The influence of crystal facet on the catalytic performance of MOFs-derived NiO with different morphologies for the total oxidation of propane: The defect engineering dominated by solvent regulation effect.

Crystal facet and defect engineering are crucial for designing heterogeneous catalysts. In this study, different solvents were utilized to generate NiO with distinct shapes (hexagonal layers, rods, and spheres) using nickel-based metal-organic frameworks (MOFs) as precursors. It was shown that the exposed crystal facets of NiO with different morphologies differed from each other. Various characterization techniques and density functional theory (DFT) calculations revealed that hexagonal-layered NiO (NiO-L) possessed excellent low-temperature reducibility and oxygen migration ability. The (111) crystal plane of NiO-L contained more lattice defects and oxygen vacancies, resulting in enhanced propane oxidation due to its highest O2 adsorption energy. Furthermore, the higher the surface active oxygen species and surface oxygen vacancy concentrations, the lower the C-H activation energy of the NiO catalyst and hence the better the catalytic activity for the oxidation of propane. Consequently, NiO-L exhibited remarkable catalytic activity and good stability for propane oxidation. This study provided a simple strategy for controlling NiO crystal facets, and demonstrated that the oxygen defects could be more easily formed on NiO(111) facets, thus would be beneficial for the activation of C-H bonds in propane. In addition, the results of this work can be extended to the other fields, such as propane oxidation to propene, fuel cells, and photocatalysis.

An Experimental Investigation of the Effects of Block Proportion on Bimrocks, Considering Different Block-to-Matrix Strength Ratios.

The rock block proportion is one of the most important factors affecting the mechanical properties of bimrocks. Under different block-to-matrix strength ratios, the influence of rock block proportion is different. To explore the influence of rock block proportion on the mechanical properties of specimens under different block-to-matrix strength ratios, a new indoor test method for making bimrocks was proposed. A uniaxial compression test and a direct shear test were carried out on specimens with different rock block proportions. The results show that this method can control the block-to-matrix strength ratio well, and the influence of rock block proportion is obviously different under different block-to-matrix strength ratios. The strong matrix sample will decrease significantly after reaching the peak compressive strength, while the weak matrix will decrease slowly after reaching the peak strength. The rock block proportion is negatively correlated with the uniaxial compressive strength of strong matrix samples (the reduction was 12.53%) and is positively correlated with the uniaxial compressive strength of weak matrix samples as a whole, but it changes when block proportion is more than 50%. With the increase in normal stress and rock block proportion increases from 30% to 60%, the shear failure zone of the weak matrix sample increases, and the cracks are inclined, while the strong matrix sample has more secondary cracks. The results of this study also show that the effect of volumetric block proportion (VBP) on the internal friction angle and cohesion of the sample is less related to the block-to-matrix strength ratio.

Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.

The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities.

The oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) are crucial for the conversion of clean energy. Recently, dual-metal-site catalysts (DMSCs) have gained much attention due to their high atom utilization, stronger stability, and better catalytic performance. An advanced method that combines density functional theory (DFT) and machine learning (ML) has been employed in this study to investigate the adsorption free energies of adsorbates on hundreds of potential catalysts, with the aim of screening for catalysts that are highly active for the ORR and OER. The result of this study is that 30 DMSCs with ORR activity superior to Pt, 10 DMSCs with OER activity superior to RuO2, and 4 bifunctional catalysts for the OER and ORR are identified. This work provides guidance for the rational selection of metals on DMSCs to prepare catalysts with a high electrocatalytic performance for renewable energy applications.

Green Synthesis of CoZn-Based Metal-Organic Framework (CoZn-MOF) from Waste Polyethylene Terephthalate Plastic As a High-Performance Anode for Lithium-Ion Battery Applications.

