Rational Design and Precise Synthesis of Single-Atom Alloy Catalysts for the Selective Hydrogenation of Nitroarenes.

Single-atom alloys (SAAs) have gained increasing prominence in the field of selective hydrogenation reactions due to their uniform distribution of active sites and the unique host-guest metal interactions. Herein, 15 SAAs are constructed to comprehensively elucidate the relationship between host-guest metal interaction and catalytic performance in the selective hydrogenation of 4-nitrostyrene (4-NS) by density functional theory (DFT) calculations. The results demonstrate that the SAAs with strong host-guest metal interactions exhibit a preference for N─O bond cleavage, and the reaction energy barrier of the hydrogenation process is primarily influenced by the host metal. Among them, Ir1Ni SAA stands out as the prime catalyst candidate, showcasing exceptional activity and selectivity. Furthermore, the Ir1Ni SAA is subsequently prepared through precise synthesis techniques and evaluated in the selective hydrogenation of 4-NS to 4-aminostyrene (4-AS). As anticipated, the Ir1Ni SAA demonstrates extraordinary catalytic performance (yield > 96%). In situ FT-IR experiments and DFT calculations further confirmed that the unique host-guest metal interaction at the Ir-Ni interface site of Ir1Ni SAA endows it with excellent 4-NS selective hydrogenation ability. This work provides valuable insights into enhancing the performance of SAAs catalysts in selective hydrogenation reactions by modulating the host-guest metal interactions.

Screening and Mechanism Exploration of Non-Noble Metal Ni3M Catalysts for Propane Dehydrogenation: The Excellence of Synergistic Effects.

The development of cost-effective and anti-coking catalysts for propane dehydrogenation (PDH) is crucial. Here, non-noble metal-incorporated Ni-based catalysts (Ni3M, M = Sc, Ti, V, Mn, Fe, Co, Cu, Zn, Ga, Zr, Nb, Mo, In, Sn) were employed in the PDH process. The introduction of V, Nb, and Mo, with their strong carbon binding ability, created unique Ni-M cooperative sites, enhancing the catalytic performance. Other non-noble metals influenced the electronic structure of Ni, affecting the overall catalytic behavior. V and Nb exhibited a balanced combination of activity, selectivity, and stability, making them potential catalyst candidates. Microkinetic simulations revealed that Ni3V and Ni3Nb displayed high selectivity toward olefins with low apparent activation energies. This study emphasizes the significance of bimetallic synergy in enhancing PDH performance and provides new directions for the development of efficient alkane dehydrogenation catalyst development.

Ultrathin LayCoOx Nanosheets with High Porosity Featuring Boosted Catalytic Oxidation of Benzene: Mechanism Elucidation via an Experiment-Theory Combined Paradigm.

Designing transition-metal oxides for catalytically removing the highly toxic benzene holds significance in addressing indoor/outdoor environmental pollution issues. Herein, we successfully synthesized ultrathin LayCoOx nanosheets (thickness of ∼1.8 nm) with high porosity, using a straightforward coprecipitation method. Comprehensive characterization techniques were employed to analyze the synthesized LayCoOx catalysts, revealing their low crystallinity, high surface area, and abundant porosity. Catalytic benzene oxidation tests demonstrated that the La0.029CoOx-300 nanosheet exhibited the most optimal performance. This catalyst enabled complete benzene degradation at a relatively low temperature of 220 °C, even under a high space velocity (SV) of 20,000 h-1, and displayed remarkable durability throughout various catalytic assessments, including SV variations, exposure to water vapor, recycling, and long time-on-stream tests. Characterization analyses confirmed the enhanced interactions between Co and doped La, the presence of abundant adsorbed oxygen, and the extensive exposure of Co3+ species in La0.029CoOx-300 nanosheets. Theoretical calculations further revealed that La doping was beneficial for the formation of oxygen vacancies and the adsorption of more hydroxyl groups. These features strongly promoted the adsorption and activation of oxygen, thereby accelerating the benzene oxidation processes. This work underscores the advantages of doping rare-earth elements into transition-metal oxides as a cost-effective yet efficient strategy for purifying industrial exhausts.

