Activity Descriptors Research Articles

Catalytic oxidation of benzyl alcohol over MnO2: Structure-activity description and reaction mechanism

Activity evaluation of metal oxide catalysts with different crystal structures has been an important prerequisite for industrial applications. This work systematically elucidates the structure-activity relationship of α-, β-, γ- and δ-MnO2 catalysts toward the selective oxidation of benzyl alcohol (BA) to benzaldehyde (BAD). Exposed surfaces of MnO2 are evaluated by a combined theoretical and experimental investigation. On this basis, a catalytic activity descriptor is established considering the surface energies, formation energies of oxygen vacancies (OVs), O2 adsorption energies, and areal densities of OVs and unsaturated Mn. Inspired by this, we fabricate β-MnO2 with a high surface area and it delivers the highest BA conversion among all MnO2. Furthermore, a developed Mars-van Krevelen (MvK) mechanism is proposed to describe the BA oxidation process on the β-MnO2 (110) surface. The research paradigm opens a door to the rational design of metal oxide catalysts.

Accelerating the screening and development of drugs for the treatment of breast cancer

Breast cancer is one of the most lethal cancers, estrogen receptor α Subtype (ERα) is an important target. The compounds that able to fight ERα active may be candidates for treatment of breast cancer. The drug discovery process is a very large and complex process that often requires one selected from a large number of compounds. This paper considers the independence, coupling, and relevance of bioactivity descriptors, selects the 15 most potentially valuable bioactivity descriptors from 729 bioactivity descriptors. An optimized back propagation neural network is used for ERα, The pharmacokinetics and safety of 15 selected bioactivity descriptors were verified by gradient lifting algorithm. The results showed that these 15 biological activity descriptors could not only fit well with the nonlinear relationship of ERα activity can also accurately predict its pharmacokinetic characteristics and safety, with an average accuracy of 89.92~94.80%. Therefore, these biological activity descriptors have great medical research value.

The Effect of Composition, Pre-Treatment on the Mechanical and Acoustic Properties of Apple Gels and Freeze-Dried Materials.

This study aimed to determine the effect of the addition of apple juice concentrate (AJC) on the properties of agar gel and dried materials. Agar gels with the addition of apple juice concentrate in the range of 5–20% were prepared with or without the addition of maltodextrin. The gels were also soaked in the solution of AJC. The water content, water activity, densities, some mechanical and acoustic descriptors of gels, and the freeze-dried gels were analysed. The porosity and shrinkage of dried products were also investigated. The addition of AJC significantly changed mechanical and acoustic properties of gels. The hardness of gels decreased with a higher addition of concentrate. Dried samples with a lower concentration of sugars (the lower addition of AJC) were characterised by lower shrinkage and higher porosity, as well as crispness and glass transition temperature. The investigated mechanical and acoustic properties of dried gels showed the addition of apple concentrate at the level of 5% to agar solution was optimal.

Electrocatalysis in Alkaline Media and Alkaline Membrane-Based Energy Technologies.

