Nanostellar, Inc.

Shubin, Inc.

Wgch Technology Limited

Juan Cai

Mats Larsson

Xianghong Hao

Cetin Kilic

Jifei Jia

Kyle L Fujdala

Jonathan W Woo

Ariana Zimbouski

Jangsuk Hyun

Ligen Wang

Timothy J Truex

Jian Wang

Chaitanya Sampara

An emission control catalyst includes copper-ceria to boost low temperature CO oxidation performance, generate exothermic heat during the process, and reduce HC and NOemissions. As a result, system performance is boosted at equal catalyst cost or maintained at a reduced catalyst cost. In one embodiment, an engine exhaust catalyst includes a first washcoat layer having at least one of a platinum-based catalyst, a palladium-based catalyst, and combinations thereof; and a second washcoat layer having copper-ceria.

Engine exhaust catalysts containing copper-ceria

An emission control catalyst that exhibits improved CO and HC reduction performance includes supported precious group metal catalysts that are coated onto different layers of the substrate for the emission control catalyst. Zeolites of one or more types are added to the emission control catalyst as a hydrocarbon absorbing component to boost the low temperature performance of the emission control catalyst. Y zeolite is used by itself or mixed with other zeolites to enhance hydrocarbon storage at low temperatures.

Engine exhaust catalysts containing palladium-gold

The oxygen content of metal species in a heterogeneous catalyst is determined using volumetric adsorption measurements. Such measurements are employed to quantify the amount of reduction gas that it takes to reduce metal species of a catalyst sample, and the oxygen content is derived from this amount and the reaction stoichiometry. This method can be applied to mono-metallic and multi-metallic heterogeneous catalysts and has been shown to provide at least 10 times better detection sensitivity than typical TCDs in TPR-TCD methods.

Determining oxygen content of metal species in heterogeneous catalysts

A catalyst sample may contain both small and large metal particle distributions simultaneously. Characterizing the properties of the metal particles contained in each distribution is important to help describe catalytic performance and optimize catalysts. Monte Carlo simulations and dispersion measurements are employed to determine the relationship between dispersion parameters of each metal particle distribution. Various properties, such as the atom fraction and the surface atom fraction of each distribution can be determined.

Characterization of metal particle distributions in a supported catalyst

Three-dimensional (3D) shapes of particles are characterized from a two-dimensional (2D) image of the particles that is obtained using TEM. The 3D shape characterization method includes the steps of obtaining a 2D image of a batch of nanoparticles, determining 2D shapes of the nanoparticles from the 2D image, and deriving six distributions, each of which corresponds to a 2D shape and a 3D shape associated with the 2D shape. The first size distribution is derived from the nanoparticles having the 2D triangle shape. The second and third size distributions are derived from the nanoparticles having the 2D tetragon shape. The fourth, fifth and sixth size distributions are derived from the nanoparticles having the 2D round shape. Based on these six size distributions, three size distributions, each of which corresponds to one of three 3D shape classes, are estimated. The size distributions corresponding to the 3D shape classes provide a better log-normal distribution than the size distributions corresponding to the 2D shapes.

Particle shape characterization from 2D images

A sample preparation method for characterization of nanoparticles embedded in the supports of heterogeneous catalysts, with improved particle dispersion, is introduced. The supported catalyst is first ground or milled into fine powder. Then, the powder is mixed into an organic solvent, and an etchant is added to the solvent to digest the supports and release metallic nanoparticles. The resulting solution is then placed in an ultrasonic bath where ultrasonic waves are generated and applied to the solution. The ultrasonic waves suppress agglomeration of the particles and also break up those particle clusters resulting from agglomeration during the prior steps. Subsequently, a sample is extracted from the solution and prepared for analysis.

Dissolving metal particles from supported catalyst using ultrasonic energy, solvent and etchant

The catalytic efficiency of supported catalysts containing metal nanoparticles is strongly related to the chemical softness at the surfaces of such nanoparticles. Supported catalysts containing platinum nanoparticles having average surface softness values (expressed in scaled units ranging from 0 to 1) between 0.07198 and 0.09247 exhibit high catalytic efficiency. The catalytic efficiency of such platinum nanoparticles for CO oxidation, expressed as the turn-over frequency (TOF), was observed to be on or above 0.03062 s. The supported catalysts containing platinum nanoparticles with tighter average surface softness ranges exhibit even higher catalytic efficiencies. The TOF for CO oxidation of platinum nanoparticles having average surface softness values between 0.08031 and 0.08679 was observed to be on or above 0.06554 s.

Supported catalysts having platinum particles

The catalytic efficiency of supported catalysts containing metal nanoparticles is strongly related to the chemical softness at the surfaces of such nanoparticles. The chemical softness of a nanoparticle is obtained using results from Density Functional Theory modeling, an extended version of Embedded Atom Method modeling, and continuum modeling based on size and shape of the nanoparticle. A metal nanoparticle of a certain size and shape is first modeled using the extended EAM and EAM parameters that have been validated with results from DFT modeling, to obtain atomic energy densities at each atom location. The chemical softness value at each atom location is then calculated from the atomic energy densities and various parameters that are derived based on results from DFT modeling. The surface chemical softness value is derived from the local chemical softness values based on the geometry and atomistic structure of the metal nanoparticle.

Method of estimating chemical reactivity of nanoparticles

The size distributions corresponding to the three-dimensional (3D) shapes of particles are estimated from a 3D-to-2D projection matrix and a two-dimensional (2D) image of the particles that is obtained using TEM. Two different methods to generate a 3D-to-2D projection matrix are described. The first method determines the matrix coefficients assuming equal probability for all high-symmetry projections. The second method employs a large set of 3D-to-2D projection matrices with randomly generated coefficients satisfying the high-symmetry projection constraint. The second method is a general method to determine 3D-to-2D projection matrices based on the assumption that certain high-symmetry projections are present for the system under investigation.

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Fremont, CA, United States of America

Juan Cai