Pressure-Swing Adsorption Using Layered Adsorbent Beds with Different Adsorption Properties: II—Experimental Investigation

An experimental study was conducted on a layered-bed pressure-vacuum-swing adsorption, PVSA, process with adsorbents that differ in their adsorption properties. An oxygen, O₂, PVSA process was employed as an example for investigating how the process performance is affected by bed-layering configuration under different operating conditions for specific purge, product purity, and cycle feature. For two adsorbents with similar nitrogen-to-oxygen, N₂/O₂, selectivity but different N₂ and O₂ capacities, placing the high-capacity adsorbent at the product end and the low-capacity adsorbent at the feed end of the adsorption bed results in a better performance than in the case of reversing the layer positions of those adsorbents. The benefit of placing the adsorbent with higher capacity at the product end becomes more significant at high O₂ product-purity levels. The experimental data obtained in this investigation agree well with simulation results reported earlier.     

电催化CO2还原合成C2+产物的机理和材料研究进展

Over the past decades, advances in science and technology have greatly benefitted the society. However, the exploitation of fossil fuels and excessive emissions of polluting gases have disturbed the balance of the normal carbon cycle, causing serious environmental issues and energy crises. Global warming caused by heavy CO₂ emissions is driving new attempts to mitigate the increase in the concentration of atmospheric CO₂. Significant efforts have been devoted for CO₂ conversion. To date, the electroreduction of CO₂, which is highly efficient and offers a promising strategy for both storing energy and managing the global carbon balance, has attracted great attention. In addition, the electrosynthesis of value-added C₂₊ products from CO₂ addresses the need for the long-term storage of renewable energy. Therefore, developing catalysts that function under ambient conditions to produce C₂ selectively over C₁ products will increase the utility of renewable feedstocks in industrial chemistry applications. Recently, great progress has been made in the development of materials for electrocatalytic CO₂ reduction (ECR) toward C₂₊ products; however, some issues (e.g., low selectivity, low current efficiency, and poor durability) remain to be addressed. In addition, the elementary reaction mechanism of each C₂₊ product remains unclear, contributing to the blindness of catalyst design. In this regard, the development of proposed mechanisms of ECR toward C₂₊ products is summarized herein. The key to generating C₂₊ products is improving the chances of C―C coupling. Test conditions significantly influence the reaction path of the catalyst. Thus, three different paths that that are most likely to occur during ECR to C₂₊ products are proposed, including the CO, CO-COH, and CO-CO paths. In addition, typical material regulatory strategies and technical designs for ECR toward C₂₊ products (e.g. crystal facet modulation, defect engineering, size effect, confinement effects, electrolyzer design, and electrolyte pH) are introduced, focusing on their effects on the selectivity, current efficiency, and durability. The four strategies for catalyst design (crystal facet modulation, defect engineering, size effect, and confinement effect) primarily affect the selectivity of the ECR via adjustment of the adsorption of reaction intermediates. The last two strategies for technique design (electrolyzer design and electrolyte pH) contributing greatly toward improving the current efficiency than selectivity. Finally, the challenges and perspectives for ECR toward C₂₊ products and their future prospects are discussed herein. Therefore, breakthroughs in the promising field of ECR toward the generation of C₂₊ products are possible when these catalyst design strategies and mechanisms are applied and novel designs are developed.     

Mechanistic Studies on the Roles of the Oxidant and Hydrogen Bonding in Determining the Selectivity in Alkene Oxidation in the Presence of Molybdenum Catalysts

When the molybdenum oxo(peroxo) acetylide complex [CpMo(O? O)(O)C?CPh] is used as a catalyst for the oxidation of olefins, completely different product selectivity is obtained depending on the oxidant employed. When tert‐butyl hydroperoxide (TBHP, 5.5 M ) in dodecane is used as the oxidant for the oxidation of cyclohexene, cyclohexene oxide is formed with high selectivity. However, when H₂O₂ is used as the oxidant, the corresponding cis‐1,2‐diol is formed as the major product. Calculations performed by using density functional theory revealed the nature of the different competing mechanisms operating during the catalysis process and also provided an insight into the influence of the oxidant and hydrogen bonding on the catalysis process. The mechanistic investigations can therefore serve as a guide in the design of molybdenum‐based catalysts for the oxidation of olefins.     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C?H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C?H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out‐of‐sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism‐based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

