent years have seen great advances in probing the active-site structure using modern experimental and theoretical tools. This Perspective serves to highlight these latest achievements, focusing on the geometrical structure and thermodynamic stability of Fe carbide bulk phases, the exposed surfaces, and their relationship to FTS activity. The current reaction mechanisms on CO activation and carbon chain growth are also discussed, in the context of theoretical models and experimental evidence. We also present the outlook regarding the current challenges in Fe-based FTS.

Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes

Solid electrolytes (SEs) with superionic conductivity and interfacial stability are highly desirable for stable all-solid-state Li-metal batteries (ASSLMBs). Here, we employ neural network potential to simulate materials composed of Li, Zr/Hf, and Cl using stochastic surface walking method and identify two potential unique layered halide SEs, named Li₂ZrCl₆ and Li₂HfCl₆, for stable ASSLMBs. The predicted halide SEs possess high Li⁺ conductivity and outstanding compatibility with Li metal anodes. We synthesize these SEs and demonstrate their superior stability against Li metal anodes with a record performance of 4000 h of steady lithium plating/stripping. We further fabricate the prototype stable ASSLMBs using these halide SEs without any interfacial modifications, showing small internal cathode/SE resistance (19.48 Ω cm²), high average Coulombic efficiency (∼99.48%), good rate capability (63 mAh g^-1 at 1.5 C), and unprecedented cycling stability (87% capacity retention for 70 cycles at 0.5 C).

Automated search for optimal surface phases (ASOPs) in grand canonical ensemble powered by machine learning

The surface of a material often undergoes dramatic structure evolution under a chemical environment, which, in turn, helps determine the different properties of the material. Here, we develop a general-purpose method for the automated search of optimal surface phases (ASOPs) in the grand canonical ensemble, which is facilitated by the stochastic surface walking (SSW) global optimization based on global neural network (G-NN) potential. The ASOP simulation starts by enumerating a series of composition grids, then utilizes SSW-NN to explore the configuration and composition spaces of surface phases, and relies on the Monte Carlo scheme to focus on energetically favorable compositions. The method is applied to silver surface oxide formation under the catalytic ethene epoxidation conditions. The known phases of surface oxides on Ag(111) are reproduced, and new phases on Ag(100) are revealed, which exhibit novel structure features that could be critical for understanding ethene epoxidation. Our results demonstrate that the ASOP method provides an automated and efficient way for probing complex surface structures that are beneficial for designing new functional materials under working conditions.

In Situ Active Site for CO Activation in Fe-Catalyzed Fischer-Tropsch Synthesis from Machine Learning

In situ-formed iron carbides (FeC_x) are the key components responsible for Fischer-Tropsch synthesis (FTS, CO + H₂ → long-chain hydrocarbons) on Fe-based catalysts in industry. The true active site is, however, highly controversial despite more than a century of study, which is largely due to the combined complexity in both FeC_x structures and mechanism of CO hydrogenation. Herein powered by machine learning simulation, millions of structure candidates for FeC_x bulk and surfaces are explored under FTS conditions, which leads to resolving the active site for CO activation. This is achieved without a priori input from experiment by first constructing the thermodynamics convex hull of bulk phases, followed by identifying the low surface energy surfaces and evaluating the adsorption ability of CO and H, and finally determining the lowest energy reaction pathway of CO activation. Rich information on FeC_x structures and CO hydrogenation pathways is gleaned: (i) Fe₅C₂, Fe₇C₃, and Fe₂C are the three stable bulk phases under FTS in producing olefins, where Fe₇C₃ and Fe₂C have multiple energetically nearly degenerate bulk crystal phases; (ii) only three low surface energy surfaces of these bulk phases, namely, χ-Fe₅C₂(510), χ-Fe₅C₂(111), and η-Fe₂C(111), expose the Fe sites that can adsorb H atoms exothermically, where the surface Fe:C ratio is 2, 1.75, and 2, respectively; (iii) CO activation via direct dissociation can occur at the surface C vacancies (e.g., with a barrier of 1.1 eV) that are created dynamically via hydrogenation. These atomic-level understandings facilitate the building of the structure-activity correlation and designing better FT catalysts.

