The catalytic performances for soot purification over the perovskite-type
ABO3 oxides, as one of the most potential non-noble metal
catalysts, are closely correlated with the substitution of A-site
and B-site ions. Herein, three-dimensional ordered macroporous (3DOM)
structural catalysts of double perovskite-type La2–x
K
x
NiCoO6 were
prepared by a method of colloidal crystal template. The contact efficiency
between the catalyst and soot particles is significantly promoted
by the 3DOM structure, and the partial substitution of A-site (La)
with low-valence potassium (K) ions in La2–x
K
x
NiCoO6 catalysts boosts
the increasing surface density of coordinatively unsaturated active
B-sites (Co and Ni) and active oxygen. 3DOM La2–x
K
x
NiCoO6 catalysts
exhibited superior performance during the purification of soot particles,
and the 3DOM La1.80K0.20NiCoO6 catalyst
exhibited the highest activity, that is, the values of T
50, S
CO2, and turnover frequency
are 346 °C, 99.3%, and 0.204 h–1 (at 300 °C),
respectively. According to the results of multiple experimental characterizations
and density functional theory calculations, the mechanism of the samples
during soot removal is proposed: the increase in surface oxygen density
induced by the doping of K ions significantly promotes the critical
step of the oxidation from NO to NO2 in catalyzing soot
purification. It is one new strategy to develop the high-efficient
non-noble metal catalysts for soot purification in practical application.
Taking the development plans of an offshore oilfield as an example, a new comprehensive evaluation method, the improved Grey Clustering Analysis based on the cloud model (GCAC), is presented in this paper, which takes the ambiguity, randomness, and uncertainty of data into account and overcomes the limits of the general methods, such as subjective prejudice and objective randomness. GCAC converts the data of the oilfield development plans into a cloud, which considers the data of fuzziness, randomness, and the relationship between them. The grey membership degree of each development plan is calculated by this cloud model and an improved grey whitened function is presented in this paper. Then the plans are reordered by their grey membership degrees. In order to make more reasonable consideration of the artificial or unartificial uncertainties, GCAC combines the Grey Entropy Weighting method, Analytical Hierarchy Process (AHP), and Expert Assessment method to determine the weights of each level of indexes, which makes the weights more reasonable and reduces the randomness and the fuzziness of data. GCAC can help obtain a better comparison between the development plans. The reliability of this method is verified by the calculation results.
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