基于金属氧化物材料的二氧化碳电催化还原

The CO₂ level in the atmosphere has been increasing since the industrial revolution owing to anthropogenic activities. The increased CO₂ level has led to global warming and also has detrimental effects on human beings. Reducing the CO₂ level in the atmosphere is urgent for balancing the carbon cycle. In this regard, reduction in CO₂ emission and CO₂ storage and usage are the main strategies. Among these, CO₂ usage has been extensively explored, because it can reduce the CO₂ level and simultaneously provide opportunities for the development in catalysts and industries to convert CO₂ as a carbon source for preparing valuable products. However, transformation of CO₂ to other chemicals is challenging owing to its thermodynamic and kinetic stabilities. Among the CO₂ utilization techniques, electrochemical CO₂ reduction (ECR) is a promising alternative because it is generally conducted under ambient conditions, and water is used as the economical hydrogen source. Moreover, ECR offers a potential route to store electrical energy from renewable sources in the form of chemical energy, through generation of CO₂ reduction products. To improve the energy efficiency and viability of ECR, it is important to decrease the operational overpotential and maintain large current densities and high product selectivities; the development of efficient electrocatalysts is a critical aspect in this regard. To date, many kinds of materials have been designed and studied for application in ECR. Among these materials, metal oxide-based materials exhibit excellent performance as electrocatalysts for ECR and are attracting increasing attention in recent years. Investigation of the mechanism of reactions that involve metallic electrocatalysts has revealed the function of trace amount of oxidized metal species—it has been suggested that the presence of metal oxides and metal-oxygen bonds facilitates the activation of CO₂ and the subsequent formation and stabilization of the reaction intermediates, thereby resulting in high efficiency and selectivity of the ECR. Although the stability of metal oxides is a concern as they are prone to reduction under a cathodic potential, the catalytic performance of metal oxide-based catalysts can be maintained through careful designing of the morphology and structure of the materials. In addition, introducing other metal species to metal oxides and fabricating composites of metal oxides and other materials are effective strategies to achieve enhanced performance in ECR. In this review, we summarize the recent progress in the use of metal oxide-based materials as electrocatalysts and their application in ECR. The critical role, stability, and structure-performance relationship of the metal oxide-based materials for ECR are highlighted in the discussion. In the final part, we propose the future prospects for the development of metal oxide-based electrocatalysts for ECR.     

单原子配位结构及与载体相互作用的调控用于二氧化碳电催化还原

The combustion of fossil fuels increases atmospheric carbon dioxide (CO₂) concentrations, leading to adverse impacts on the planetary radiation balance and, consequently, on the climate. Fossil fuel utilization has contributed to a marked rise in global temperatures, now at least 1.2 ℃ above 'pre-industrial' levels. To meet the 2015 Paris Agreement target of 1.5 ℃ above pre-industrial levels, considerable efforts are required to efficiently capture and utilize CO₂. Among the different strategies developed for converting CO₂, electrochemical CO₂ reduction (ECR) to valuable chemicals using renewable energy is expected to revolutionize the manufacture of sustainable "green" chemicals, thereby achieving a closed anthropogenic carbon cycle. However, CO₂ is a thermodynamically stable and kinetically inert molecule that requires high electrical energy to bend the linear O＝C＝O bond by attacking the C atom. To facilitate the ECR with good energy efficiency, it is essential to lower the reaction overpotential as well as maintain a high current density and desirable product selectivity; therefore, the design and development of advanced electrocatalysts are crucial. A plethora of heterogeneous and homogeneous materials has been explored in the ECR. Among these materials, single-atom catalysts (SACs) have been the focus of most extensive research in the context of ECR. A SAC with isolated metal atoms dispersed on a supporting host exhibits a unique electronic structure, well-defined coordination environment, and an extremely high atom utilization maximum; thus, SACs have emerged as promising materials over the last two decades. Single-atom catalysis has covered the periodic table from d-block and ds-block metals to p-block metals. The types of support materials for SACs, ranging from metal oxides to tailored carbon materials, have also expanded. The adsorption strength and catalytic activity of SACs can be effectively tuned by modulating the central metal and local coordination structure of the SACs. In this article, we discuss the progress made to date in the field of single-atom catalysis for promoting ECR. We provide a comprehensive review of state-of-the-art SACs for the ECR in terms of product distribution, selectivity, partial current density, and performance stability. Special attention is paid to the modification of SACs to improve the ECR efficiency. This includes tailoring the coordination of the heteroatom, constructing bimetallic sites, engineering the morphologies and surface defects of supports, and regulating surface functional groups. The correlation of the coordination structure of SACs and metal-support interactions with ECR performance is analyzed. Finally, development opportunities and challenges for the application of SACs in the ECR, especially to form multi-carbon products, are presented.     

