Selective Cleavage of α-Olefins to Produce Acetylene and Hydrogen

Acetylene production from mixed α-olefins emerges as a potentially green and energy-efficient approach with significant scientific value in the selective cleavage of C-C bonds. On the Pd(100) surface, it is experimentally revealed that C2 to C4 α-olefins undergo selective thermal cleavage to form surface acetylene and hydrogen. The high selectivity toward acetylene is attributed to the 4-fold hollow sites which are adept at severing the terminal double bonds in α-olefins to produce acetylene. A challenge arises, however, because acetylene tends to stay at the Pd(100) surface. By using the surface alloying methodology with alien Au, the surface Pd d-band center has been successfully shifted away from the Fermi level to release surface-generated acetylene from α-olefins as a gaseous product. Our study actually provides a technological strategy to economically produce acetylene and hydrogen from α-olefins.

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.

Stabilization of Pd0 by Cu Alloying: Theory-Guided Design of Pd3Cu Electrocatalyst for Anodic Methanol Carbonylation

Electrocatalytic carbonylation of CO and CH3OH to dimethyl carbonate (DMC) on metallic palladium (Pd) electrode offers a promising strategy for C1 valorization at the anode. However, its broader application is limited by the high working potential and the low DMC selectivity accompanied with severe methanol self-oxidation. Herein, our theoretical analysis of the intermediate adsorption interactions on both Pd0 and Pd4+ surfaces revealed that inevitable reconstruction of Pd surface under strongly oxidative potential diminishes its CO adsorption capacity, thus damaging the DMC formation. Further theoretical modeling indicates that doping Pd with Cu not only stabilizes low-valence Pd in oxidative environments but also lowers the overall energy barrier for DMC formation. Guided by this insight, we developed a facile two-step thermal shock method to prepare PdCu alloy electrocatalysts for DMC. Remarkably, the predicted Pd3Cu demonstrated the highest DMC selectivity among existing Pd-based electrocatalysts, reaching a peaked DMC selectivity of 93% at 1.0 V versus Ag/AgCl electrode. (Quasi) in-situ spectra investigations further confirmed the predicted dual role of Cu dopant in promoting Pd-catalyzed DMC formation.

Tuning the Crystallization Mechanism by Composition Vacancy in Phase Change Materials

Interface-influenced crystallization is crucial to understanding the nucleation- and growth-dominated crystallization mechanisms in phase-change materials (PCMs), but little is known. Here, we find that composition vacancy can reduce the interface energy by decreasing the coordinate number (CN) at the interface. Compared to growth-dominated GeTe, nucleation-dominated Ge2Sb2Te5 (GST) exhibits composition vacancies in the (111) interface to saturate or stabilize the Te-terminated plane. Together, the experimental and computational results provide evidence that GST prefers (111) with reduced CN. Furthermore, the (8 - n) bonding rule, rather than CN6, in the nuclei of both GeTe and GST results in lower interface energy, allowing crystallization to be observed at the simulation time in general PCMs. In comparison to GeTe, the reduced CN in the GST nuclei further decreases the interface energy, promoting faster nucleation. Our findings provide an approach to designing ultrafast phase-change memory through vacancy-stabilized interfaces.

[Disease burden of acute viral hepatitis in Guangdong Province, 1990-2019]

