Toward Next-Generation Heterogeneous Catalysts:Empowering Surface Reactivity Prediction with Machine Learning

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes,and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility.Computational high-throughput screening presents a viable solution to this challenge,as machine learning(ML)has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information.This review focuses on recent progress in applying ML in adsorption energy prediction,which predominantly quantifies the catalytic potential of a solid catalyst.ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed.At the end of the review,an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied.We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and,ultimately,reshape the chemical industry and energy landscape.

A post-modification strategy to precisely construct dual-atom sites for oxygen reduction electrocatalysis

Dual-atom catalysts(DACs) afford promising potential for oxygen reduction electrocatalysis due to their high atomic efficiency and high intrinsic activity.However,precise construction of dual-atom sites remains a challenge.In this work,a post-modification strategy is proposed to precisely fabricate DACs for oxygen reduction electrocatalysis.Concretely,a secondary metal precursor is introduced to the primary single-atom sites to introduce direct metal-metal interaction,which ensures the formation of desired atom pair structure during the subsequent pyrolysis process and allows for successful construction of DACs.The as-prepared FeCo-NC DAC exhibits superior oxygen reduction electrocatalytic activity with a half-wave potential of 0,91 V vs.reversible hydrogen electrode.Zn-air batteries equipped with the FeCo-NC DAC demonstrate higher peak power density than those with the Pt/C benchmark.More importantly,this post-modification strategy is demonstrated universal to achieve a variety of dual-atom sites.This work presents an effective synthesis methodology for precise construction of catalytic materials and propels their applications in energy-related devices.

Super-exchange effect induced by early 3d metal doping on NiFe_(2)O_(4)(001)surface for oxygen evolution reaction

Understanding the intrinsic activity of oxygen evolution reaction(OER) is crucial for catalyst design.To date,different metal-doping strategies have been developed to achieve this,but the involving mechanisms remain unclear.Here,the electronic structure of the transition metal-doped NiFe_(2)O_(4)(001) surface is scrutinized for OER intrinsic activity using density functional theory calculations.Five 3d-orbital filling metals(Ti,V,Cr,Mn,and Co) are introduced as dopants onto A-and B-layers of the NiFe_(2)O_(4)(001) surface,and variation of oxidation states over Fe sites is observed on B-layer.Analyzing the magnetic moment and charge transfer of surface cation sites reveals that the variation of Fe oxidation states originates from the super-exchange effect and is influenced by the t2g-electron configuration of 3d metal dopants.This trend governs the generation of highly-active Fe3+sites on the B-layer,the adsorption strength of OER intermediates,i.e.,*O and*OH,and therefore the intrinsic activity.The finding of super-exchange mechanism induced by 3d early metal doping offers insights into electronic structure tailoring strategies for improving the intrinsic activity of OER electrocatalysts.

Effect of homojunction structure in boosting sodium-ion storage: The case of MoO_(2)

High-efficiency sodium-ion batteries(SIBs) are in great demand for energy storage applications,which are dominated by the Na+storage performance of electrode materials.Here,a one-pot solvothermal method is developed to construct amorphous/crystalline MoO_(2)(a/c-MoO_(2)) homojunction for boosting Na+storage.Theoretical simulations signify that electrons redistribute at the homogenous interface of a/c-MoO_(2),resulting in an inbuilt driving force to easily adsorb charge carriers and promote the electron/ion transfer ability.Relying on its crystallographic superiorities,the a/c-MoO_(2)homojunction with high Na adsorbability(-1.61 eV) and low Na diffusion energy barrier(0.519 eV) achieves higher capacity(307 mA h g^(-1)at 0.1 A/g),better rate capability and cycling stability than either a-MoO_(2)or c-MoO_(2)counterpart.Combining in-situ X-ray diffraction(XRD) and ex-situ X-ray photoelectron spectroscopy(XPS)techniques,the ’adsorption-insertion-conversion’ mechanism is well established for Na+storage of MoO_(2).Our work opens new opportunities to optimize electrode materials via crystallographic engineering for efficient Na+storage,and helps to better understand the effects of homojunction structure in enhanced electrochemical performance.