Antitumor effects of apoptin expressed by the dual cancer-specific oncolytic adenovirus - a review

Apoptin is a small molecular weight protein derived from chicken anemia virus, which can induce the apoptosis of transformed cells and tumor cells and leave primary and nontransformed cells unharmed. Apoptin's cell localization depends on its own phosphorylation state and cell type. In tumor cells, phosphorylated apoptin enters the nucleus and induces apoptosis. While, in normal cells apoptin mainly exists in the cytoplasm. Apoptin, as a disordered protein in cells, interacts with many proteins in cell signal pathways to induce apoptosis of tumor cells. The specific mechanism of apoptosis induced by apoptin has not been completely elucidated. Therefore, apoptin has become a potential anticancer agent. This review summarizes the research results of apoptin in our laboratory and reveals the specific antitumor mechanism of apoptin expressed by oncolytic virus vector on a variety of tumor cells and mouse models.

In Situ Surface Structures of PdAg Catalyst and Their Influence on Acetylene Semihydrogenation Revealed by Machine Learning and Experiment

PdAg alloy is an industrial catalyst for acetylene-selective hydrogenation in excess ethene. While significant efforts have been devoted to increase the selectivity, there has been little progress in the catalyst performance at low temperatures. Here by combining a machine-learning atomic simulation and catalysis experiment, we clarify the surface status of PdAg alloy catalyst under the reaction conditions and screen out a rutile-TiO₂ supported Pd₁Ag₃ catalyst with high performance: i.e., 85% selectivity at >96% acetylene conversion over a 100 h period in an experiment. The machine-learning global potential energy surface exploration determines the Pd-Ag-H bulk and surface phase diagrams under the reaction conditions, which reveals two key bulk compositions, Pd₁Ag₁ (R3̅m) and Pd₁Ag₃ (Pm3̅m), and quantifies the surface structures with varied Pd:Ag ratios under the reaction conditions. We show that the catalyst activity is controlled by the PdAg patterns on the (111) surface that are variable under reaction conditions, but the selectivity is largely determined by the amount of Pd exposure on the (100) surface. These insights provide the fundamental basis for the rational design of a better catalyst via three measures: (i) controlling the Pd:Ag ratio at 1:3, (ii) reducing the nanoparticle size to limit PdAg local patterns, (iii) searching for active supports to terminate the (100) facets.

The dome of gold nanolized for catalysis

Gold is noble in bulk but turns out to be a superior catalyst at the nanoscale when supported on oxides, in particular titania. The critical thickness for activity, namely two-layer gold particles on titania, observed two decades ago represents one of the most influential mysteries in the recent history of heterogeneous catalysis. By developing a Bayesian optimization controlled global potential energy surface exploration tool with machine learning potential, here we determine the atomic structures of gold particles within ∼2 nm on a TiO2 surface. We show that the smallest stable Au nanoparticle is Au24 which is pinned on the oxygen-rich TiO2 and exhibits an unprecedented dome architecture made by a single-layer Au sheet but with an apparent two-atomic-layer height. Importantly, this has the highest activity for CO oxidation at room temperature. The physical origin of the high activity is the outstanding electron storage ability of the nano-dome, which activates the lattice oxygen of the oxide. The determined CO oxidation mechanism, the simulated rate and the fitted apparent energy barrier are consistent with known experimental facts, providing key evidence for the presence of both the high-activity Au dome and the low-activity close-packed Au particles in real catalysts. The future direction for the preparation of active and stable Au-based catalysts is thus outlined.

Ultrasonic Bending of Silver Nanowires

We study silver nanowires as a model for the mechanical effects of ultrasonication. Their bending is caused by the outward push of shock waves against the inertia and fluid resistance. The structural analyses of a large number of cases reveal the principles of the mechanical effects on the freely suspended colloidal nanostructures. In addition to providing knowledge of the sonication effects, we believe that understanding would help to exploit sonication for nanoscale mechanical manipulation.

Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration

Atomic simulations based on quantum mechanics (QM) calculations have entered into the tool box of chemists over the past few decades, facilitating an understanding of a wide range of chemistry problems, from structure characterization to reactivity determination. Due to the poor scaling and high computational cost intrinsic to QM calculations, one has to either sacrifice accuracy or time when performing large-scale atomic simulations. The battle to find a better compromise between accuracy and speed has been central to the development of new theoretical methods.The recent advances of machine-learning (ML)-based large-scale atomic simulations has shown great promise to the benefit of many branches of chemistry. Instead of solving the Schrödinger equation directly, ML-based simulations rely on a large data set of accurate potential energy surfaces (PESs) and complex numerical models to predict the total energy. These simulations feature both a high speed and a high accuracy for computing large systems. Due to the lack of a physical foundation in numerical models, ML models are often frustrated in their predictivity and robustness, which are key to applications. Focusing on these concerns, here we overview the recent advances in ML methodologies for atomic simulations on three key aspects. Namely, the generation of a representative data set, the extensity of ML models, and the continuity of data representation. While global optimization methods are the natural choice for building a representative data set, the stochastic surface walking method is shown to provide the desired PES sampling for both minima and transition regions on the PES. The current ML models generally utilize local geometrical descriptors as an input and consider the total energy as the sum of atomic energies. There are many flavors of data descriptors and ML models, but the applications for material and reaction predictions are still limited, not least because of the difficulty to train the associated vast global data sets. We show that our recently designed power-type structure descriptors together with a feed-forward neural network (NN) model are compatible with highly complex global PES data, which has led to a large family of global NN (G-NN) potentials.Two recent applications of G-NN potentials in material and reaction simulations are selected to illustrate how ML-based atomic simulations can help the discovery of new materials and reactions.

Thermodynamic rules for zeolite formation from machine learning based global optimization

While the [TO4] tetrahedron packing rule leads to millions of likely zeolite structures, there are currently only 252 types of zeolite frameworks reported after decades of synthetic efforts. The subtle synthetic conditions, e.g. the structure-directing agents, pH and the feed ratio, were often blamed for the limited zeolite types due to the complex kinetics. Here by developing machine learning global optimization techniques, we are now able to establish the global potential energy surface of a typical zeolite system, Si x Al y P z O2H y-z with 12 T atoms (T: Si, Al and P) that is the general formula shared by CHA, ATS, ATO and ATV zeolite frameworks. After analyzing more than 106 minima data, we identify thermodynamic rules on energetics and local bonding patterns for stable zeolites. These rules provide general guidelines to classify zeolite types and correlate them with synthesis conditions. The machine learning based atomistic simulation thus paves a new way towards rational design and synthesis of stable zeolite frameworks with desirable compositions.

Root morphological traits and distribution in direct-seeded rice under dense planting with reduced nitrogen

Water and nutrient absorption from soil by crops mainly depend on the morphological traits and distribution of the crop roots. Dense planting with reduced nitrogen is a sustainable strategy for improving grain yield and nitrogen use efficiency. However, there is little information on the effects of dense planting with reduced nitrogen on direct-seeded inbred rice. Two-year field experiments were conducted with minirhizotron techniques to characterize the root morphological traits and distributions under different nitrogen application rates and sowing densities in two representative inbred rice varieties, Huanghuazhan (HHZ) and Yuenongsimiao (YNSM), grown under three nitrogen application rates (N0: 0 kg ha^–1, LN: 135 kg ha^-1, HN: 180 kg ha^–1) and two sowing densities (LD: 18.75 kg ha⁻¹, HD: 22.5 kg ha⁻¹). Our study showed that dense planting with low nitrogen improved grain yield partly due to the increased panicle number. The higher sowing density with low nitrogen significantly affected the total root number (TRN), total root length (TRL), total root surface area (TRSA), and total root volume (TRV). There was a significant positive correlation between grain yield and TRL in the 10–20-cm soil layer (P < 0.05). The root morphological indexes were positively correlated with dry matter accumulation (P < 0.05) and negatively correlated with nitrogen content (P < 0.05) at the maturity stage. This study showed that a high sowing density with low nitrogen application can improve root morphology and distribution and increase grain yield and nitrogen use efficiency in direct-seeded inbred rice.

[Influence factors related epidemics on hantavirus disease]

Hantavirus disease is a globally distributed, natural foci-related infectious disease caused by hantavirus, that maintaining persistent infections in their rodent hosts without apparent disease symptoms but seriously affecting the health safety of human beings. Development of the disease depends on the interaction between virus, rodent host and the individual person. Factors as significant geographical and seasonal variations, certain periodicity and contingency can all be related to the incidence of hantavirus disease. The disease is affected by climate and meteorological,environment, economic and social development, human life style and individual behaviors, etc.. Results from the analysis on main influencing factors and the nature of epidemics provide as with more evidence and information in setting up programs onto timely implementation of related prevention and control measures scientifically. By searching relevant scientific and technological literature, this paper summarizes the factors that affecting the nature of transmission and infection of hantavirus from related perspectives and factors including virus, host, climate and meteorological, meteorology, geographical environment, economic and social factors, etc.. In order to elaborate on the understanding of the epidemics and transmission characteristics of this kind of diseases, this paper provides evidence on prediction, prevention and control measures of hantavirus disease.

