Atomic, Molecular and Hybrid Oxygen Structures on Silver

Structure and Reactivity of Active Oxygen Species on Silver Surfaces for Ethylene Epoxidation

The epoxidation of ethylene stands as one of the most important industrial catalytic reactions, and silver-based catalysts show superior activity and selectivity. Oxygen is activated on the surface of silver during the reaction and exerts a substantial impact on product selectivity. Notably, the oxygen species residing in the topmost atomic layers profoundly influence the reactivity of a catalyst. However, their characterization under in situ reaction conditions remains a huge challenge, and specific structures have not been identified yet. In this study, we employ in situ X-ray photoelectron spectroscopy and density functional theory calculations to determine the oxygen species formed at the topmost atomic layers of a silver foil and to assign them a structure. Three different groups of oxygen species activated on silver are identified: (i) surface lattice oxygen and two oxygen species originating from associatively adsorbed dioxygen and (ii) top and (iii) subsurface oxygen. Transient in situ photoelectron spectroscopy experiments are carried out to reveal the dynamic evolution and thus reactivity of the different oxygen species under ethylene epoxidation reaction environments. The top oxygen atom from the adsorbed associated dioxygen is the most active. Meanwhile, a frequency-selective data analysis method, developed to process time-resolved data, provides insights into the evolving trends of peak intensities for different oxygen species. The versatility of this method suggests its potential application in future time-resolved characterization studies.

Revealing the Nature of Active Oxygen Species and Reaction Mechanism of Ethylene Epoxidation by Supported Ag/α-Al2O3 Catalysts

The oxygen species on Ag catalysts and reaction mechanisms for ethylene epoxidation and ethylene combustion continue to be debated in the literature despite decades of investigation. Fundamental details of ethylene oxidation by supported Ag/α-Al2O3 catalysts were revealed with the application of high-angle annular dark-field-scanning transmission electron microscopy-energy-dispersive X-ray spectroscopy (HAADF-STEM-EDS), in situ techniques (Raman, UV-vis, X-ray diffraction (XRD), HS-LEIS), chemical probes (C2H4-TPSR and C2H4 + O2-TPSR), and steady-state ethylene oxidation and SSITKA (16O2 → 18O2 switch) studies. The Ag nanoparticles are found to carry a considerable amount of oxygen after the reaction. Density functional theory (DFT) calculations indicate the oxidative reconstructed p(4 × 4)-O-Ag(111) surface is stable relative to metallic Ag(111) under the relevant reaction environment. Multiple configurations of reactive oxygen species are present, and their relevant concentrations depend on treatment conditions. Selective ethylene oxidation to EO proceeds with surface Ag4-O2* species (dioxygen species occupying an oxygen site on a p(4 × 4)-O-Ag(111) surface) only present after strong oxidation of Ag. These experimental findings are strongly supported by the associated DFT calculations. Ethylene epoxidation proceeds via a Langmuir-Hinshelwood mechanism, and ethylene combustion proceeds via combined Langmuir-Hinshelwood (predominant) and Mars-van Krevelen (minor) mechanisms.

Remote sensing traffic scene retrieval based on learning control algorithm for robot multimodal sensing information fusion and human-machine interaction and collaboration

In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning.

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots

With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles. We position our research within the broader context of robotics, emphasizing the fusion of information and decision-making capabilities. The introduction of attention mechanisms enables vehicles to perceive the environment more accurately by focusing on important information and making informed decisions in complex scenarios. By inputting multimodal information, such as images and LiDAR data, into the attention mechanism module, the system can automatically learn and weigh crucial environmental features, thereby placing greater emphasis on key information during obstacle avoidance decisions. Additionally, we leverage the end-to-end architecture and draw from classical theories and algorithms in the field of robotics to enhance the perception and decision-making abilities of autonomous driving vehicles. Furthermore, we address the optimization of path planning using attention mechanisms. We transform the vehicle's navigation task into a sequential decision-making problem and employ LSTM (Long Short-Term Memory) models to handle dynamic navigation in varying environments. By applying attention mechanisms to weigh key points along the navigation path, the vehicle can flexibly select the optimal route and dynamically adjust it based on real-time conditions. Finally, we conducted extensive experimental evaluations and software experiments on the proposed end-to-end architecture on real road datasets. The method effectively avoids obstacles, adheres to traffic rules, and achieves stable, safe, and efficient autonomous driving in diverse road scenarios. This research provides an effective solution for optimizing obstacle avoidance and path planning in the field of autonomous driving. Moreover, it contributes to the advancement and practical applications of multimodal information fusion in navigation, localization, and human-robot interaction.

Room temperature epoxidation of ethylene over delafossite-based AgNiO2 nanoparticles

A mixed oxide of silver and nickel AgNiO2 was obtained via co-precipitation in alkaline medium. This oxide demonstrates room temperature activity in the reaction of ethylene epoxidation with a high selectivity (up to 70%). Using the PDF method, it was found that the initial structure of AgNiO2 contains stacking faults and silver vacancies, which cause the nonstoichiometry of the oxide (Ag/Ni < 1). It has been established that on the initial surface of AgNiO2 oxide, silver state can be considered as an intermediate between Ag2O and Ag0 (i.e. Agδ+-like), while nickel is characterized by signs of a deeply oxidized state (Ni3+-like). The interaction of AgNiO2 with C2H4 at room temperature leads to the simultaneous removal of two oxygen species with Eb(O 1s) = 529.0 eV and 530.5 eV considered as nucleophilic and electrophilic oxygen states, respectively. Nucleophilic oxygen was attributed to the lattice oxygen (Ag-O-Ni), while the electrophilic species with epoxidation activity was associated with the weakly bound oxygen stabilized on the surface. According to the TPR-C2H4 data, a large number of weakly bound oxygen species were found on the pristine AgNiO2 surface. The removal of such species at room temperature didn't result in noticeable structural transformation of delafossite. As the temperature of ethylene oxidation over AgNiO2 increased, the appearance of Ag0 particles was first observed below 200 °C followed by the complete destruction of the delafossite structure at higher temperatures.