A perspective on the electrocatalytic conversion of carbon dioxide to methanol with metallomacrocyclic catalysts

Electrocatalytic carb on dioxide reducti on(CO_(2)R)presents a promising route to establish zero-e mission carb on cycle and store in termittent ren ewable energy into chemical fuels for steady energy supply.Methanol is an ideal energy carrier as alternative fuels and one of the most important commodity chemicals.Nevertheless,methanol is currently mainly produced from fossil-based syngas,the production of which yields tremendous carb on emission globally.Direct CO_(2)R towards metha nol poses great potential to shift the paradigm of methanol production.In this perspective,we focus our discussions on producing methanol from electrochemical CO_(2)R,using metallomacrocyclic molecules as the model catalysts.We discuss the motivation of having methanol as the sole CO_(2)R product,the documented application of metallomacrocyclic catalysts for CO_(2)R,and recent advance in catalyzing CO_(2) to methanol with cobalt phthalocyanine-based catalysts.We attempt to understand the key factors in determining the activity,selectivity,and stability of electrocatalytic CO_(2)-to-methanol conversion,and to draw mechanistic insights from existing observations.Finally,we identify the challenges hindering methanol electrosynthesis directly from CO_(2) and some intriguing directions worthy of further investigation and exploration.

Iron(Fe,Ni,Co)-based transition metal compounds for lithium-sulfur batteries:Mechanism,progress and prospects

Lithium-sulfur batteries(LSBs)have a high theoretical capacity,which is considered as one of the most promising high-energy-density secondary batteries due to the double electrons reaction of sulfur.However,the shuttle effects of lithium polysulfides(Li PSs)and sluggish redox kinetics lead to their materials capacity loss and cycle stability deterioration,which restrains LSBs commercialization.Metallic compounds as additions can improve the electrochemical performance of the Li-S system,through the trap of Li PSs and accelerate the conversion of the soluble Li PSs.Among of them,the iron group elements(Fe,Ni,Co)-based compounds are the promising materials for the LSBs,due to their unique outer electronic structure and its tunable properties,low cost,abundant in the earth,environmental benignity,controllable and scalable prepared,and so on.In this review,we have made a summary for iron-based compounds to capture Li PSs according to lithium bond,sulfur bond and magnetic force.The type of iron-based compound including oxides,sulfides,nitrides,phosphides,carbides,and so on,and we have investigated the electrocatalytic mechanism of these materials.Besides,some improvement strategies are proposed,such as the engineering of the special micro/nanostructure,defect concentrations,band structures,and heterostructures.We hope to shed an in-depth light on the rationally design and fabrication of robust,commercial and stable materials for high-performance LSBs.

A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries

Estimating battery degradation is vital not only to monitor battery’s state-of-health but also to accelerate research on new battery chemistries. Herein, we present a data-driven approach to forecast the capacity fading trajectory of lab-assembled lithium batteries. Features with physical meanings in addition to predictive abilities are extracted from discharge voltage curves, enabling online prediction for a single cell with only its historical data. The robustness and generalizability allow for the demonstration on a compromised quality dataset consisting of batteries varying in battery architectures and cycling conditions,with superior accuracy for end of life and degradation trajectory prediction with average errors of 8.2%and 2.8%, respectively. Apart from the impressive prediction accuracy, the as-extracted features also provide physical insights, the incorporation of which into material design or battery operation conditions further enlightens the development of better batteries. We highlight the effectiveness of time-seriesbased techniques in forecasting battery cycling performance, as well as the huge potential of datadriven methods in unveiling hidden correlations in complicated energy chemistries such as lithium metal batteries.