Hydrogen energy-based electrochemical energy conversion technologies offer the promise of enabling a transition of the global energy landscape from fossil fuels to renewable energy. Here, we present a comprehensive review of the fundamentals of electrocatalysis in alkaline media and applications in alkaline-based energy technologies, particularly alkaline fuel cells and water electrolyzers. Anion exchange (alkaline) membrane fuel cells (AEMFCs) enable the use of nonprecious electrocatalysts for the sluggish oxygen reduction reaction (ORR), relative to proton exchange membrane fuel cells (PEMFCs), which require Pt-based electrocatalysts. However, the hydrogen oxidation reaction (HOR) kinetics is significantly slower in alkaline media than in acidic media. Understanding these phenomena requires applying theoretical and experimental methods to unravel molecular-level thermodynamics and kinetics of hydrogen and oxygen electrocatalysis and, particularly, the proton-coupled electron transfer (PCET) process that takes place in a proton-deficient alkaline media. Extensive electrochemical and spectroscopic studies, on single-crystal Pt and metal oxides, have contributed to the development of activity descriptors, as well as the identification of the nature of active sites, and the rate-determining steps of the HOR and ORR. Among these, the structure and reactivity of interfacial water serve as key potential and pH-dependent kinetic factors that are helping elucidate the origins of the HOR and ORR activity differences in acids and bases. Additionally, deliberately modulating and controlling catalyst-support interactions have provided valuable insights for enhancing catalyst accessibility and durability during operation. The design and synthesis of highly conductive and durable alkaline membranes/ionomers have enabled AEMFCs to reach initial performance metrics equal to or higher than those of PEMFCs. We emphasize the importance of using membrane electrode assemblies (MEAs) to integrate the often separately pursued/optimized electrocatalyst/support and membranes/ionomer components. Operando/in situ methods, at multiscales, and ab initio simulations provide a mechanistic understanding of electron, ion, and mass transport at catalyst/ionomer/membrane interfaces and the necessary guidance to achieve fuel cell operation in air over thousands of hours. We hope that this Review will serve as a roadmap for advancing the scientific understanding of the fundamental factors governing electrochemical energy conversion in alkaline media with the ultimate goal of achieving ultralow Pt or precious-metal-free high-performance and durable alkaline fuel cells and related technologies.

Understanding slip activity and void initiation in metals using machine learning-based microscopy analysis

The use of machine learning techniques to supplement traditional data analysis in mechanics and materials research can improve the understanding of microstructure-property relationships. Identification of key microstructural features or correlation between deformation mechanisms and material response can be discerned that might otherwise have been overlooked. Motivated by the possibilities of gaining additional insight into the process of void nucleation in polycrystalline metals, several machine learning techniques are applied to the analysis of mesoscopic deformation mechanisms as determined by experimental characterization and modeling. Results from crystal plasticity modeling, experimental microstructural analysis, and theoretical models of slip transmission are combined to test a hypothesis regarding fatigue-induced void nucleation. Unsupervised spectral clustering was used with results from crystal plasticity simulations to characterize slip system activity for different crystallographic orientations. The slip system activity as determined by the clustering analysis was then fed into a K-nearest neighbor classifier to quantify the probability of slip transmission across different grain boundaries of interest and analyze grains containing fatigue-induced voids. An unique and unanticipated result from the unsupervised clustering analysis shows that including a group of partially-active slip systems was more appropriate than using the binary classification of active/non-active. Predicted slip activity behavior in a face-centered cubic material was shown to differ significantly from that of a body-centered cubic material due to non-Schmid effects. The outcome of the overall analysis was that grains containing fatigue-induced voids were more likely to be surrounded by grains with orientations that inhibited slip transmission according the Lee-Robertson-Birnbaum (LRB) criteria. Finally, it is demonstrated that smaller datasets using limited simulation results were equally effective at predicting a similar outcome when additional physical descriptors for the slip system activity are used.

Selective hydrogenation of 1,3-butadiene on iridium nanostructures: Structure sensitivity, host effect, and deactivation mechanism

A systematic study on the structure sensitivity, host effect, and the deactivation mechanism of Ir-catalyzed selective hydrogenation of 1,3-butadiene, a key process in the purification of alkadiene for the upgrading of C4 cut, is presented by coupling steady-state catalytic testing, in-depth characterization, kinetic evaluation, and density functional theory calculations. We reveal that: (i) 1,3-Butadiene hydrogenation on iridium is structure-sensitive with the optimal particle size of about 2 nm, and the H2 dissociation energy is a reliable activity descriptor; (ii) The nature of the NC hosts exerts a critical impact on the catalytic performance, and balanced nitrogen content and speciation seem key for the optimized performance; and (iii) Different deactivation mechanisms occur: fouling by coke deposition on the catalysts with a high N:C ratio (>1), and site blockage due to the competitive adsorption between 1-butene/cis-2-butene and 1,3-butadiene. These molecular insights provide valuable guidelines for the catalyst design in selective hydrogenations.