Objective: To examine the burden and trends of acute viral hepatitis in Guangdong Province from 1990 to 2019, and provide reference evidences for hepatitis prevention and control in the province. Methods: Data on acute viral hepatitis (hepatitis A, B, C, and E) in Guangdong from 1990 to 2019 were extracted from the Global Burden of Disease Study 2019 database. The incidence, prevalence, mortality, and disability-adjusted life years (DALY) data were analyzed by age and gender, and the estimated annual percentage change (EAPC) was calculated to describe the changing trends in disease burden. Results: From 1999 to 2019, the standardized incidence, prevalence, mortality, and DALY of acute viral hepatitis in Guangdong were higher than the national averages. In 2019, 51.43% (2 245 087/4 365 221) of acute viral hepatitis cases in Guangdong Province were mainly attributed to hepatitis B, and 77.18% (106/138) of deaths were due to acute hepatitis B. In different age groups, except for acute hepatitis B, which was more common in adults, the incidence rates of other types of viral hepatitis such as hepatitis A, B, and E showed an overall decreasing trend with age. The mortality rates of different types of acute viral hepatitis, except for the <5 age group, increased with age. The overall incidence and mortality rates of acute viral hepatitis were higher in men than in women. Conclusions: The overall burden of acute viral hepatitis in Guangdong declined in 2019, but remained higher than the national level. Further efforts are needed to strengthen hepatitis prevention and screening in different population in Guangdong Province, especially in children and the elderly.

Homolytic H2 dissociation for enhanced hydrogenation catalysis on oxides

The limited surface coverage and activity of active hydrides on oxide surfaces pose challenges for efficient hydrogenation reactions. Herein, we quantitatively distinguish the long-puzzling homolytic dissociation of hydrogen from the heterolytic pathway on Ga2O3, that is useful for enhancing hydrogenation ability of oxides. By combining transient kinetic analysis with infrared and mass spectroscopies, we identify the catalytic role of coordinatively unsaturated Ga3+ in homolytic H2 dissociation, which is formed in-situ during the initial heterolytic dissociation. This site facilitates easy hydrogen dissociation at low temperatures, resulting in a high hydride coverage on Ga2O3 (H/surface Ga3+ ratio of 1.6 and H/OH ratio of 5.6). The effectiveness of homolytic dissociation is governed by the Ga-Ga distance, which is strongly influenced by the initial coordination of Ga3+. Consequently, by tuning the coordination of active Ga3+ species as well as the coverage and activity of hydrides, we achieve enhanced hydrogenation of CO2 to CO, methanol or light olefins by 4-6 times.

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.

The Identity of Nickel Peroxide as a Nickel Superoxyhydroxide for Enhanced Electrocatalysis

Nickel peroxides are a class of stoichiometric oxidants that can selectively oxidize various organic compounds, but their molecular level structure remained elusive until now. Herein, we utilized structural prediction using the Stochastic Surface Walking method based on a neural network potential energy surface and advanced characterization using the as-synthesized nickel peroxide to unravel its chemical identity as the bridging superoxide containing nickel hydroxide, or nickel superoxyhydroxide. Superoxide incorporation tunes the local chemical environment of nickel and oxygen beyond the conventional Bode plot, offering a 6.4-fold increase in the electrocatalytic activity of urea oxidation. A volcanic dependence of the activity on the oxygen equivalents leads to the proposed active site of the Ni(OO)(OH)Ni five-membered ring. This work not only unveils the possible structures of nickel peroxides but also emphasizes the significance of tailoring the oxygen environment for advanced catalysis.

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.

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.

A versatile control program for positioning and shooting targets in laser-plasma experiments

We introduce a LabVIEW-based control program that significantly improves the efficiency and flexibility in positioning and shooting solid targets in laser-plasma experiments. The hardware driven by this program incorporates a target positioning subsystem and an imaging subsystem, which enables us to install up to 400 targets for one experimental campaign and precisely adjust them in six freedom degrees. The overall architecture and the working modes of the control program are demonstrated in detail. In addition, we characterized the distributions of target positions of every target holder and simultaneously saved the target images, resulting in a large dataset that can be used to train machine learning models and develop image recognition algorithms. This versatile control system has become an indispensable platform when preparing and conducting laser-plasma experiments.