Molecular genetic analysis of phosphomannomutase genes in Triticum monococcum

In higher plants, phosphomannomutase(PMM) is essential for synthesizing the antioxidant ascorbic acid through the Smirnoff–Wheeler pathway. Previously, we characterized six PMM genes(Ta PMM-A1, A2, B1, B2, D1 and D2) in common wheat(Triticum aestivum, AABBDD).Here, we report a molecular genetic analysis of PMM genes in Triticum monococcum(AmAm), a diploid wheat species whose Amgenome is closely related to the A genome of common wheat. Two distinct PMM genes, Tm PMM-1 and Tm PMM-2, were found in T. monococcum. The coding region of Tm PMM-1 was intact and highly conserved. In contrast, two main Tm PMM-2 alleles were identified, with Tm PMM-2a possessing an intact coding sequence and Tm PMM-2b being a pseudogene. The transcript level of Tm PMM-2a was much higher than that of Tm PMM-2b, and a bacterially expressed Tm PMM-2a recombinant protein displayed relatively high PMM activity. In general, the total transcript level of PMM was substantially higher in accessions carrying Tm PMM-1 and Tm PMM-2a than those harboring Tm PMM-1 and Tm PMM-2b. However, total PMM protein and activity levels did not differ drastically between the two genotypes. This work provides new information on PMM genes in T. monococcum and expands our understanding on Triticeae PMM genes, which may aid further functional and applied studies of PMM in crop plants.

Conceptual framework of modern Zangxiang concept: a qualitative content analysis

Background:Some scholars believe modern Zangxiang concept is conceived under the thought of bio-medicine (Western medicine),which differs greatly compared to the ancient one.The view put forward stirs up great argument.Therefore,we attempt to explore whether the modern Zangxiang concept can fully reflect the specific content and structural relationship of ancient one through analyzing the frame structure of modern Zangxiang concept.Methods:The qualitative content analysis was used.The document was selected from Zangxiang Chapter in Fundamental Theory of Traditional Chinese Medicine (7th edition).The textual document was added to QSR International's NVivo qualitative data analysis software.Text was manually coded line by line according to the content described,and new categories were identified which were further divided into initial code,sub-theme,and theme.The relationships among them were explored,and a visual map of the body of knowledge (BoK) in Zangxiang theory was depicted.The similarities and differences between modern and ancient Zangxiang concepts were discussed by interpreting these themes,relationships and the visual map.Results:After analysis,there were 126 initial codes,13 sub-themes,and 4 themes including structure,function,functional outcome and the corresponding external sign.Two principal relationships,generation and mutual promotion,restriction,and inter-dependence in function were obtained.By analyzing four themes and two kinds of principal relationships,we found five different aspects between modern Zangxiang concept and ancient Zangxiang theory.Conclusion:The BoK of the modern Zangxiang concept contains four themes:structure,function,functional outcome,and the corresponding external sign.The principal relationship is referring to generation between structure and function,and between function and its corresponding outcomes.Great differences exist when talking about modern Zangxiang concept which doesn't reflect TCM characteristics and is more inclined to bio-medicine in structure from the perspective of ancient TCM theory.

A self-adaptive,data-driven method to predict the cycling life of lithium-ion batteries

Accurately forecasting the nonlinear degradation of lithium-ion batteries(LIBs)using early-cycle data can obviously shorten the battery test time,which accelerates battery optimization and production.In this work,a self-adaptive long short-term memory(SA-LSTM)method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data.Specifically,two features were extracted from discharge voltage curves by a time-series-based approach and forecasted to further cycles using SA-LSTM model.The as-obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model.The proposed method achieved an average online prediction error of 6.00%and 6.74%for discharge capacity and end of life,respectively,when using the early-cycle discharge information until 90%capacity retention.Fur-thermore,the importance of temperature control was highlighted by correlat-ing the features with the average temperature in each cycle.This work develops a self-adaptive data-driven method to accurately predict the cycling life of LIBs,and unveils the underlying degradation mechanism and the impor-tance of controlling environmental temperature.

Prediction and interpretation of photocatalytic NO removal on g-C_(3)N_(4)-based catalysts using machine learning

Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems.Herein,a dataset of g-C_(3) N_(4)-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning(ML)model was proposed to predict the NO removal rate.The result shows that the Gradient Boosting Decision Tree(GBDT)demonstrated the greatest prediction accuracy with R 2 of 0.999 and 0.907 on the training and test data,respectively.The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate,in the order of importance,were catalyst characteristics>reaction process>preparation conditions.Moreover,the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features(e.g.,doping ratio,flow rate,and pore volume)to the model output outcomes.This ML approach presents a pure data-driven,interpretable framework,which provides new insights into the influence of catalyst characteristics,reaction process,and preparation conditions on NO removal.