Electronic Structure-Based Descriptors for Oxide Properties and Functions

ConspectusThe transition from fossil fuels to renewable energy requires the development of efficient and cost-effective energy storage technologies. A promising way forward is to harness the energy of intermittent renewable sources, such as solar and wind, to perform (electro)catalytic reactions to generate fuels, thus storing energy in the form of chemical bonds. However, current catalysts rely on the use of expensive, rare, or geographically localized elements, such as platinum. Widespread adoption of new (electro)catalytic technologies hinges on the discovery and development of materials containing earth-abundant elements, which can efficiently catalyze an array of (electro)chemical reactions.In the context of catalysis, descriptors provide correlations between fundamental physical properties, such as the electronic structure, and the resulting catalytic activity. The use of easily accessible descriptors has proven to be a powerful method to advance and accelerate discovery and design of new catalyst materials. The position of the oxygen electronic 2p band center has been proposed to capture the basic physical properties of oxides, including oxygen vacancy formation energy, diffusion barrier of oxygen ions, and work function. Moreover, the adsorption strength of relevant reaction intermediates at the surface of oxides can be strongly correlated with the energy of the oxygen 2p states, which affects the catalytic activity of reactions, such as oxygen electrocatalysis, and oxidative dehydrogenation of organic molecules. Such descriptors for catalytic activity can be used to predict the activity of new catalysts and understand trends and behavior among different catalysts.In this Account, we discuss how the energy of the oxygen 2p states can be used as a descriptor for oxide bulk and surface chemical properties. We show how the oxide redox properties vary linearly with the position of the oxygen 2p band center with respect to the Fermi level, and we discuss how this descriptor can be expanded across different materials and structural families, including possible generalizations to compounds outside oxides. We highlight the power of the oxygen 2p band center to predict the catalytic activity of oxides. We conclude with an outlook examining under which conditions this descriptor can be applied to predict oxide properties and possible opportunities for further refining and accelerating property predictions of oxides by leveraging material databases and machine learning.

A Statistical Study on Anti-Breast Cancer Drug Screening

Breast cancer is one of the most lethal cancers, estrogen receptor α Subtype (ERα) is an important target. The compounds that able to fight ERα active may be candidates for treatment of breast cancer. The drug discovery process is a very large and complex process that often requires one selected from a large number of compounds. This paper considers the independence, coupling, and relevance of bioactivity descriptors, selects the 15 most potentially valuable bioactivity descriptors from 729 bioactivity descriptors. An optimized back propagation neural network is used for ERα, the pharmacokinetics and safety of 15 selected bioactivity descriptors were verified by gradient lifting algorithm. The results showed that these 15 biological activity descriptors could not only fit well with the nonlinear relationship of ERα activity can also accurately predict its pharmacokinetic characteristics and safety, with an average accuracy of 89.92~94.80%. Therefore, these biological activity descriptors have great medical research value.

Tuning the Metal Electronic Structure of Anchored Cobalt Phthalocyanine via Dual‐Regulator for Efficient CO2 Electroreduction and Zn–CO2 Batteries

AbstractHeterogeneous macromolecule catalysts have been known as efficient electrocatalysts for CO2 reduction reaction, however, manipulating the activity of heterogeneous molecules via controllable metal electronic structure is still challenging. Herein, different CO2 activated 3D, robust, nitrogen‐doped hollow carbon spheres are synthesized to anchor cobalt phthalocyanine as molecularly dispersed electrocatalysts, where the electron‐withdrawing coeffect of carbon defects and heteroatom N is responsible for tuning the electronic structure of metal center. The optimal electrocatalyst reveals high CO faradaic efficiency (FECO) of 95.68%, turnover frequency of 13.80 s−1, and current density of 16.49 mA cm−2 at an overpotential of 760 mV. The control experiment and DFT calculations unveil that the significant activity is mainly ascribed to the optimal electron‐withdrawing coeffect of carbon defects and pyrrolic N, which reduce the electron density of Co center to facilitate CO2 activated to form *COOH intermediate on Co(I) active sites during electrocatalysis. The 2p‐charge loss of Co is summarized as an activity descriptor, which steers the current density and production rate toward CO. Furthermore, the design strategy can universally fabricate the hybrid MPc catalyst with transitional metal (Ni, Fe) site while a rechargeable Zn–CO2 battery is devised to deliver a maximal power density of 1.02 mW cm−2.