Ultrasmall Au clusters supported on pristine and defected CeO2: Structure and stability

The atomistic simulation of supported metal catalysts has long been challenging due to the increased complexity of dual components. In order to determine the metal/support interface, efficient theoretical tools to map out the potential energy surface (PES) are generally required. This work represents the first attempt to apply the recently developed SSW-NN method, stochastic surface walking (SSW) global optimization based on global neural network potential (G-NN), to explore the PES of a highly controversial supported metal catalyst, Au/CeO₂, system. By establishing the ternary Au-Ce-O G-NN potential based on first principles global dataset, we have searched for the global minima for a series of Au/CeO₂ systems. The segregation and diffusion pathway for Au clusters on CeO₂(111) are then explored by using enhanced molecular dynamics. Our results show that the ultrasmall cationic Au clusters, e.g., Au₄O₂, attaching to surface structural defects are the only stable structural pattern and the other clusters on different CeO₂ surfaces all have a strong energy preference to grow into a bulky Au metal. Despite the thermodynamics tendency of sintering, Au clusters on CeO₂ have a high kinetics barrier (>1.4 eV) in segregation and diffusion. The high thermodynamics stability of ultrasmall cationic Au clusters and the high kinetics stability for Au clusters on CeO₂ are thus the origin for the high activity of Au/CeO₂ catalysts in a range of low temperature catalytic reactions. We demonstrate that the global PES exploration is critical for understanding the morphology and kinetics of metal clusters on oxide support, which now can be realized via the SSW-NN method.

Nonreciprocity in a strongly coupled three-mode optomechanical circulatory system

In this work, we propose a scheme in three-mode optical systems to simulate a strongly coupled optomechanical system. The nonreciprocity observed in such a three-mode optomechanical circulatory system (OMCS) is explored. To be specific, we first derive a quantum Langevin equation (QLE) for the strongly coupled OMCS by suitably choosing the laser field, then we give a condition for the frequency of the laser and the mechanical decay rate, beyond which the optomechanical system has a unidirectional transmission regardless of how strong the optomechanical coupling is. The optomechanically induced transparency is also studied. The present results can be extended to a more general two-dimensional optomechanical system and a planar quantum network, and the prediction is possible to be observed in an optomechanical crystal or integrated quantum superconducting circuit. This scheme paves a way for the construction of various quantum devices that are necessary for quantum information processing.

[Effects and mechanism of salidroside on streptozotocin-induced mode rats of diabetic nephropathy]

OBJECTIVE

To investigate the effects and mechanism of salidroside(SAL) on model rats of diabetic nephropathy(DN).

METHODS

Rats were divided into control, model, SAL(50 mg/kg), SAL(100 mg/kg) and SAL(200 mg/kg) groups. The rats beside in control group were injected with streptozotocin(STZ) combined with right nephrectomy. And rats in SAL(50, 100, 200 mg/kg) groups were received gavage with SAL(50, 100, 200 mg/kg). After 12 weeks, rats were sacrificed and kidneys were collected. HE staining was performed for renal injury, the concentrations of urine protein, urine creatine(Ucr), blood urea nitrogen(BUN), malondialdehyde(MDA), superoxide dismutase(SOD) and glutathione peroxidase(GSH-Px) were measured by kits. Western blot was used to measure the protein levels of Collagen Ⅳ, fibronectin, E-cadherin, α-smooth muscle actin(α-SMA), N-cadherin, Smad2、Smad3, phosphorylated-Smad2(p-Smad2), p-Smad3 and transforming growth factor-β1(TGF-β1).

RESULTS

Compared with control group, the renal injury of rats in model group was aggravated, the concentrations of urine protein, Ucr and BUN were elevated significantly, the apoptosis cells and positive cells of caspase-3 were increased; compared with model group, the renal injury of rats in SAL(100, 200 mg/kg) groups were alleviated markedly, the concentrations of urine protein, Ucr and BUN were reduced, the apoptosis cells and positive cells of caspase-3 were decreased notably. SAL(50 mg/kg) increased the concentration of SOD in DN model rats, SAL(100, 200 mg/kg) increased the concentrations of SOD and GSH-Px, decreased the level of MDA. Meanwhile, the inhibition of collagen Ⅳ, fibronrctin, α-SMA, and N-cadherin and the induction of E-cadherin in DN rats were induced by SAL(100, 200 mg/kg). In addition, SAL(100, 200 mg/kg) reduced the ratio of p-Smad2/Smad2, p-Smad3/Smad3 and the level of TGF-β1(P<0.05 or P<0.01).