Pregnancy Complicated With a Giant Pancreatic Tumor and Decompensation of Liver Cirrhosis:A Case Report and Literature Review

Pregnancy with solid pseudopapillary tumor of the pancreas(SPTP)is rare.Because pregnancy hormones may cause tumor progression,the management and treatment of SPTP need to balance the safety of pregnant women and fetuses with surgical treatment.We reported a case of a giant pancreatic tumor diagnosed during pregnancy that was considered to be SPTP.Examinations also showed hepatitis B virus infection and severe decompensation of liver cirrhosis.Medical termination of pregnancy was performed.The patient has lived with the tumor until now without surgery.We retrieved the published case reports,summarized the clinical characteristics of pregnancy with SPTP,and explored its management during the perinatal period.Most patients with SPTP have a good prognosis with good maternal and fetal outcomes,and it is important to choose an appropriate treatment method and timing.However,pregnancy combined with decompensated liver cirrhosis needs to be terminated in a timely manner because of its high-risk status.

On the cationic nature of lithium polysulfide in lithium-sulfur batteries

Lithium-sulfur(Li-S) battery, with an ultra-high theoretical energy density of 2600 Wh/kg, is inevitably deemed as one of the most promising alternatives for the state-of-the-art lithium-ion(Li-ion) batteries [1]. As a typical conversion-type of batteries, Li-S batteries comply multi-electron transfer and multiphase transformation between elemental S and lithium sulfide(Li_(2)S) during charging/discharging, accompanied by a series of dissolved LiPSs with a general formula of Li_(2)Sn(n=4, 6 or 8) [2].

All-dielectric terahertz metasurface for linearly-polarized multichannel transmission and superposition states of spherical and vortex waves

Compared to pure vortex waves,the superposition state of spherical waves and vortex waves has enough degrees of freedom to upgrade applications in particle manipulation,information encryption,and large-capacity communications.Here,we propose a new scheme to achieve superposition states and multichannel transmission of vortex and spherical waves.Two transmissive all-silicon metasurfaces that enable mutual interference between linearly polarized(LP)waves in the terahertz region are demonstrated.Type A can achieve interference between x and y polarized waves,while type B can achieve interference between x(or y)and x(or y)polarized waves.The multichannel transmission and superposition states of topological charges of+3,+2,and+4 are designed and demonstrated from theoretical,simulative,and experimental perspectives at 1.1 THz.In addition,the objective fact that the focused superposition state must be observed close to the focal plane is also revealed.The measured results are in good agreement with the theoretical and simulative results.This work provides an idea for the design of ultrathin terahertz devices and could be applied in the fields of information encryption and highfrequency communications.

Eye movement evidence in investigative identification based on experiments

Investigative identification is a routine criminal investigative procedure,the results of which can be used as evidence in litigation.However,some suspects often deny their involvement in the case,and some witnesses may withhold information or misrepresent it,all of which may lead to a miscarriage of justice.This study created a stressful environment and conducted a simulated crime experiment to explore whether eye movement data can be an effective feature for distinguishing perpetrators,innocents,and insiders.The eye movement features—such as the total fixation duration,number of fixations,and first fixation duration—within an area of interest were collected from 83 participants sorted into informed,involved,and innocent groups.The results revealed the following:(1)compared with the object and scene stimuli,subjects with different identities were more likely to exhibit significant differences in eye movement data for the involved and irrelevant portraits.The total fixation duration and the number of fixations can provide a reference for judging whether someone is involved in a case,and the first fixation duration effect was not obvious.(2)Using machine learning algorithms to predict subjects’identities through eye movement features,it was demonstrated that the involved portrait-object-scene model had the best predictive effect.(3)Multiple algorithmic models were used to distinguish subjects’identities,and the highest accuracy of 92.7%was achieved for the informed×innocent group,88%for the innocent×suspect group(including the informed and involved groups),and 84.5%for the involved group.The eye movement analysis method can provide a reference for criminal investigators to distinguish between the perpetrator,insider,and innocent,and offer a novel approach to determining the direction of further investigation and uncovering and verifying case clues.

Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures

Ionic liquids(ILs),because of the advantages of low volatility,good thermal stability,high gas solubility and easy recovery,can be regarded as the green substitute for traditional solvent.However,the high viscosity and synthesis cost limits their application,the hybrid solvent which combining ILs together with others especially water can solve this problem.Compared with the pure IL systems,the study of the ILs-H_(2)O binary system is rare,and the experimental data of corresponding thermodynamic properties(such as density,heat capacity,etc.)are less.Moreover,it is also difficult to obtain all the data through experiments.Therefore,this work establishes a predicted model on ILs-water binary systems based on the group contribution(GC)method.Three different machine learning algorithms(ANN,XGBoost,LightBGM)are applied to fit the density and heat capacity of ILs-water binary systems.And then the three models are compared by two index of MAE and R^(2).The results show that the ANN-GC model has the best prediction effect on the density and heat capacity of ionic liquid-water mixed system.Furthermore,the Shapley additive explanations(SHAP)method is harnessed to scrutinize the significance of each structure and parameter within the ANN-GC model in relation to prediction outcomes.The results reveal that system components(XIL)within the ILs-H_(2)O binary system exert the most substantial influence on density,while for the heat capacity,the substituents on the cation exhibit the greatest impact.This study not only introduces a robust prediction model for the density and heat capacity properties of IL-H_(2)O binary mixtures but also provides insight into the influence of mixture features on its density and heat capacity.

MicroRNA-21 in the pathogenesis of acute kidney injury

Acute kidney injury(AKI),associated with significant mor-bidity and mortality,is widely known to involve epithelial apoptosis,excessive inflammation,and fibrosis in re-sponse to ischemia or reperfusion injury,which results in either chronic pathological changes or death.Therefore,it is imperative that investigations are conducted in order to fi nd effective,early diagnoses,and therapeutic targets needed to help prevent and treat AKI.However,the mech-anisms modulating the pathogenesis of AKI still remain largely undetermined.MicroRNAs(miRNAs),small non-coding RNA molecules,play an important role in several fundamental biological and pathological processes by a post transcriptional regulatory function of gene expres-sion.MicroRNA-21(miR-21)is a recently identifi ed,typi-cal miRNA that is functional as a regulator known to be involved in apoptosis as well as inflammatory and fi brotic signaling pathways in AKI.As a result,miR-21 is now considered a novel biomarker when diagnosing and treat-ing AKI.This article reviews the correlative literature and research progress regarding the roles of miR-21 in AKI.

锂硫二次电池电催化

随着社会发展对储能系统的要求不断提高,锂硫二次电池因其低成本、高能量密度和环境友好等优点,近年来受到广泛关注.但是多硫化物的穿梭效应和缓慢的氧化还原过程限制了硫正极的活性物质利用率,同时导致其快速的容量衰减,阻碍了其实际应用.为了解决这些问题,研究者采用“锂硫电催化”模型体系以缓解穿梭效应并促进多硫化物的转化动力学.本文系统总结了包括非均相与均相催化体系在内的锂硫电池电催化材料和模型体系的设计原理及最新研究进展,展望了未来锂硫二次电池电催化研究的主要挑战和发展机遇.

Pepper variome reveals the history and key loci associated with fruit domestication and diversification

Pepper(Capsicum spp.)is an important vegetable crop that provides a unique pungent sensation when eaten.Through construction of a pepper variome map,we examined the main groups that emerged during domestication and breeding of C.annuum,their relationships and temporal succession,and the molecular events underlying the main transitions.The results showed that the initial differentiation in fruit shape and pungency,increase in fruit weight,and transition from erect to pendent fruits,as well as the recent appearance of large,blocky,sweet fruits(bell peppers),were accompanied by strong selection/fixation of key alleles and introgressions in two large genomic regions.Furthermore,we identified Up,which encodes a BIG GRAIN protein involved in auxin transport,as a key domestication gene that controls erect vs pendent fruit orientation.The up mutation gained increased expression especially in the fruit pedicel through a 579-bp sequence deletion in its 5′upstream region,resulting in the phenotype of pendent fruit.The function of Up was confirmed by virus-induced gene silencing.Taken together,these findings constitute a cornerstone for understanding the domestication and differentiation of a key horticultural crop.