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

AbstractMachine learning (ML) is emerging as a powerful tool for identifying quantitative structure–activity relationships to accelerate electrocatalyst design by learning from historic data without explicit programming. The algorithms, data/database, and descriptors are usually the decisive factors for ML and the descriptors play a pivotal role for electrocatalysis as they contain the essence of catalysis from the physicochemical nature. Despite the considerable research efforts regarding electrocatalyst design with ML, the lack of universal selection tactics for descriptors bridging the gap between structures and activity impedes its wider application. A timely summary of the application of ML in electrocatalyst design helps to deepen the understanding of the nature of descriptors and improve the application scope and design efficiency. This review summarizes the geometrical, electronic, and activity descriptors used as input for ML training and predicting to reveal the general rules for their application in the design of electrocatalysts. In response to the challenges of hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, CO2 reduction reaction, and nitrogen reduction reaction, the ML application in these areas is tracked for the progress and prospective changes. Additionally, the potential application of the automated design and discovery are discussed for the other well‐known electrocatalytic processes.

MAPO-18 Catalysts for the Methanol to Olefins Process: Influence of Catalyst Acidity in a High-Pressure Syngas (CO and H2) Environment.

The transition from integrated petrochemical complexes toward decentralized chemical plants utilizing distributed feedstocks calls for simpler downstream unit operations. Less separation steps are attractive for future scenarios and provide an opportunity to design the next-generation catalysts, which function efficiently with effluent reactant mixtures. The methanol to olefins (MTO) reaction constitutes the second step in the conversion of CO2, CO, and H2 to light olefins. We present a series of isomorphically substituted zeotype catalysts with the AEI topology (MAPO-18s, M = Si, Mg, Co, or Zn) and demonstrate the superior performance of the M(II)-substituted MAPO-18s in the conversion of MTO when tested at 350 °C and 20 bar with reactive feed mixtures consisting of CH3OH/CO/CO2/H2. Co-feeding high pressure H2 with methanol improved the catalyst activity over time, but simultaneously led to the hydrogenation of olefins (olefin/paraffin ratio < 0.5). Co-feeding H2/CO/CO2/N2 mixtures with methanol revealed an important, hitherto undisclosed effect of CO in hindering the hydrogenation of olefins over the Brønsted acid sites (BAS). This effect was confirmed by dedicated ethene hydrogenation studies in the absence and presence of CO co-feed. Assisted by spectroscopic investigations, we ascribe the favorable performance of M(II)APO-18 under co-feed conditions to the importance of the M(II) heteroatom in altering the polarity of the M–O bond, leading to stronger BAS. Comparing SAPO-18 and MgAPO-18 with BAS concentrations ranging between 0.2 and 0.4 mmol/gcat, the strength of the acidic site and not the density was found to be the main activity descriptor. MgAPO-18 yielded the highest activity and stability upon syngas co-feeding with methanol, demonstrating its potential to be a next-generation MTO catalyst.

One-step synergistic optimization of hierarchical pore topology and nitrogen dopants in activated coke for efficient catalytic oxidation of nitric oxide

Carbon-catalyzed oxidation represents a promising strategy for nitrogen oxide emission control. Here, we report a new strategy to simultaneously optimize the pore topology and doping environment of activated coke (AC) catalyst through one-step catalytic activation process with nitrogen resource in-situ participation. Dynamic experiments of nitric oxide catalytic oxidation using the prepared activated cokes with controllable pore configurations and doping environment firstly demonstrate that hierarchical pore configuration and nitrogen functional groups can synergistically improve catalytic oxidation activity. Especially, nitrogen-doped hierarchical porous AC catalyst exhibits the highest conversion efficiency (above 70%). Based on density functional theory calculations in combination with control experiments using pyridinic nitrogen and quaternary nitrogen-dominant catalysts, a new activity descriptor of electrophilic Fukui function is proposed and quaternary nitrogen is thereby identified to be the optimal nitrogen doping configuration towards nitric oxide catalytic oxidation. This work provides new insights into the synergistic role of pore topology and nitrogen doping in nitric oxide low-temperature oxidation, giving guidance for designing high-performance carbocatalyst.