Predicting experimental properties of proteins from sequence by machine learning techniques

Efficient target selection methods are an important prerequisite for increasing the success rate and reducing the cost of high-throughput structural genomics efforts. There is a high demand for sequence-based methods capable of predicting experimentally tractable proteins and filtering out potentially difficult targets at different stages of the structural genomic pipeline. Simple empirical rules based on anecdotal evidence are being increasingly superseded by rigorous machine-learning algorithms. Although the simplicity of less advanced methods makes them more human understandable, more sophisticated formalized algorithms possess superior classification power. The quickly growing corpus of experimental success and failure data gathered by structural genomics consortia creates a unique opportunity for retrospective data mining using machine learning techniques and results in increased quality of classifiers. For example, the current solubility prediction methods are reaching the accuracy of over 70%. Furthermore, automated feature selection leads to better insight into the nature of the correlation between amino acid sequence and experimental outcome. In this review we summarize methods for predicting experimental success in cloning, expression, soluble expression, purification and crystallization of proteins with a special focus on publicly available resources. We also describe experimental data repositories and machine learning techniques used for classification and feature selection.     

单原子配位结构及与载体相互作用的调控用于二氧化碳电催化还原

电催化二氧化碳还原(ECR) 制备高值化学品被认为是在碳中和背景下实现可再生能源存储及降低CO₂浓度的一种有效策略。为了实现此目标,催化剂的开发与设计是ECR研究的关键。单原子催化剂(SACs) 因其独特的电子结构、明确的配位环境和极高的原子利用率,近年来在ECR领域引起了广泛关注。通过调节SACs的中心金属元素种类和局部配位结构,可有效调节SACs对CO₂和其还原中间体的吸附强度和催化活性。本文总结了SACs在ECR领域所取得的最新研究进展,重点讨论了SACs的配位结构及其与载体之间的相互作用对催化活性的影响以及相关调控策略,最后,提出了SACs应用于ECR所面临的机遇与挑战。     

Operando NRIXS and XAFS Investigation of Segregation Phenomena in Fe-Cu and Fe-Ag Nanoparticle Catalysts during CO2 Electroreduction

Operando nuclear resonant inelastic X-ray scattering (NRIXS) and X-ray absorption fine-structure spectroscopy (XAFS) measurements were used to gain insight into the structure and surface composition of FeCu and FeAg nanoparticles (NPs) during the electrochemical CO₂ reduction (CO₂RR) and to extract correlations with their catalytic activity and selectivity. The formation of a core–shell structure during CO₂RR for FeAg NPs was inferred from the analysis of the operando NRIXS data (phonon density of states, PDOS) and XAFS measurements. Electrochemical analysis of the FeAg NPs revealed a faradaic selectivity of 36 % for CO in 0.1 M KHCO₃ at −1.1 V vs. RHE, similar to that of pure Ag NPs. In contrast, a predominant selectivity towards H₂ evolution is obtained in the case of the FeCu NPs, analogous to the results obtained for pure Fe NPs, although small Cu NPs have also been shown to favor H₂ production.     

Simultaneous removal of Cu2+ and Cr3+ ions from aqueous solution based on Complexation with Eriochrome cyanine‐R and derivative spectrophotometric method

TiO₂ nanoparticles deposited on activated carbon (TiO₂–NP–AC) was prepared and characterized by XRD and SEM analysis. Subsequently, simultaneous ultrasound‐assisted adsorption of Cu²⁺ and Cr³⁺ ions onto TiO₂‐NPs‐AC after complexation via eriochrome cyanine R (ECR) has been investigated with UV–Vis and FAA spectrophotometer. Spectra overlapping of the ECR‐Cu and ECR‐Cr complex was resolve by derivative spectrophotometric technique. The effects of various parameters such as initial Cu²⁺ (A) and Cr³⁺ (B) ions concentrations, TiO₂‐NPs‐AC mass (C), sonication time (D) and pH (E) on the removal percentage were investigated and optimized by central composite design (CCD). The optimize conditions were set as: 4.21 min, 0.019 mg, 20.02 and 13.22 mg L^?1 and 6.63 for sonication time, TiO₂–NP–AC mass, initial Cr³⁺ and Cu²⁺ ions concentration and pH, respectively. The experimental equilibrium data fitting to Langmuir, Freundlich, Temkin and Dubinin–Radushkevich models show that the Langmuir model is a good and suitable model for evaluation and the actual behavior of adsorption process and maximum adsorption capacity of 105.26 and 93.46 mg g^?1 were obtained for Cu²⁺ and Cr³⁺ ions, respectively. Kinetic evaluation of experimental data showed that the adsorption processes followed well pseudo second order and intraparticle diffusion models.     