Designing Efficient Single-Atom Alloy Catalysts for Selective C═O Hydrogenation: A First-Principles, Active Learning and Microkinetic Study.

Selective hydrogenation of α,ß-unsaturated aldehydes into unsaturated alcohols is a process in high demand in organic synthesis, pharmaceuticals, and food production. This process requires the precise hydrogenation of C═O bonds, a challenge that requires a tailored catalyst. Single-atom alloys (SAAs), where individual atoms of one metal are distributed in a host metal matrix, offer a potential solution to this challenge. Nevertheless, identifying the appropriate SAA capable of targeted adsorption and the efficient activation of C═O bonds remains a substantial hurdle. In this work, we synergistically combine density functional theory (DFT) calculations, active learning, and microkinetic simulations to design SAAs for the efficient and selective hydrogenation of α,ß-unsaturated aldehydes. We first comprehensively assessed the potential of 66 SAAs across 264 surfaces (including (100), (110), (111), and (320) crystal planes), to gauge their potential in activating C═C and C═O bonds. Our assessment unveiled the excellent selectivity of the Ti1Au SAA in activating C═O bonds. Moreover, our detailed DFT calculations further demonstrated the high catalytic activity of Ti1Au(320) and Ti1Au(111) surfaces with a low activation energy barrier of only 0.60 eV. Subsequently, we conducted microkinetic simulations on the selective hydrogenation process of crotonaldehyde, by selecting Ti1Au (320) and (111) surfaces as the catalysts and demonstrated that they exhibited a remarkable selectivity and nearly 100% conversion toward crotyl alcohol in the temperature range from 373 to 553 K. The present study not only reveals novel SAAs for targeted hydrogenation of α,ß-unsaturated aldehydes but also establishes a promising path toward efficient design of selective hydrogenation catalysts more broadly.

Active Learning Accelerating to Screen Dual-Metal-Site Catalysts for Electrochemical Carbon Dioxide Reduction Reaction.

Dual-metal-site catalysts (DMSCs) are increasingly important catalysts in the field of electrochemical carbon dioxide reduction reaction (CO2RR) in recent years. However, rapid screening of suitable metal combinations of DMSCs remains a huge challenge. Herein, we constructed an active learning (AL) framework to study CO2RR to HCOOH. This AL framework turned out a success in the accurate prediction of 282 DMSCs for CO2RR through interactive learning between users and machine learning (ML) models. Among the 42 DMSCs calculated in three iteration loops of AL, 29 DMSCs were obtained, where the screening success rate was as high as 70%. Furthermore, we found five experimentally unexplored DMSCs that exhibited better CO2RR activity and selectivity than pure Bi. Low prediction errors on other DMSCs show that the AL model possessed outstanding universality. The results prove the excellent potential of the AL method and provide guidance on the design of high-performance electrocatalysts for CO2RR.

GRA-GCN: Dense Granule Protein Prediction in Apicomplexa Protozoa Through Graph Convolutional Network.

Dense granule proteins (GRAs) are secreted by Apicomplexa protozoa, which are closely related to an extensive variety of farm animal diseases. Predicting GRAs is an integral part in prevention and treatment of parasitic diseases. Considering that biological experiment approach is time-consuming and labor-intensive, computational method is a superior choice. Hence, developing an effective computational method for GRAs prediction is of urgency. In this paper, we present a novel computational method named GRA-GCN through graph convolutional network. In terms of the graph theory, the GRAs prediction can be regarded as a node classification task. GRA-GCN leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation. To our knowledge, this is the first attempt to utilize computational approach for GRAs prediction. Evaluated by 5-fold cross-validations, the GRA-GCN method achieves satisfactory performance, and is superior to four classic machine learning-based methods and three state-of-the-art models. The analysis of the comprehensive experiment results and a case study could offer valuable information for understanding complex mechanisms, and would contribute to accurate prediction of GRAs. Moreover, we also implement a web server at http://dgpd.tlds.cc/GRAGCN/index/, for facilitating the process of using our model.