Can the local electric field be a descriptor of catalytic activity? A case study on chorismate mutase.

The current theoretical perception of enzymatic activity is highly reliant on the determination of the activation energy of the reactions, which is often calculated using computationally demanding quantum mechanical calculations. With the ever-increasing use of bioengineering techniques that produce too many variants of the same enzyme, a fast and accurate way to study the relative efficiency of enzymes is currently in high demand. Here, we propose the local electric field (LEF) of the enzyme along the reaction axis as a descriptor for the enzymatic activity using the example of chorismate mutase in its native form and several variants (R90A, R90G, and R90K/C88S). The study shows a direct correlation between the calculated enzymatic EF and the enzymatic activity for all the complexes. MD simulations of the Michaelis complex and the transition state analog (TSA) show a stabilizing force on the TSA due to the enzymatic EF. QM/MM and QM-only DFT calculations in the presence of an external electric field (EEF) oriented along the reaction axis show that the electric field can interact with the dipole moment of the TS, thereby stabilizing it and thus lowering the activation energy.

Trends and descriptors of heterogeneous hydroformylation activity and selectivity of RhM3 (M = Fe, Co, Ni, Cu and Zn) catalysts

The origin of superior C3 oxygenate selectivity of RhCo3 catalysts during hydroformylation was studied, which provided the design principles for catalyst improvements.

Screening of transition metal single-atom catalysts supported by a WS2 monolayer for electrocatalytic nitrogen reduction reaction: insights from activity trend and descriptor.

The electrocatalytic nitrogen reduction reaction (NRR), as an alternative green technology to the Haber-Bosch process, can efficiently synthesize ammonia under ambient conditions and has a reduced carbon footprint. Here we systematically investigate the NRR activity and selectivity of transition metal (TM) single-atom catalyst (SAC) anchored WS2 monolayers (TM@WS2) by means of first-principles calculations and microkinetic modeling. The construction of the reaction activity trend and the identification of an activity descriptor, namely *N2H adsorption energy, facilitate the efficient screening and rational design of SACs with high activity. Manipulating the adsorption strength of the pivotal *N2H intermediate is a potential strategy for enhancing NRR activity. Utilizing the limiting potential difference of NRR and the hydrogen evolution reaction (HER) as a selectivity descriptor, we screen three SACs with excellent activity and selectivity toward NRR, i.e., Re@WS2, Os@WS2 and Ir@WS2 with favorable limiting potentials of -0.44 V, -0.38 V and -0.69 V. By using the explicit H9O4+ model, the kinetic barriers of the rate-determining steps (0.47 eV-1.15 eV) of the solvated proton transfer on the screened SACs are found to be moderate, indicative of a kinetically feasible process. Microkinetic modeling shows that the turnover frequencies of N2 reduction to NH3 on Re@WS2, Os@WS2 and Ir@WS2 are 1.52 × 105, 8.21 × 102 and 4.17 × 10-4 per s per site at 400 K, achieving fast reaction rates. The coexistence of empty and occupied 5d orbitals of candidate SACs is beneficial for σ donation and π* backdonation, endowing them with extraordinary N2 adsorption and activation. Moreover, the screened SACs possess good dispersity and thermodynamic stability. Our work provides a promising solution for the efficient screening and rational design of high-performance electrocatalysts toward the NRR.