Experimental and theoretical study of D2/H2 quantum sieving in a carbon molecular sieve

The present work aims at providing additional insight into the crucial effect of pore size and pressure on the adsorption of H₂ and D₂ in porous carbons by means of Grand Canonical Monte Carlo simulations in model slit micropores at 77 K. In order to address the quantum behavior of the molecules the Feynman–Hibbs corrected LJ interaction potential is used for fluid–solid and fluid–fluid interactions. Based on the GCMC isotherms for the two isotopes, D₂ selectivity over H₂ is deduced for pores with different sizes as a function of pressure. Furthermore, GCMC results are coupled with experimental high pressure H₂ and D₂ adsorption data at 77 K for a commercial carbon molecular sieve (Takeda 3A).     

Specificity rule discovery in HIV-1 protease cleavage site analysis

Several machine learning algorithms have recently been applied to modeling the specificity of HIV-1 protease. The problem is challenging because of the three issues as follows: (1) datasets with high dimensionality and small number of samples could misguide classification modeling and its interpretation; (2) symbolic interpretation is desirable because it provides us insight to the specificity in the form of human-understandable rules, and thus helps us to design effective HIV inhibitors; (3) the interpretation should take into account complexity or dependency between positions in sequences. Therefore, it is necessary to investigate multivariate and feature-selective methods to model the specificity and to extract rules from the model. We have tested extensively various machine learning methods, and we have found that the combination of neural networks and decompositional approach can generate a set of effective rules. By validation to experimental results for the HIV-1 protease, the specificity rules outperform the ones generated by frequency-based, univariate or black-box methods.     

Comparative Study of Methane Activation Process by Different Plasma Sources

In this paper, we compare the characteristics of methane activation by diverse plasma sources. The test conditions of reactant flow rate and composition are fixed for each plasma source to eliminate any possible misleading effects from varying test conditions. Among the diverse characteristics of each plasma source, we focus on the electron energy and degree of thermal activation in evaluating the cost-effectiveness of methane decomposition. The reaction is evaluated based on the selectivity of specific products, including H₂, C₂H₆, and C₂H₂. Among the tested plasma sources, those that provide a somewhat thermal environment have a rather high degree of warmness, resulting in higher methane conversion and lower operational costs. As the non-thermal characteristics of the plasma sources become stronger, the selectivity of C₂H₆ increases. This reflects C₂H₆ formation from the direct collision of CH₄ with high-energy electrons. On the other hand, as the degree of warmness increases, the selectivity of H₂ and C₂H₂ increase. The results give an insight into possible tools for process control or selectivity control by varying the degree of warmness in the plasma source. The process optimization and cost reduction of methane activation should be based on this concept of selectivity control.     

Mechanistic Study of Unprecedented Highly Regioselective Hydrocyanation of Terminal Alkynes: Insight into the Origins of the Regioselectivity and Ligand Effects

Density functional theory (DFT) calculations were performed to gain insight into the mechanism of the nickel-catalyzed hydrocyanation of terminal alkynes with Zn(CN)₂ and water to exclusively generate the branched nitrile with excellent Markovnikov selectivity. After precatalyst activation to give the LNi(0) active species, the transformation proceeds via the following steps: (1) oxidative addition of H₂O to the LNi(0) provides the intermediate LNi(II)H(OH); (2) ligand exchange of LNi(II)H(OH) with Zn(CN)₂ gives the intermediate LNi(II)H(CN); (3) alkyne insertion to the LNi(II)H(CN) forms the alkenyl nickel complex, followed by the reductive elimination step reaching the final product. This mechanism is kinetically and thermodynamically more favorable than that of the experimental proposed ones. On the basis of the experimental observations, more water molecules cannot further improve the reaction as it has also been rationalized. Furthermore, the origin of the high regioselectivity of the product, the variable effectiveness of the metal mediator as function of ligands, as well as the high yield of the alkyl-substituted alkynes substrates, is analyzed in detail. © 2019 Wiley Periodicals, Inc.     