CONCLUSION

SAL can inhibit renal fibrosis of STZ-induced DN model rats, and the mechanism may be related to inhibition of TGF-β1/Smad pathway.

Nuisance alarm reduction: Using a correlation based algorithm above differential signals in direct detected phase-OTDR systems

Significant research efforts have focused on techniques for alleviating the nuisance alarm rate (NAR) in the field of ϕ-OTDR pattern recognition systems. Unfortunately, ephemeral events were mostly neglected in previous research and algorithms meant for improving classification accuracy were emphasized at the cost of acquiring a very large number of traces. This problem engendered an additional source of NAR in a specific class of events. The proposed solution uses a novel correlation based wrapper on top of differential signals that aims to filter out the effect of unnecessary phases in direct detected ϕ-OTDR systems. This technique avoids the use of irrelevant data in these differential signals by exploiting a better use of these unnecessary phases and provides a better intensity translation with fewer acquired traces as compared with contemporary techniques of extracting features.

Atomic structure of boron resolved using machine learning and global sampling

Boron crystals, despite their simple composition, must rank top for complexity: even the atomic structure of the ground state of β-B remains uncertain after 60 years' study. This makes it difficult to understand the many exotic photoelectric properties of boron. The presence of self-doping atoms in the crystal interstitial sites forms an astronomical configurational space, making the determination of the real configuration virtually impossible using current techniques. Here, by combining machine learning with the latest stochastic surface walking (SSW) global optimization, we explore for the first time the potential energy surface of β-B, revealing 15 293 distinct configurations out of the 2 × 105 minima visited, and reveal the key rules governing the filling of the interstitial sites. This advance is only allowed by the construction of an accurate and efficient neural network (NN) potential using a new series of structural descriptors that can sensitively discriminate the complex boron bonding environment. We show that, in contrast to the conventional views on the numerous energy-degenerate configurations, only 40 minima of β-B are identified to be within 7 meV per atom in energy above the global minimum of β-B, most of them having been discovered for the first time. These low energy structures are classified into three types of skeletons and six patterns of doping configurations, with a clear preference for a few characteristic interstitial sites. The observed β-B and its properties are influenced strongly by a particular doping site, the B19 site that neighbors the B18 site, which has an exceptionally large vibrational entropy. The configuration with this B19 occupancy, which ranks only 15th at 0 K, turns out to be dominant at high temperatures. Our results highlight the novel SSW-NN architecture as the leading problem solver for complex material phenomena, which would then expedite substantially the building of a material genome database.

Material discovery by combining stochastic surface walking global optimization with a neural network

A powerful material discovery tool is invented by combining SSW global optimization with neural network computing, which identifies unprecedented TiO₂ phases.

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a “Global-to-Global” approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO₂, is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO₂ porous crystal structures are identified, which have similar thermodynamics stability to the common TiO₂ rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

Pressure-induced silica quartz amorphization studied by iterative stochastic surface walking reaction sampling

The origin of the pressure-induced amorphization of SiO₂ is resolved from theory based on pathways on the global potential energy surface.

Crystal phase transition of urea: what governs the reaction kinetics in molecular crystal phase transitions

The predominant type-I hydrogen-bonding network in urea crystals facilitates the solid-to-solid transformation between major crystal forms.

3D spin-flop transition in enhanced 2D layered structure single crystalline TlCo2Se2

The enhanced 2D layered structure single crystalline TlCo2Se2 has been successfully fabricated, which exhibits field-induced 3D spin-flop phase transitions. In the case of the magnetic field parallel to the c-axis (B//c), the applied magnetic field induces the evolution of the noncollinear helical magnetic coupling into a ferromagnetic (FM) state with all the magnetization of the Co ion parallel to the c-axis. A striking variation of the field-induced strain within the ab-plane is noticed in the magnetic field region of 20-30 T. In the case of the magnetic field perpendicular to the c-axis (B  ⊥  c), the inter-layer helical antiferromagnetic (AFM) coupling may transform to an initial canted AFM coupling, and then part of it transforms to an intermediate metamagnetic phase with the alignment of two-up-one-down Co magnetic moments and finally to an ultimate FM coupling in higher magnetic fields. The robust noncollinear AFM magnetic coupling is completely destroyed above 30 T. In combination with the measurements of magnetization, magnetoresistance and field-induced strain, a complete magnetic phase diagram of the TlCo2Se2 single crystal has been depicted, demonstrating complex magnetic structures even though the crystal geometry itself gives no indication of the magnetic frustration.