The recycling of discarded polyethylene terephthalate (PET) plastics produced metal-organic frameworks can effectively minimize environmental pollution and promote sustainable economic development. In this study, we developed a method using NaOH in alcohol and ether solvent environments to degrade PET plastics for synthesizing terephthalic acid. The method achieved a 97.5% degradation rate of PET plastics under a reaction temperature of 80 °C for 60 min. We used terephthalic acid as a ligand from the degradation products to successfully synthesize two types of monometallic and bimetallic CoZn-MOF materials. We investigated the impact of different metal centers and solvents on the electrochemical performance of the MOF materials. The result showed that the MOF-DMF/H2O material maintained a specific capacity of 1485.5 mAh g-1 after 100 cycles at a current density of 500 mA g-1, demonstrating excellent rate capability and cycling stability. In addition, our finding showed that the performance difference might be attributed to the synergistic effect of bimetallic Co2+ and Zn2+ in MOF-DMF/H2O, rapid lithium-ion diffusion and electron transfer rates, and the absence of coordinating solvents. Additionally, the non-in situ X-ray powder diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy analysis results showed that lithium storage in the MOF-DMF/H2O electrode mainly depended on the aromatic C6 ring and carboxylate portions of the organic ligands in different charge and discharge states. Lithium ions can be reversibly inserted/removed into/from the electrode material. The physical adsorption on the MOF surface through electrostatic interactions enhanced both capacity and cycling stability. This research provides valuable insight for mitigating solid waste pollution, promoting sustainable economic development, and advancing the extensive applications of MOF materials in lithium-ion batteries.

Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature.

The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%.

BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction.

Recently, in the field of crystal property prediction, the graph neural network (GNN) model has made rapid progress. The GNN model can effectively capture high-dimensional crystal features from the crystal structure, thereby achieving optimal performance in property prediction. However, the existing GNN model faces limitations in handling the hidden layer after the pooling layer, which restricts the training performance of the model. In the present research, we propose a novel GNN model called the batch normalization multilayer perceptron crystal distance graph neural network (BNM-CDGNN). BNM-CDGNN encodes the crystal's geometry structure only based on the distance vector between atoms. The graph convolutional layer utilizes the radial basis function as the attention mask, ensuring the crystal's rotation invariance and adding the geometric information on the crystal. Subsequently, the average pooling layer is connected after the convolutional layer to enhance the model's ability to learn precise information. BNM-CDGNN connects multiple hidden layers after the average pooling layers, and these layers are processed by the batch normalization layer. Finally, the fully connected layer maps the results to the target property. BNM-CDGNN significantly enhances the accuracy of crystal property prediction compared with previous baseline models such as SchNet, MPNN, CGCNN, MEGNet, and GATGNN.

Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning.

In drug design, the design and manufacture of safe and effective compounds is a long-term, complex, and complicated process. Therefore, developing a new rapid and generalizable drug design method is of great value. This study aimed to propose a general model based on reinforcement learning combined with drug-target interaction, which could be used to design new molecules according to different protein targets. The method adopted recurrent neural network molecular modeling and took the drug-target affinity model as the reward function of optimal molecular generation. It did not need to know the three-dimensional structure and active sites of protein targets but only required the information of a one-dimensional amino acid sequence. This approach was demonstrated to produce drugs highly similar to marketed drugs and design molecules with a better binding energy.

Waste PET Plastic-Derived CoNi-Based Metal-Organic Framework as an Anode for Lithium-Ion Batteries.

Recycling waste PET plastics into metal-organic frameworks is conducive to both pollution alleviation and sustainable economic development. Herein, we have utilized waste PET plastic to synthesize CoNi-MOF applied to lithium battery anode materials via a low-temperature solvothermal method for the first time. The preparation process is effortless, and the sources' conversion rate can reach almost 100%. In addition, the anode performance of MOFs with various Co/Ni mole ratios was investigated. The as-synthesized Co0.8Ni-MOF exhibits excellent crystallinity, purity, and electrochemical performance. The initial discharge and charge capacities are 2496 and 1729 mAh g-1, respectively. Even after 200 cycles, the Co0.8Ni-MOF electrode can exhibit a high Coulombic efficiency of over 99%. Consequently, given the environmental and economic benefits, the Co0.8Ni-MOF derived from waste PET plastic is thought to be an appealing anode material for lithium-ion batteries.

Theoretical Study of the Catalytic Activity and Anti-SO2 Poisoning of a MoO3/V2O5 Selective Catalytic Reduction Catalyst.