Insights into Stereoselectivity Switch in Michael Addition-Initiated Tandem Mannich Cyclizations and Their Extension from Enamines to Vinyl Ethers

Both cis- and trans- tetracyclic spiroindolines are the core of many important biologically active indole alkaloids, but the divergent synthesis of these important motifs is largely hampered by the limited stereoselectivity control. A facile stereoinversion protocol is reported here in Michael addition-initiated tandem Mannich cyclizations for constructing tetracyclic spiroindolines, providing an easy access to two diastereoisomeric cores of monoterpene indole alkaloids with high selectivity. The mechanistic studies including in situ NMR experiments, control experiments, and DFT calculations reveal that the reaction undergoes a unique retro-Mannich/re-Mannich rearrangement including a C-C bond cleavage that is very rare for a saturated six-membered carbocycle. Insights into the stereoinversion process have been uncovered, and the major effects were determined to be the electronic properties of N-protecting groups of the indole with the aid of Lewis acid catalysts. By understanding these insights, the stereoselectivity switching strategy is also smoothly applied from enamine substrates to vinyl ether substrates, which are enriched greatly for the divergent synthesis and stereocontrol of monoterpene indole alkaloids. The current reaction also proves to be very practical and was successfully applied to the gram-scale total synthesis of strychnine and deethylibophyllidine in short routes.

In Situ Structure of a Mo-Doped Pt-Ni Catalyst during Electrochemical Oxygen Reduction Resolved from Machine Learning-Based Grand Canonical Global Optimization

Pt-Ni alloy is by far the most active cathode material for oxygen reduction reaction (ORR) in the proton-exchange membrane fuel cell, and the addition of a tiny amount of a third-metal Mo can significantly improve the catalyst durability and activity. Here, by developing machine learning-based grand canonical global optimization, we are able to resolve the in situ structures of this important three-element alloy system under ORR conditions and identify their correlations with the enhanced ORR performance. We disclose the bulk phase diagram of Pt-Ni-Mo alloys and determine the surface structures under the ORR reaction conditions by exploring millions of likely structure candidates. The pristine Pt-Ni-Mo alloy surfaces are shown to undergo significant structure reconstruction under ORR reaction conditions, where a surface-adsorbed MoO₄ monomer or Mo₂O _x dimers cover the Pt-skin surface above 0.9 V vs RHE and protect the surface from Ni leaching. The physical origins are revealed by analyzing the electronic structure of O atoms in MoO₄ and on the Pt surface. In viewing the role of high-valence transition metal oxide clusters, we propose a set of quantitative measures for designing better catalysts and predict that six elements in the periodic table, namely, Mo, Tc, Os, Ta, Re, and W, can be good candidates for alloying with PtNi to improve the ORR catalytic performance. We demonstrate that machine learning-based grand canonical global optimization is a powerful and generic tool to reveal the catalyst dynamics behavior in contact with a complex reaction environment.

Electrochemical hydrogen evolution on Pt-based catalysts from a theoretical perspective

Hydrogen evolution reaction (HER) by splitting water is a key technology toward a clean energy society, where Pt-based catalysts were long known to have the highest activity under acidic electrochemical conditions but suffer from high cost and poor stability. Here, we overview the current status of Pt-catalyzed HER from a theoretical perspective, focusing on the methodology development of electrochemistry simulation, catalytic mechanism, and catalyst stability. Recent developments in theoretical methods for studying electrochemistry are introduced, elaborating on how they describe solid-liquid interface reactions under electrochemical potentials. The HER mechanism, the reaction kinetics, and the reaction sites on Pt are then summarized, which provides an atomic-level picture of Pt catalyst surface dynamics under reaction conditions. Finally, state-of-the-art experimental solutions to improve catalyst stability are also introduced, which illustrates the significance of fundamental understandings in the new catalyst design.

Unraveling different influences of the fraction of the tetragonal phase in oxide films on the corrosion resistance of Zr alloys from the phase transition mechanism

The mechanism of Sn and Nb influence on the fraction of tetragonal ZrO₂ in oxide films on Zr alloys and their influence mechanism on corrosion resistance of Zr alloys, despite decades of research, are ambiguous due to the lack of kinetic knowledge of phase evolution of ZrO₂ with doping. Using stochastic surface walking and density functional theory calculations, we investigate the influence of Nb and Sn on the stability of tetragonal (t) and monoclinic (m) ZrO₂, and t-m phase transition in oxide films. We found that though Nb and Sn result in similar apparent variation trends in the t-phase fraction in oxide films, their influences on t-m phase transition differ significantly, which is the underlying origin of different influences of the t-phase fraction in oxide films on the corrosion resistance of Zr alloys with Sn and Nb alloying. These results clarify an important aspect of the relationship between the microstructure and corrosion resistance of Zr alloys.