DGPD: a knowledge database of dense granule proteins of the Apicomplexa.

Apicomplexan parasites cause severe diseases in human and livestock. Dense granule proteins (GRAs), specific to the Apicomplexa, participate in the maintenance of intracellular parasitism of host cells. GRAs have better immunogenicity and they can be emerged as important players in vaccine development. Although studies on GRAs have increased gradually in recent years, due to incompleteness and complexity of data collection, biologists have difficulty in the comprehensive utilization of information. Thus, there is a desperate need of user-friendly resource to integrate with existing GRAs. In this paper, we developed the Dense Granule Protein Database (DGPD), the first knowledge database dedicated to the integration and analysis of typical GRAs properties. The current version of DGPD includes annotated GRAs metadata of 245 samples derived from multiple web repositories and literature mining, involving five species that cause common diseases (Plasmodium falciparum, Toxoplasma gondii, Hammondia hammondi, Neospora caninum and Cystoisospora suis). We explored the baseline characteristics of GRAs and found that the number of introns and transmembrane domains in GRAs are markedly different from those of non-GRAs. Furthermore, we utilized the data in DGPD to explore the prediction algorithms for GRAs. We hope DGPD will be a good database for researchers to study GRAs. Database URL: http://dgpd.tlds.cc/DGPD/index/.

Theoretical Investigation of Cu-Au Alloy for Carbon Dioxide Electroreduction: Cu/Au Ratio Determining C1/C2 Selectivity.

Copper-gold alloy exhibits excellent catalytic performance for the carbon dioxide electroreduction (CO2ER) reaction, but the mechanism of the effect of the Cu/Au ratio on the selectivity of C1/C2 products has not been carefully investigated. In this work, (100) and (111) surfaces of three CuAu alloys with different Cu/Au (3:1, 1:1, 1:3) ratios are constructed. The properties of CuAu surfaces like density of states, Bader charge, and the whole CO2ER to C2H4 and C2H5OH mechanisms are investigated. Our calculation reveals that the adsorption capacity of the catalyst surface for the intermediates *COOH and *CO is enhanced with the increase of the Au ratio. The calculation results show that the Cu1Au1(100) surface has the highest activity for CO2ER to CO (UL = -0.32 V). Furthermore, the Cu3Au1(100) surface exhibits the best coupling performance, and ethanol is the dominant product for CO2ER to C2 products. Our work provides a useful guideline for further developing CO2ER electrocatalysts.

MTAGCN: predicting miRNA-target associations in Camellia sinensis var. assamica through graph convolution neural network.

BACKGROUND: MircoRNAs (miRNAs) play a central role in diverse biological processes of Camellia sinensis var.assamica (CSA) through their associations with target mRNAs, including CSA growth, development and stress response. However, although the experiment methods of CSA miRNA-target identifications are costly and time-consuming, few computational methods have been developed to tackle the CSA miRNA-target association prediction problem. RESULTS: In this paper, we constructed a heterogeneous network for CSA miRNA and targets by integrating rich biological information, including a miRNA similarity network, a target similarity network, and a miRNA-target association network. We then proposed a deep learning framework of graph convolution networks with layer attention mechanism, named MTAGCN. In particular, MTAGCN uses the attention mechanism to combine embeddings of multiple graph convolution layers, employing the integrated embedding to score the unobserved CSA miRNA-target associations. DISCUSSION: Comprehensive experiment results on two tasks (balanced task and unbalanced task) demonstrated that our proposed model achieved better performance than the classic machine learning and existing graph convolution network-based methods. The analysis of these results could offer valuable information for understanding complex CSA miRNA-target association mechanisms and would make a contribution to precision plant breeding.

Highly-efficient RuNi single-atom alloy catalysts toward chemoselective hydrogenation of nitroarenes.