Degradation and fate of 2,4-dinitroanisole (DNAN) and its intermediates treated with Mg/Cu bimetal: Surface examination with XAS, DFT, and LDI-MS

A novel Mg-based bimetal reagent (Mg/Cu) was used as an enhanced reductive system to degrade insensitive munition 2,4-dinitroanisole (DNAN), a contaminant found in energetic-laden waste. Degradation of DNAN was significantly impacted by dissolved oxygen and studied in anoxic and oxic bimetal systems (i.e., purging with N₂, air, or O₂ gas). Degradation occurred through sequential nitroreduction: first one nitro group was reduced (ortho or para) to form short-lived intermediates 2-amino-4-nitroanisole or 4-amino-2-nitroanisole (2-ANAN or 4-ANAN), and then subsequent reduction of the other nitro group formed 2,4-diaminoanisole (DAAN). The nitro-amino intermediates demonstrated regioselective reduction in the ortho position to 2-ANAN; Regioselectivity was also impacted by the anoxic/oxic environment. Under O₂-purging DNAN degradation rate was slightly enhanced, but most notably O₂ significantly accelerated DAAN generation. DAAN also further degraded only in the oxygenated Mg/Cu system. Adsorption of DNAN byproducts to the reagent occurred regardless of anoxic/oxic condition, resulting in a partition of carbon mass between the adsorbed phase (27%-35%) and dissolved phase (59%-72%). Additional surface techniques were applied to investigate contaminant interaction with Cu. Density functional theory (DFT) calculations identified preferential adsorption structures for DNAN on Cu with binding through two O atoms of one or both nitro groups. X-ray absorption spectroscopy (XAS) measurements determined the oxidation state of catalytic metal Cu and formation of a Cu-O-N bond during treatment. Laser desorption ionization mass spectrometry (LDI-MS) measurements also identified intermediate 2-ANAN adsorbed to the bimetal surface.

Silver Is Not Equal to Silver: Synthesis and Evaluation of Silver Nanoparticles with Low Biological Activity, and Their Incorporation into C12Alanine-Based Hydrogel

A new type of silver nanoparticles (AgNPs) was prepared and comprehensively studied. Scanning electron microscopy (SEM) and dynamic light scattering (DLS) analyses indicated that 24 nm AgNPs with narrow size distribution were obtained while Z-potential confirms their good stability. The composites of the obtained AgNPs with nontoxic-nature-inspired hydrogel were formed upon cooling of the aqueous solution AgNPs and C₁₂Ala. The thermal gravimetric analysis (TGA) and the differential scanning calorimetry (DSC) do not show significant shifts in the characteristic temperature peaks for pure and silver-enriched gels, which indicates that AgNPs do not strongly interact with C₁₂Ala fibers, which was also confirmed by SEM. Both AgNPs alone and in the assembly with the gelator C₁₂Ala were almost biologically passive against bacteria, fungus, cancer, and nontumor human cells, as well as zebra-fish embryos. These studies proved that the new inactive AgNPs-doped hydrogels have potential for the application in therapy as drug delivery media.

Spectroscopic observation and structure-insensitivity of hydroxyls on gold

The O-H stretching vibration of surface hydroxyls remained at 3691 cm^-1 for gold structures ranging in size from clusters to nanoparticles, to non-flat bulk surfaces. In contrast, this vibration was not observed on flat gold surfaces. Therefore, this vibration can serve as an indicator of the roughness of the gold surface and associated functional properties, such as catalytic activity.

Au-on-Ag nanostructure forin-situSERS monitoring of catalytic reactions

Dual-functionality Au-on-Ag nanostructures (AOA) were fabricated on a silicon substrate by first immobilizing citrate-reduced Ag nanoparticles (Ag NPs, ∼43 nm in diameter), followed by depositing ∼7 nm Au nanofilms (Au NFs) via thermal evaporation. Au NFs were introduced for their catalytic activity in concave-convex nano-configuration. Ag NPs underneath were used for their significant enhancement factor (EF) in surface-enhanced Raman scattering (SERS)-based measurements of analytes of interest. Rhodamine 6G (R6G) was utilized as the Raman-probe to evaluate the SERS sensitivity of AOA. The SERS EF of AOA is ∼37 times than that of Au NPs. Using reduction of 4-nitrothiophenol (4-NTP) by sodium borohydride (NaBH₄) as a model reaction, we demonstrated the robust catalytic activity of AOA as well as its capacity to continuously monitor via SERS the disappearance of reactant 4-NTP, emergence and disappearance of intermediate 4,4'-DMAB, and the appearance of product 4-ATP throughout the reduction process in real-time andin situ.