Role of heteroatom-doping in enhancing catalytic activities and the stability of single-atom catalysts for oxygen reduction and oxygen evolution reactions

Single-atom catalysts (SACs) are promising as efficient electrocatalysts for clean energy technologies such as fuel cells, water splitting, and metal-air batteries. Still, the unsatisfactory loading density and stability of the catalytic active centers limit their applications. Herein, a doping strategy is explored to achieve highly efficient and stable SACs for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). The stability, electronic structures, and ORR/OER overpotentials of S-doped transition metal-nitrogen-carbon SAC structures were investigated using first-principles calculation methods. An intrinsic descriptor linking the intrinsic properties of catalysts and the catalytic activity was established for screening the best SACs. The theoretical predictions are well consistent with the experimental results, which provide a theoretical basis for understanding the catalytic mechanism and an approach for the rational design of SACs for clean energy conversion and storage.

Rational design of ZSM-5 zeolite containing a high concentration of single Fe sites capable of catalyzing the partial oxidation of methane with high turnover frequency

We successfully synthesized a Fe/ZSM-5 catalyst enabling conversion of methane to C1 oxygenates in record yields, and demonstrated that the fraction of the single Fe cation, as well as the Al distribution, are the powerful activity descriptors.

Anti-breast cancer drug screening based on Neural Networks and QSAR model

Breast cancer is one of the most lethal cancers, estrogen receptor α Subtype (ERα) is an important target. The compounds that able to fight ERα active may be candidates for treatment of breast cancer. The drug discovery process is a very large and complex process that often requires one selected from a large number of compounds. This paper considers the independence, coupling, and relevance of bioactivity descriptors, selects the 15 most potentially valuable bioactivity descriptors from 729 bioactivity descriptors. An optimized back propagation neural network is used for ERα, The pharmacokinetics and safety of 15 selected bioactivity descriptors were verified by gradient lifting algorithm. The results showed that these 15 biological activity descriptors could not only fit well with the nonlinear relationship of ERα activity can also accurately predict its pharmacokinetic characteristics and safety, with an average accuracy of 89.92~94.80%. Therefore, these biological activity descriptors have great medical research value.

Designing Pt-based subsurface alloy catalysts for the dehydrogenation of perhydro-dibenzyltoluene: A first-principles study

Designing effective dehydrogenation catalysts for liquid organic hydrogen carriers is essential to the release and transport of hydrogen. The hydrogen release of perhydro-dibenzyltoluene using Pt-based subsurface alloys (Pt/M/Pt(111), where M = Pd, Cu, or Ni) and the effect of the applied biaxial strain on dehydrogenation performance of Pt/M/Pt(111) were systematically investigated. The doping of M atoms onto Pt/M/Pt(111) and strained Pt/M/Pt(111) could effectively tune the electronic properties of the Pt atoms, thus eventually affecting the hydrogen adsorption strength. The rate-determining step (RDS) of the dehydrogenation process on surfaces of Pt(111), Pt/M/Pt(111), strained Pt(111), and strained Pt/M/Pt(111) was identical: the first step of dehydrogenation in the middle ring of perhydro-dibenzyltoluene. The doping of the M atoms and application of tensile strain promoted dehydrogenation. Furthermore, it was revealed that d-band centers and reaction energies of the RDS correlated with the hydrogen adsorption energy, suggesting that hydrogen adsorption strength is a practical descriptor of dehydrogenation activity.

Utilizing the charge-transfer model to design promising electrocatalysts

Utilizing renewable electricity resources to produce various C/O/N/H-containing chemicals is an extremely promising energy conversion pathway. However, huge amounts of electricity inputs are always necessary for these electrochemical synthetic processes. Therefore, developing efficient electrocatalyst candidates is the key, where the charge-transfer model plays a vital role in the rational design of electrocatalysts. During the past decade, three main achievements have been attained in electrochemical catalysis: (i) Numerous new materials with unique physicochemical properties have been explored, (ii) The systems of activity descriptors and design principles have been successfully established, and (iii) The insights into the underlying catalysis mechanisms have deepened. The charge-transfer model is crucial for these strides and achievements. This short review will systematically discuss the charge-transfer model, including its origin, description, correlation with material properties, acquisition, and representative applications in electrochemical fields. We also make a brief conclusion and outlook for the charge-transfer model in the development of electrochemistry.