The design and exploitation of high-performance catalysts have gained considerable attention in selective hydrogenation reactions, but remain a huge challenge. Herein, we report a RuNi single atom alloy (SAA) in which Ru single atoms are anchored onto Ni nanoparticle surface via Ru-Ni coordination accompanied with electron transfer from sub-surface Ni to Ru. The optimal catalyst 0.4% RuNi SAA exhibits simultaneously improved activity (TOF value: 4293 h-1) and chemoselectivity toward selective hydrogenation of 4-nitrostyrene to 4-aminostyrene (yield: >99%), which is, to the best of our knowledge, the highest level compared with reported heterogeneous catalysts. In situ experiments and theoretical calculations reveal that the Ru-Ni interfacial sites as intrinsic active centers facilitate the preferential cleavage of N-O bond with a decreased energy barrier by 0.28 eV. In addition, the Ru-Ni synergistic catalysis promotes the formation of intermediates (C8H7NO* and C8H7NOH*) and accelerates the rate-determining step (hydrogenation of C8H7NOH*).

Machine-Learning-Assisted Catalytic Performance Predictions of Single-Atom Alloys for Acetylene Semihydrogenation.

Selective semihydrogenation of acetylene for the production of polymer-grade ethylene is a significant chemical industrial process. Facile activization of acetylene and weak adsorption of ethylene are critical requirements for high-performance catalysis. Single-atom alloys (SAAs) have strong binding effect on acetylene and weak effect on ethylene, which have been regarded as the superior catalysts for acetylene semihydrogenation. Herein, we established a pioneering machine learning (ML) assisted approach to investigate the reaction activity and selectivity of 70 SAA catalysts for acetylene semihydrogenation. As the most desirable ML model, the gradient boosting regression (GBR) algorithm has been extended to predict the energy barrier of *C2Hn (n = 2-4) hydrogenation with a root-mean-square error (RMSE) of only 0.02 eV. Notably, five candidate SAAs with excellent activity and selectivity for acetylene semihydrogenation are screened out via accessible descriptors. These data of ML prediction have been verified by DFT calculation with a high-accuracy (error less than 0.07 eV). This work demonstrates the potential of ML-assisted approach for predicting the energy barrier of transition state and simultaneously provides a convenient approach for the rational design of efficient catalysts.

RicENN: Prediction of Rice Enhancers with Neural Network Based on DNA Sequences.

Enhancers are the primary cis-elements of transcriptional regulation and play a vital role in gene expression at different stages of plant growth and development. Having high locational variation and free scattering in non-encoding genomes, identification of enhancers is a crucial, but challenging work in understanding the biological mechanism of model plants. Recently, applications of neural network models are gaining increasing popularity in predicting the function of genomic elements. Although several computational models have shown great advantages to tackle this challenge, a further study of the identification of rice enhancers from DNA sequences is still lacking. We present RicENN, a novel deep learning framework capable of accurately identifying enhancers of rice, integrating convolution neural networks (CNNs), bi-directional recurrent neural networks (RNNs), and attention mechanisms. A combined-feature representation method was designed to extract the sequence features from original DNA sequences using six types of autocorrelation encodings. Moreover, we verified that the integrated model achieves the best performance by an ablation study. Finally, our deep learning framework realized a reliable prediction of the rice enhancers. The results show RicENN outperforms available alternative approaches in rice species, achieving the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) of 0.960 and 0.960 on cross-validation, and 0.879 and 0.877 during independent tests, respectively. This study develops a hybrid model to combine the merits of different neural network architectures, which shows the potential ability to apply deep learning in bioinformatic sequences and contributes to the acceleration of functional genomic studies of rice. RicENN and its code are freely accessible at http://bioinfor.aielab.cc/RicENN/ .

Correction: A kinetics study on hydrogen abstraction reactions of cyclopentane by hydrogen, methyl, and ethyl radicals.

Correction for 'A kinetics study on hydrogen abstraction reactions of cyclopentane by hydrogen, methyl, and ethyl radicals' by Wenqi Chen et al., Phys. Chem. Chem. Phys., 2021, 23, 7333-7342, DOI: 10.1039/D1CP00386K.