Tertiary recycling of commingled polymer waste over commercial FCC equilibrium catalysts for producing hydrocarbons

A mixture of post-consumer polymer waste (PE/PP/PS) was pyrolysed over cracking catalysts using a fluidising reaction system similar to the FCC process operating isothermally at ambient pressure. Greater product selectivity was observed with a commercial FCC equilibrium catalyst (FCC-E1) with about 53 wt% olefin products in the C₃-C₆ range. Experiments carried out with various catalysts gave good yields of valuable hydrocarbons with differing selectivity in the final products dependent on reaction conditions. A kinetic model based on a lumping reaction scheme for the observed products and catalyst coking behaviours has been investigated. The model gave a good representation of experiment results. This model provides the benefits of lumping product selectivity, in each reaction step, in relation to the performance of the catalyst used and particle size selected as well as the effect of operation conditions, such as rate of fluidising gas and reaction temperature. It is demonstrated that under appropriate reaction temperatures and suitable catalysts can have the ability to control both the product yield and product distribution from polymer degradation, and can potentially lead to a cheaper process with more valuable products.     

Improved machine learning models for predicting selective compounds

The identification of small potent compounds that selectively bind to the target under consideration with high affinities is a critical step toward successful drug discovery. However, there is still a lack of efficient and accurate computational methods to predict compound selectivity properties. In this paper, we propose a set of machine learning methods to do compound selectivity prediction. In particular, we propose a novel cascaded learning method and a multitask learning method. The cascaded method decomposes the selectivity prediction into two steps, one model for each step, so as to effectively filter out nonselective compounds. The multitask method incorporates both activity and selectivity models into one multitask model so as to better differentiate compound selectivity properties. We conducted a comprehensive set of experiments and compared the results with those of other conventional selectivity prediction methods, and our results demonstrated that the cascaded and multitask methods significantly improve the selectivity prediction performance.     

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol⁻¹ were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.

A machine learning model for enantioselectivity prediction using reaction-based molecular representations.     

Exfoliated 2D Layered and Nonlayered Metal Phosphorous Trichalcogenides Nanosheets as Promising Electrocatalysts for CO2 Reduction

Two-dimensional (2D) materials catalysts provide an atomic-scale view on a fascinating arena for understanding the mechanism of electrocatalytic carbon dioxide reduction (CO₂ ECR). Here, we successfully exfoliated both layered and nonlayered ultra-thin metal phosphorous trichalcogenides (MPCh₃) nanosheets via wet grinding exfoliation (WGE), and systematically investigated the mechanism of MPCh₃ as catalysts for CO₂ ECR. Unlike the layered CoPS₃ and NiPS₃ nanosheets, the active Sn atoms tend to be exposed on the surfaces of nonlayered SnPS₃ nanosheets. Correspondingly, the nonlayered SnPS₃ nanosheets exhibit clearly improved catalytic activity, showing formic acid selectivity up to 31.6 % with −7.51 mA cm⁻² at −0.65 V vs. RHE. The enhanced catalytic performance can be attributed to the formation of HCOO* via the first proton-electron pair addition on the SnPS₃ surface. These results provide a new avenue to understand the novel CO₂ ECR mechanism of Sn-based and MPCh₃-based catalysts.     

Selective CO2-to-C2H4 Photoconversion Enabled by Oxygen-Mediated Triatomic Sites in Partially Oxidized Bimetallic Sulfide

Selective CO₂ photoreduction into C₂ fuels under mild conditions suffers from low product yield and poor selectivity owing to the kinetic challenge of C−C coupling. Here, triatomic sites are introduced into bimetallic sulfide to promote C−C coupling for selectively forming C₂ products. As an example, FeCoS₂ atomic layers with different oxidation degrees are first synthesized, demonstrated by X-ray photoelectron spectroscopy and X-ray absorption near edge spectroscopy spectra. Both experiment and theoretical calculation verify more charges aggregate around the introduced oxygen atom, which enables the original Co−Fe dual sites to turn into Co−O−Fe triatomic sites, thus promoting C−C coupling of double *COOH intermediates. Accordingly, the mildly oxidized FeCoS₂ atomic layers exhibit C₂H₄ formation rate of 20.1 μmol g⁻¹ h⁻¹, with the product selectivity and electron selectivity of 82.9 % and 96.7 %, outperforming most previously reported photocatalysts under similar conditions.