Ligand vacancy channels in pillared inorganic-organic hybrids for electrocatalytic organic oxidation with enzyme-like activities

Simultaneously achieving abundant and well-defined active sites with high selectivity has been one of the ultimate goals for heterogeneous catalysis. Herein, we construct a class of Ni hydroxychloride-based inorganic-organic hybrid electrocatalysts with the inorganic Ni hydroxychloride chains pillared by the bidentate N-N ligands. The precise evacuation of N-N ligands under ultrahigh-vacuum forms ligand vacancies while partially retaining some ligands as structural pillars. The high density of ligand vacancies forms the active vacancy channel with abundant and highly-accessible undercoordinated Ni sites, exhibiting 5-25 fold and 20-400 fold activity enhancement compared to the hybrid pre-catalyst and standard β-Ni(OH)₂ for the electrochemical oxidation of 25 different organic substrates, respectively. The tunable N-N ligand can also tailor the sizes of the vacancy channels to significantly impact the substrate configuration leading to unprecedented substrate-dependent reactivities on hydroxide/oxide catalysts. This approach bridges heterogenous and homogeneous catalysis for creating efficient and functional catalysis with enzyme-like properties.

[Implications for risk management of foodborne pathogens in China from the outbreak of monophasic salmonella enterica serovar Typhimurium contaminated chocolate products]

Outbreaks caused by highly industrialized food companies are characterized by cross-border, trans-regional, rapid and unpredictable, related to serious disease and economic burden. A cluster of cases with monophasic salmonella enterica serovar Typhimurium ST34 infection suspected to be associated with consumption of contaminated chocolate products have been reported in several Europe countries since December 2021. After retrospective investigations, the buttermilk circuit in the Belgian factory was suspected to be the point of origin of the contamination. This outbreak could provide a reference for the risk management of foodborne pathogens contamination in China. The objective of this paper was to summarize the process and characteristics of the outbreak of monophasic S. Typhimurium caused by contaminated chocolate products, analyze the characteristics of ST34 monophasic S. Typhimurium and the microbial management measures in the process of chocolate products, and systematically discuss the suggestions for the risk management of foodborne pathogens contamination and countermeasures for the rapid development of industrialization of food enterprises in China, in order to provide scientific and technological support for the prevention and control, prediction and early warning of sudden cases in China.

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.

Smallest Stable Si/SiO_{2} Interface that Suppresses Quantum Tunneling from Machine-Learning-Based Global Search

While the downscaling of size for field effect transistors is highly desirable for computation efficiency, quantum tunneling at the Si/SiO_{2} interface becomes the leading concern when approaching the nanometer scale. By developing a machine-learning-based global search method, we now reveal all the likely Si/SiO_{2} interface structures from thousands of candidates. Two high Miller index Si(210) and (211) interfaces, being only ∼1  nm in periodicity, are found to possess good carrier mobility, low carrier trapping, and low interfacial energy. The results provide the basis for fabricating stepped Si surfaces for next-generation transistors.

Machine learning potential era of zeolite simulation

Zeolites, owing to their great variety and complexity in structure and wide applications in chemistry, have long been the hot topic in chemical research. This perspective first presents a short retrospect of theoretical investigations on zeolites using the tools from classical force fields to quantum mechanics calculations and to the latest machine learning (ML) potential simulations. ML potentials as the next-generation technique for atomic simulation open new avenues to simulate and interpret zeolite systems and thus hold great promise for finally predicting the structure-functionality relation of zeolites. Recent advances using ML potentials are then summarized from two main aspects: the origin of zeolite stability and the mechanism of zeolite-related catalytic reactions. We also discussed the possible scenarios of ML potential application aiming to provide instantaneous and easy access of zeolite properties. These advanced applications could now be accomplished by combining cloud-computing-based techniques with ML potential-based atomic simulations. The future development of ML potentials for zeolites in the respects of improving the calculation accuracy, expanding the application scope and constructing the zeolite-related datasets is finally outlooked.

