Spectroscopic observation and structure-insensitivity of hydroxyls on gold

Zheng, Yiteng; Qi, Yue; Tang, Ziyu; Tan, Junzhi; Koel, Bruce E.; Podkolzin, Simon G.

doi:10.1039/d2cc00283c

Cited by 9 publications

(8 citation statements)

References 31 publications

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“…These findings have important implications: first, the observed preferential partial oxidation at low-coordinated sites is consistent with results for selective ethanol oxidation on supported gold nanoparticles, suggesting that their effects are not limited to methanol oxidation on npAu alone. In methanol oxidation, these sites have significantly different selectivities not only for partial oxidation to MeFo but also for overoxidation, with low-coordinated sites favoring the former and lowering the latter, as desired for an ideal partial oxidation catalyst.…”

supporting

confidence: 80%

Partial Oxidation of Methanol on Gold: How Selectivity Is Steered by Low-Coordinated Sites

Eltayeb,

Carroll,

Dippel

et al. 2024

ACS Catal.

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Partial methanol oxidation proceeds with high selectivity to methyl formate (MeFo) on nanoporous gold (npAu) catalysts. As low-coordinated sites on npAu were suggested to affect the selectivity, we experimentally investigated their role in the isothermal selectivity for flat Au(111) and stepped Au(332) model surfaces using a molecular beam approach under well-defined conditions. Direct comparison shows that steps enhance desired MeFo formation and lower undesired overoxidation. DFT calculations reveal differences in oxygen distribution that enhance the barriers to overoxidation at steps. Thus, these results provide an atomic-level understanding of factors controlling the complex reaction network on gold catalysts, such as npAu.

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supporting

confidence: 80%

Partial Oxidation of Methanol on Gold: How Selectivity Is Steered by Low-Coordinated Sites

Eltayeb,

Carroll,

Dippel

et al. 2024

ACS Catal.

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“…After Mo deposition, the Pt–Mo catalyst was dried and calcined with the same procedure. Similar catalyst synthesis methods were used previously for studying other supported metal catalysts. ,− …”

Section: Experimental and Computational Methodsmentioning

confidence: 99%

“…The positions of the Pt atoms in the bottom two layers were constrained, simulating the bulk structure of Pt nanoparticles. Similar computational settings were previously used for studying adsorption and reactivity on multiple metallic catalytic surfaces. ,,− …”

Section: Experimental and Computational Methodsmentioning

confidence: 99%

Kinetics and Reaction Mechanisms of Acetic Acid Hydrodeoxygenation over Pt and Pt–Mo Catalysts

Zheng

Tang

et al. 2022

ACS Sustainable Chem. Eng.

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Kinetic measurements for silica-supported Pt and Pt−Mo catalysts were collected in vapor-phase acetic acid hydrodeoxygenation by varying the hydrogen partial pressure between 18 and 72 kPa and the acetic acid partial pressure between 7 and 18 kPa at 423−473 K. Under all testing conditions, the Pt− Mo catalyst was more active and selective. In addition, the apparent activation energy for Pt−Mo of 76 ± 3 kJ/mol was lower than that of 84 ± 4 kJ/mol for Pt. The apparent reaction orders were also different. The order in hydrogen of 0.8 ± 0.1 for Pt−Mo changed to zero at higher hydrogen partial pressures while that for Pt remained constant at 0.6 ± 0.1. A near-zero order in acetic acid for Pt−Mo changed to −2.1 at higher acetic acid pressures while that for Pt remained constant at −2.9 ± 0.3. These differences in reaction kinetics as well as in selectivity trends with changes in temperature and feed composition indicated a change in the reaction mechanism for Pt−Mo. The catalysts were characterized with hydrogen temperature programmed desorption, oxygen temperature programmed oxidation and transmission electron microscopy with energy-dispersed X-ray spectroscopy elemental mapping. Mo was present in the form of subnanometer-size clusters on the surface of Pt nanoparticles. Both Pt and Pt−Mo catalysts were stable under the reaction conditions for 10 h, and the size and structure of Pt and Pt−Mo particles remained mostly unchanged, without coke accumulation. Density functional theory calculations show that neighboring Pt−Mo surface atoms act as a single active site where Mo serves as a preferential binding anchor for O atoms. The presence of Mo changes the structure and reactivity of hydrocarbon surface species, leading to a change in the reaction mechanism. In contrast with Pt, C−O bond breaking reactions become more favorable on Pt−Mo and, conversely, C−C bond breaking reactions become less favorable on Pt−Mo, explaining the experimentally observed higher activities and selectivities of Pt−Mo.

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“…This is because the label information used in weakly supervised learning methods is relatively coarse and cannot provide precise object positions and bounding boxes. In applications that require high precision, weakly supervised learning methods may not achieve the desired results (Zheng et al, 2022 ). Customizing weak supervision strategies for different educational robot tasks and scenarios is necessary to fully leverage the auxiliary information during the training process.…”

Section: Related Workmentioning

confidence: 99%

Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning

Hong,

Dong,

Deng

et al. 2024

Front. Neurorobot.

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The development of education robots has brought tremendous potential and opportunities to the field of education. These intelligent machines can interact with students in classrooms and learning environments, providing personalized educational support. To enable education robots to fulfill their roles, they require accurate object detection capabilities to perceive and understand the surrounding environment of students, identify targets, and interact with them. Object detection in complex environments remains challenging, as classrooms or learning scenarios involve various objects, backgrounds, and lighting conditions. Improving the accuracy and efficiency of object detection is crucial for the development of education robots. This paper introduces the progress of an education robot's object detection based on a brain-inspired heuristic method, which integrates Faster R-CNN, YOLOv3, and semi-supervised learning. By combining the strengths of these three techniques, we can improve the accuracy and efficiency of object detection in education robot systems. In this work, we integrate two popular object detection algorithms: Faster R-CNN and YOLOv3. We conduct a series of experiments on the task of education robot object detection. The experimental results demonstrate that our proposed optimization algorithm significantly outperforms individual algorithms in terms of accuracy and real-time performance. Moreover, through semi-supervised learning, we achieve better performance with fewer labeled samples. This will provide education robots with more accurate perception capabilities, enabling better interaction with students and delivering personalized educational experiences. It will drive the development of the field of education robots, offering innovative and personalized solutions for education.

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Spectroscopic observation and structure-insensitivity of hydroxyls on gold

Abstract: 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...

Cited by 9 publications

References 31 publications

Partial Oxidation of Methanol on Gold: How Selectivity Is Steered by Low-Coordinated Sites

Partial Oxidation of Methanol on Gold: How Selectivity Is Steered by Low-Coordinated Sites

Kinetics and Reaction Mechanisms of Acetic Acid Hydrodeoxygenation over Pt and Pt–Mo Catalysts

Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning

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