A kinetics study on hydrogen abstraction reactions of cyclopentane by hydrogen, methyl, and ethyl radicals.

Hydrogen abstraction reactions of (cyclo)alkanes by radicals play a fundamental role in both combustion and atmospheric chemistry. In this work, we select three common radicals in the pyrolysis of hydrocarbon fuels: hydrogen radical (H[combining dot above]), methyl radical (CH3), and ethyl radical (CH2CH3) to investigate the kinetics of their hydrogen abstraction reactions with cyclopentane. The rate constants over a broad temperature range of 150-3000 K are calculated by using the multi-structural variational transition state theory in the small-curvature tunneling approximation (MS-CVT/SCT), by which the multi-structural torsional (MS-T) anharmonicity of partition functions, variational effects, and corner-cutting tunneling are all included in dynamics calculations. We stress the particular importance of considering the MS-T anharmonicity in the rate constant calculation for the reaction with the ethyl radical compared to those with hydrogen and methyl radicals. The MS-T anharmonicity significantly accelerates the reaction with the ethyl radical in the whole temperature range, and in particular, it increases the rate constant by a factor of >-9 at 1000 K. We also found that the tunneling effect drastically increases the rate constants at low-temperatures by up to 3-5 orders of magnitudes. The calculated reaction rate constants have an order of .

Platinum-copper single atom alloy catalysts with high performance towards glycerol hydrogenolysis.

Selective hydrogenolysis of biomass-derived glycerol to propanediol is an important reaction to produce high value-added chemicals but remains a big challenge. Herein we report a PtCu single atom alloy (SAA) catalyst with single Pt atom dispersed on Cu nanoclusters, which exhibits dramatically boosted catalytic performance (yield: 98.8%) towards glycerol hydrogenolysis to 1,2-propanediol. Remarkably, the turnover frequency reaches up to 2.6 × 103 molglycerol·molPtCu-SAA-1·h-1, which is to our knowledge the largest value among reported heterogeneous metal catalysts. Both in situ experimental studies and theoretical calculations verify interface sites of PtCu-SAA serve as intrinsic active sites, in which the single Pt atom facilitates the breakage of central C-H bond whilst the terminal C-O bond undergoes dissociation adsorption on adjacent Cu atom. This interfacial synergistic catalysis based on PtCu-SAA changes the reaction pathway with a decreased activation energy, which can be extended to other noble metal alloy systems.

Mechanistic insights into artificial metalloenzymes towards imine reduction.

In the field of artificial metalloenzyme (ArM) catalysis, how to identify the critical factors affecting the catalytic activity and enantioselectivity remains a challenge. In this work, the mechanism of enantioselective reduction of imine catalyzed by using [Rh(Me4Cpbiot)Cl2]·S112H Sav (denoted as S112H) and [Rh(Me4Cpbiot)Cl2]·K121H Sav (denoted as K121H) was studied by using molecular dynamics (MD) simulations combined with density functional theory (DFT) calculations. Four binding modes of imine, two proton sources (hydronium ion and lysine) and eight proposed reaction pathways were systematically discussed. The results showed that due to the anchoring effect of the mutation site of ArMs, the rhodium complex which oscillated like a pendulum was bound to a specific conformation, which further determined the chirality of the reduced product. C-Hπ, cation-π and ππ weak interactions played an important role in imine binding, and the favorable binding mode of imine was catalyzed by S112H in landscape orientation and catalyzed by K121H in portrait orientation, respectively. LYS121 is the most possible proton source in the S112H catalytic process while the proton source in the K121H catalytic process is the hydronium ion of the active sites. Furthermore, based on the reaction mechanism, modification of Rh(Me4Cpbiot)Cl2 was carried out in S112H and K121H, and the results suggested that the reaction barrier could be effectively reduced by replacing the methyl groups on Cp* with an amino group. This work gives a fundamental understanding of the mechanism of ArMs toward the imine reduction reaction, in the hope of providing a strategy for reasonable designs of ArMs with high enantioselectivity.