In this paper, density functional theory has been applied to study the mechanism of anti-SO2 poisoning and selective catalytic reduction (SCR) reaction on a MoO3/V2O5 surface. According to the calculation results, the SO2 molecule can be converted into SO3 on V2O5(010) and further transformed into NH4HSO4, which poisons V2O5. If V2O5 and MoO3 are combined with each other, charge separation of V2O5 and MoO3, which are negatively and positively charged, respectively, occurs at the interface. In ammonium bisulfate liquid droplets on the MoO3/V2O5 surface, NH4 + tends to adhere to the V2O5(010) surface and can be removed through the SCR reaction and HSO4 - tends to adhere to the MoO3(100) surface and can be resolved into SO3 and H2O, which can be released into the gas phase. Thus, MoO3/V2O5 materials are resistant to SO2 poisoning. In the MoO3/V2O5 material, Brønsted acid sites are easily formed on the negatively charged V2O5(010) surface; this reduces the energy barrier of the NH3 dissociation step in the NH3-SCR process and further improves the catalytic activity.

Revealing of Active Sites and Catalytic Mechanism in N-Coordinated Fe, Ni Dual-Doped Carbon with Superior Acidic Oxygen Reduction than Single-Atom Catalyst.

Herein, we synthesized a Fe, Ni dual-metal embedded in porous nitrogen-doped carbon material to endow higher turnover frequency (TOF), lower H2O2 yield, and thus superior durability than for the single-atom catalyst for oxygen reduction in acid media. Quantitative X-ray absorption near edge structure (XANES) fitting and density functional theory (DFT) calculation were implemented to explore the active sites in the catalysts. The results suggest FeNi-N6 with type I (each metal atom coordinated with four nitrogen atoms) instead of type II configuration (each metal atom coordinated with three nitrogen atoms) dominates the catalytic activity of the noble-metal free catalyst (NMFC). Further, theoretical calculation reveals that the oxygen reduction reaction (ORR) activity trend of different moieties was FeNi-N6 (type I) > FeNi-N6 (type II) > Fe-N4 > Fe2-N6. Our research represents an important step for developing dual-metal doping NMFC for proton exchange membrane fuel cells (PEMFCs) by revealing its new structural configuration and correlation with catalytic activity.

In†Situ Observation of Dynamic Galvanic Replacement Reactions in Twinned Metallic Nanowires by Liquid Cell Transmission Electron Microscopy.

Galvanic replacement is a versatile approach to prepare hollow nanostructures with controllable morphology and elemental composition. The primary issue is to identify its fundamental mechanism. In this study, in situ liquid cell transmission electron microscopy was employed to monitor the dynamic reaction process and to explore the mechanism of galvanic replacement. The detailed reaction process was revealed based on in situ experiments in which small Au particles first appeared around Ag nanowires; they coalesced, grew, and adhered to Ag nanowires. After that, small pits grew from the edge of Ag nanowires to form tubular structures, and then extended along the Ag nanowires to obtain hollowed structures. All of our experimental observations from the viewpoint of electron microscopy, combined with DFT calculations, contribute towards an in-depth understanding of the galvanic replacement reaction process and the design of new materials with hollow structures.

Enhanced photocatalytic N2 fixation by promoting N2 adsorption with a co-catalyst.

Photocatalytic N2 fixation involves a nitrogen reduction reaction on the surface of the photocatalyst to convert N2 into ammonia. Currently, the adsorption of N2 is the limiting step for the N2 reduction reaction on the surface of the catalyst. Based on the concept of photocatalytic water splitting, the photocatalytic efficiency can be greatly enhanced by introducing a co-catalyst. In this report, we proposed a new strategy, namely, the loading of a NiS co-catalyst on CdS nanorods for photocatalytic N2 fixation. Theoretical calculation results indicated that N2 was effectively adsorbed onto the NiS/CdS surface. Temperature programmed desorption studies confirmed that the N2 molecules preferred to adsorb onto the NiS/CdS surface. Linear sweep voltammetry results revealed that the overpotential of the N2 reduction reaction was reduced by loading NiS. Furthermore, transient photocurrent and electrochemical impedance spectroscopy indicated that the charge separation was enhanced by introducing NiS. Photocatalytic N2 fixation was carried out in the presence of the catalyst dispersed in water without any sacrificial agent. As a result, 1.0 wt% NiS/CdS achieved an ammonia production rate of 2.8 and 1.7 mg L-1 for the first hour under full spectrum and visible light (λ > 420 nm), respectively. The catalyst demonstrated apparent quantum efficiencies of 0.76%, 0.39% and 0.09% at 420, 475 and 520 nm, respectively. This study provides a new method to promote the photocatalytic efficiency of N2 fixation.