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.

Topological Ordering of Memory Glass on Extended Length Scales

Identifying ordering in non-crystalline solids has been a focus of natural science since the publication of Zachariasen's random network theory in 1932, but it still remains as a great challenge of the century. Literature shows that the hierarchical structures, from the short-range order of first-shell polyhedra to the long-range order of translational periodicity, may survive after amorphization. Here, in a piece of AlPO₄, or berlinite, we combine X-ray diffraction and stochastic free-energy surface simulations to study its phase transition and structural ordering under pressure. From reversible single crystals to amorphous transitions, we now present an unambiguous view of the topological ordering in the amorphous phase, consisting of a swarm of Carpenter low-symmetry phases with the same topological linkage, trapped in a metastable intermediate stage. We propose that the remaining topological ordering is the origin of the switchable "memory glass" effect. Such topological ordering may hide in many amorphous materials through disordered short atomic displacements.

[The effect of prealbumin on the long-term prognosis of hilar cholangiocarcinoma following radical surgery]

Objective: To investigate the association between prealbumin and the long-term prognosis of patients with hilar cholangiocarcinoma(HCCA) following radical surgery. Methods: The clinical data of 262 HCCA patients who underwent radical surgery admitted from January 2010 to January 2017 at the First Affiliated Hospital of Army Medical University were collected,retrospectively. There were 158 males and 104 females; aged (57.6±9.9)years old(range:32 to 78 years). According to the preoperative serum prealbumin level(170 mg/L),the patients were divided into low prealbumin group(n=143) and normal prealbumin group(n=119). Follow-up until September 2020,the main research indicator was overall survival(OS), and the secondary research indicator was recurrence-free survival(RFS). The measurement data conforming to the normal distribution adopted the t test,the measurement data not conforming to the normal distribution adopted the Mann-Whitney U test,and the count data adopted the χ² test. The Kaplan-Meier method was used to calculate the cumulative survival rate. The Log-rank test was used for univariate analysis of the cumulative survival rate. Variables with P<0.10 in univariate analysis were included in the Cox proportional hazards model for multivariate analysis. Results: The 1-, 3-, and 5-year OS rate of the 262 patients was 73.4%, 32.1%, and 24.0%, respectively, and the 1-, 3-, and 5-year RFS rate was 54.6%, 25.2%, and 16.2%, respectively. Median OS and RFS were 21 months and 12 months for patients with low prealbumin and 25 months and 19 months for patients with normal prealbumin. The OS rate and RFS rate of patients in the low prealbumin group were lower than those in the normal prealbumin group, and the difference was statistically significant (both P<0.05). The results of univariate analysis indicated that low prealbumin, CA19-9>150 U/L, tumor infiltration length>3 cm, preoperative jaundice, macrovascular invasion, microvascular invasion, lymph node metastasis, and poor differentiation maybe the risk factors of OS,and low prealbumin,tumor invasion length>3 cm,macrovascular invasion, microvascular invasion,lymph node metastasis,and poor differentiation maybe the risk factors of RFS for postoperative for radical resection in patients with HCCA (all P<0.10). Multivariate results suggested that low prealbumin,tumor invasion length>3 cm,microvascular invasion,lymph node metastasis,and poor differentiation were independent risk factors affecting OS and RFS in patients with HCCA after radical operation (all P<0.05). Conclusion: Preoperative prealbumin level can predict the long-term prognosis of patients with hilar cholangiocarcinoma following radical surgery.

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.

Determination of acid structures on the surface of sulfated monoclinic and tetragonal zirconia through experimental and theoretical approaches

The acid structures on both tetragonal and monoclinic sulfated zirconia were studied and successfully proposed through experimental and theoretical approaches.

Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT

To date, the understanding of reactions at solid-liquid interfaces has proven challenging, mainly because of the inaccessible nature of such systems to current experimental techniques with atomic resolution. This has meant that many important features, including free energy barriers and the atomistic structure of intermediates, remain unknown. To tackle these issues, we construct and utilize a high-dimensional neural network (HDNN) potential for the simulation of hydrogen evolution at the HCl(aq)/Pt(111) interface, taking into consideration the influence of adsorbate-adsorbate, adsorbate-solvent interactions, and ion solvation explicitly. Long time scale MD simulations reveal coadsorbed H_ad/H₂O_ad on the surface. The free energy profiles for the Tafel and Heyrovsky type hydrogen coupling are extracted using umbrella sampling. It is found that the preferential mechanism can change depending on the surface coverage, highlighting the dual mechanistic nature for HER on Pt(111). Our work demonstrates the importance of controlling the solvent-substrate interactions in developing catalysts beyond Pt.

Deciphering and Suppressing Over-Oxidized Nitrogen in Nickel-Catalyzed Urea Electrolysis

Urea electrolysis is a prospective technology for simultaneous H₂ production and nitrogen suppression in the process of water being used for energy production. Its sustainability is currently founded on innocuous N₂ products; however, we discovered that prevalent nickel-based catalysts could generally over-oxidize urea into NO₂ ^- products with ≈80 % Faradaic efficiencies, posing potential secondary hazards to the environment. Trace amounts of over-oxidized NO₃ ^- and N₂ O were also detected. Using ¹⁵ N isotopes and urea analogues, we derived a nitrogen-fate network involving a NO₂ ^- -formation pathway via OH^- -assisted C-N cleavage and two N₂ -formation pathways via intra- and intermolecular coupling. DFT calculations confirmed that C-N cleavage is energetically more favorable. Inspired by the mechanism, a polyaniline-coating strategy was developed to locally enrich urea for increasing N₂ production by a factor of two. These findings provide complementary insights into the nitrogen fate in water-energy nexus systems.

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.

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.

Zirconia-Supported ZnO Single Layer for Syngas Conversion Revealed from Machine-Learning Atomic Simulation

ZnZrO ternary oxide represents a prominent catalytic system, identified recently for syngas conversion and CO₂ reduction via OX-ZEO technology. One intriguing observation of the ZnZrO catalyst is the very low amount of Zn required for achieving high activity, which challenges the current views on the active site of binary oxide catalysts. Herein, we demonstrate, via machine-learning-based atomic simulation, that the structure evolution of the ZnZrO system in synthesis can be traced from bulk to surface, which leads to the identification of the active site of the ZnZrO catalyst. Theory shows that an unprecedented single-layer Zn-O structure can adhere strongly to the monoclinic ZrO₂ minority (001) surface, forming a stable oxide-on-oxide interface Zn-O/M(001). The single-layer Zn-O can convert syngas to methanol with a high turnover frequency (7.38 s^-1) from microkinetics simulation. Electron structure analyses reveal that the pentahedron [ZnO₄] in Zn-O/M(001) enhances the surface electron donation to promote the catalytic activity.

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.

Reaction prediction via atomistic simulation: from quantum mechanics to machine learning

It is an ultimate goal in chemistry to predict reaction without recourse to experiment. Reaction prediction is not just the reaction rate determination of known reactions but, more broadly, the reaction exploration to identify new reaction routes. This review briefly overviews the theory on chemical reaction and the current methods for computing/estimating reaction rate and exploring reaction space. We particularly focus on the atomistic simulation methods for reaction exploration, which are benefited significantly by recently emerged machine learning potentials. We elaborate the stochastic surface walking global pathway sampling based on the global neural network (SSW-NN) potential, developed in our group since 2013, which can explore complex reactions systems unbiasedly and automatedly. Two examples, molecular reaction and heterogeneous catalytic reactions, are presented to illustrate the current status for reaction prediction using SSW-NN.

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.