Alleviation of doxorubicin–induced hepatorenal toxicities with sesamin via the suppression of oxidative stress

Hepatorenal toxicities are an important side effect of anthracycline antibiotics. The objective of this study was to determine whether sesamin (Ses) protects against acute doxorubicin (DOX)-induced hepatorenal toxicities. Rats received daily treatment with either 0.5% carboxymethylcellulose (10 mL/kg) or Ses (10, 20 and 40 mg/kg) orally for 10 days, followed by an intravenous injection at day 8 of either saline (10 mL/kg) or DOX (20 mg/kg). Hepatorenal toxicity was assessed by measuring the levels of serum creatinine (Cre), blood urea nitrogen (BUN), aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP). The protein expression of 4-hydroxynonenal (4-HNE) in hepatorenal tissues was evaluated using immunohistochemistry. The malondialdehyde (MDA) content and antioxidant activity in the kidney and liver tissues were also measured. The results suggest that pretreatment with Ses ameliorated DOX-induced liver and kidney injury by lowering the serum ALT, AST, ALP, Cre and BUN levels ( p < 0.05 or p < 0.01), and the histological damage to the liver and kidney tissues induced by DOX compared to control were also significantly attenuated by Ses. Furthermore, Ses significantly decreased the DOX-induced increase of MDA and 4-HNE and increased the activity of CAT, SOD and GPX compared to the DOX-treated rats ( p < 0.05 or p < 0.01), whereas the change of DOX + Ses (10 mg/kg) group is not significant compared to the DOX-treated group ( p > 0.05). These findings indicate that Ses elicits a typical protective effect against DOX-induced acute hepatorenal toxicity via the suppression of oxidative stress.

Evolution of High-Temperature Superconductivity from a Low-T_{c} Phase Tuned by Carrier Concentration in FeSe Thin Flakes

We report the evolution of superconductivity in an FeSe thin flake with systematically regulated carrier concentrations by the liquid-gating technique. With electron doping tuned by the gate voltage, high-temperature superconductivity with an onset at 48 K can be achieved in an FeSe thin flake with T_{c} less than 10 K. This is the first time such high temperature superconductivity in FeSe is achieved without either an epitaxial interface or external pressure, and it definitely proves that the simple electron-doping process is able to induce high-temperature superconductivity with T_{c}^{onset} as high as 48 K in bulk FeSe. Intriguingly, our data also indicate that the superconductivity is suddenly changed from a low-T_{c} phase to a high-T_{c} phase with a Lifshitz transition at a certain carrier concentration. These results help to build a unified picture to understand the high-temperature superconductivity among all FeSe-derived superconductors and shed light on the further pursuit of a higher T_{c} in these materials.

Angular-Dependent Phase Factor of Shubnikov-de Haas Oscillations in the Dirac Semimetal Cd_{3}As_{2}

We measure the magnetotransport properties of the three-dimensional Dirac semimetal Cd_{3}As_{2} single crystal under magnetic fields up to 36 T. Shubnikov-de Haas (SdH) oscillations are clearly resolved and the n=1 Landau level is reached. A detailed analysis on the intercept of the Landau index plot reveals a significant dependence of the SdH phase factor on the orientation of the applied magnetic field. When the magnetic field is applied in the [001] direction, i.e., along the fourfold screw axis of the tetragonal crystal structure, a nontrivial π Berry phase, as predicted for the Dirac fermions, is observed. However, in a magnetic field tilted away from the [001] direction, the π Berry phase is evidently reduced, and a considerable enhancement of the effective mass is also revealed. Our observations demonstrate that the Dirac dispersion in Cd_{3}As_{2} is effectively modified in a tilted magnetic field, whereas the preserved π Berry phase in a magnetic field along the [001] direction can be related to the realization of the Weyl fermions. The sudden change of the SdH phase also indicates a possible topological phase transition induced by the symmetry-breaking effect.