Precisely controlled synthesis of α-/ß-MnO2 materials by adding Zn(acac)2 as a phase transformation-inducing agent.

In this paper, we present an approach for the precisely controlled phase transformation of MnO2 in order to synthesise different compositions of α-/ß-MnO2 materials, by adding a trace amount of Zn(acac)2 as the phase transformation-inducing agent in a hydrothermal reaction. The single-atomic dispersion of Zn might reduce the barrier of phase transformation of δ-MnO2 to ß-MnO2. The ratio of the Zn species present in the single-atomic dispersions and nanoclusters might dominate the generation of α-MnO2 and ß-MnO2. The results of the oxygen reduction reactions indicate that the MnO2 materials have potential applications as promising catalysts in electrochemical catalysis.

Improvement Effect of Water-Based Organic Polymer on the Strength Properties of Fiber Glass Reinforced Sand.

The mechanical properties of sandy soil can be effectively improved by the incorporation of water-based polymer and glass fibers. In order to study the reinforcement effects of a type of water-based organic polymer and fiber glass on sand, three strength tests (unconfined compression test, direct shear test and tensile test) and scanning electron microscopy were carried out. A series of polymer content, fiber content and dry density were selected for the tests. The results revealed that the composite reinforcement of water-based organic polymer and fiber glass can improve the strength. With an increase in polymer content and fiber content, the unconfined compression strength, the cohesion, and the tensile strength increase. The internal friction angles maintain a relatively stable state. All three strength properties increase with an increase in dry density. The results can be considered as the reference for sand reinforced engineering.

Surface Defects Enhanced Visible Light Photocatalytic H2 Production for Zn-Cd-S Solid Solution.

In order to investigate the defect effect on photocatalytic performance of the visible light photocatalyst, Zn-Cd-S solid solution with surface defects is prepared in the hydrazine hydrate. X-ray photoelectron spectra and photoluminescence results confirm the existence of defects, such as sulfur vacancies, interstitial metal, and Zn and Cd in the low valence state on the top surface of solid solutions. The surface defects can be effectively removed by treating with sulfur vapor. The solid solution with surface defect exhibits a narrower band gap, wider light absorption range, and better photocatalytic perfomance. The optimized solid solution with defects exhibits 571 µmol h(-1) for 50 mg photocatalyst without loading Pt as cocatalyst under visible light irradiation, which is fourfold better than that of sulfur vapor treated samples. The wavelength dependence of photocatalytic activity discloses that the enhancement happens at each wavelength within the whole absorption range. The theoretical calculation shows that the surface defects induce the conduction band minimum and valence band maximum shift downward and upward, respectively. This constructs a type I junction between bulk and surface of solid solution, which promotes the migration of photogenerated charges toward the surface of nanostructure and leads to enhanced photocatalytic activity. Thus a new method to construct highly efficient visible light photocatalysts is opened.

The interactions of oxygen with small gold clusters on nitrogen-doped graphene.

By means of density functional theory, the adsorption properties of O2 molecule on both isolated and N-graphene supported gold clusters have been studied. The N-graphene is modeled by a C65NH22 cluster of finite size. The results indicate that the catalytic activity and the O2 adsorption energies of odd-numbered Au clusters are larger than those of adjacent even-numbered ones. The O2 molecule is in favor of bonding to the bridge sites of odd-numbered Au clusters, whereas for odd-numbered ones, the end-on adsorption mode is favored. The perpendicular adsorption orientation on N-graphene is preferred than the parallel one for Au2, Au3 and Au4 clusters, while for Au5, Au6 and Au7, the parallel ones are favored. When O2 is adsorbed on N-graphene supported Au clusters, the adsorption energies are largely increased compared with those on gas-phase ones. The increased adsorption energies would significantly facilitate the electron transfer from Au d-orbital to π* orbital of O2, which would further weakening the O-O bond and therefore enhancing the catalytic activity. The carbon atoms on N-graphene could anchor the clusters, which could make them more difficult to structural distortion, therefore enhance their stability.