Influence of preparation conditions on catalytic activity and stability of platinum on alumina catalysts in methane oxidation

Nartova, Anna V.; Kovtunova, Larisa M.; Khudorozhkov, Alexander K.; Шефер, К. И.; Shterk, Genrikh; Kvon, Ren I.; Bukhtiyarov, Valerii I.

doi:10.1016/j.apcata.2018.08.029

Cited by 15 publications

(13 citation statements)

References 22 publications

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“…Active catalyst components are often nanoparticles where the catalytic reaction occurs and reaction products are formed. One of the main characteristics of catalytic activity is the “turnover frequency” of reaction (TOF), defined as the amount of product obtained at one active center per time unit [ 1 ]. To calculate TOF, it is necessary to know the particle’s parameters (amount, size, coverage of the surface, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…To calculate TOF, it is necessary to know the particle’s parameters (amount, size, coverage of the surface, etc.). A variety of physical and chemical research methods are currently used to study the characteristics of catalysts e.g., electron microscopy, specific surface analysis, scanning probe microscopy (STM, atomic force microscopy) [ 1 , 2 ]. As a rule, calculating TOF requires processing data on hundreds of particles from several points of the catalyst, the more the better.…”

Section: Introductionmentioning

confidence: 99%

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Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

et al. 2020

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Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87–0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service “ParticlesNN” based on the trained neural network, which can be used by any researcher in the world.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

et al. 2020

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“…The redox-active perovskite materials along with their very large specific surface areas likely lead to a high concentration of ionic defects on the surface, which can create ideal sites for the binding of Pt NPs as well as the adsorption of reactant species. ,,,, Therefore, in this study, the synthesized mesoporous LSTF was used as an active support for Pt NPs. Pt particles (0.8 wt %) surrounded by cationic surfactants were uniformly dispersed by electrostatic attraction on the negatively charged LSTF-40 surface in an aqueous solution.…”

Section: Resultsmentioning

confidence: 99%

“…Supported noble metals, which combine the superior activity of noble-metal nanoparticles (NPs) with the stability and cost effectiveness of oxide supports, are currently employed as industry-standard catalytic systems. Metal–support interactions in these systems usually lead to a dramatic improvement of the catalytic properties from both standalone constituents, i.e., noble-metal nanoparticles and the reducible oxide. − Improved reactivity due to the electronic and ionic charge carrier transfer between the metal and support, − stabilization of the oxidation state of the active phase, ,− and sintering resistance − are reported for numerous supported metal catalysts.…”

Section: Introductionmentioning

confidence: 99%

Pt Nanoparticles Supported on a Mesoporous (La,Sr)(Ti,Fe)O_3−δ Solid Solution for the Catalytic Oxidation of CO and CH₄

Kayaalp

Lee

Nodari

et al. 2020

ACS Appl. Nano Mater.

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The synthesis of a mesoporous (La,Sr)(Ti,Fe)O3−δ (LSTF) solid solution and its application as a catalytic support is presented. A mesoporous nanostructured perovskite solid solution with a tunable specific surface area and pore volume of up to 201 m2 g–1 and 0.411 cm3 g–1, respectively, was obtained by a sol–gel-like SiO2 endotemplating approach. Very high porosity combined with the redox-active characteristics of the perovskite structure makes these systems effective supports for Pt nanoparticle (NP) catalysts. As a result, when 0.8 wt % Pt NPs was introduced, the mesoporous LSTF showed improved CO and CH4 oxidation rates compared to commercially available SiO2 or CeO2. These observations demonstrate the promotional effects between Pt and LSTF and suggest an efficient strategy for achieving active support materials toward oxidation catalysis.

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“…Advanced microscopy techniques, such as transmission electron microscopy (TEM), are widely used in modern materials science, and in particularly in catalysis. In heterogeneous catalysis, the catalyst usually consists of an active component deposited as nanoparticles on a support [1][2][3][4][5][6]. The active component and porous supports can be of different nature but are commonly well suited to electron microscopy experiments [1,3,5].…”

Section: Introductionmentioning

confidence: 99%

Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications

et al. 2022

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Recognition and measuring particles on microscopy images is an important part of many scientific studies, including catalytic investigations. In this paper, we present the results of the application of deep learning to the automated recognition of nanoparticles deposited on porous supports (heterogeneous catalysts) on images obtained by transmission electron microscopy (TEM). The Cascade Mask-RCNN neural network was used. During the training, two types of objects were labeled on raw TEM images of ‘real’ catalysts: visible particles and overlapping particle projections. The trained neural network recognized nanoparticles in the test dataset with 0.71 precision and 0.72 recall for both classes of objects and 0.84 precision and 0.79 recall for visible particles. The developed model is integrated into the open-access web service ‘ParticlesNN’, which can be used by any researcher in the world. Instead of hours, TEM data processing per one image analysis is reduced to a maximum of a couple of minutes and the divergence of mean particle size determination is approximately 2% compared to manual analysis. The proposed tool encourages accelerating catalytic research and improving the objectivity and accuracy of analysis.

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Influence of preparation conditions on catalytic activity and stability of platinum on alumina catalysts in methane oxidation

Cited by 15 publications

References 22 publications

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

Pt Nanoparticles Supported on a Mesoporous (La,Sr)(Ti,Fe)O_3−δ Solid Solution for the Catalytic Oxidation of CO and CH₄

Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications

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Influence of preparation conditions on catalytic activity and stability of platinum on alumina catalysts in methane oxidation

Cited by 15 publications

References 22 publications

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

Pt Nanoparticles Supported on a Mesoporous (La,Sr)(Ti,Fe)O3−δ Solid Solution for the Catalytic Oxidation of CO and CH4

Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications

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Pt Nanoparticles Supported on a Mesoporous (La,Sr)(Ti,Fe)O_3−δ Solid Solution for the Catalytic Oxidation of CO and CH₄