Modulating
the morphologies of transition metal carbides (TMCs)
in situ in gas–solid reactions to improve catalytic performance
remains a major challenge. Herein, we present a mechanism for manipulating
prismatic and spherical Co2C by altering the surface energy
and crystal growth rate by influencing the generation and amount of
carboxylate species on hollow cubic Co3O4 (without
Mn). Co2C nanoprisms delivered an excellent activity in
reverse water gas shift (RWGS) at 270 °C, where CO2 conversion was close to thermodynamic limitations at a space velocity
of 60 000 mL gcat
–1 h–1. Furthermore, it showed a bifunctional effect that bridged RWGS
and Fischer–Tropsch synthesis reactions, allowing for the direct
synthesis of olefins and alcohols (C2+OH/ROH fraction of
98.4%, 4.3 mmol g–1 h–1) by adjusting
reaction conditions. The catalytic performance of Co2C
nanoprisms was linked to (020) and (101) surfaces with high activity
as well as double reaction pathways (redox and formate routes) through
reaction mechanism and kinetics studies. This investigation provides
a method for designing and modulating morphologies of TMCs and exhibits
great potential for bridging RWGS and sequent cascade reactions.
The demand for renewable
and environmentally friendly energy sources
has attracted extensive research on high-performance catalysts. Ferroelectrics,
a class of materials with switchable polarization, are unique and
promising catalyst candidates due to the significant effects of polarization
on surface chemistry and physics. The band bending at the ferroelectric/semiconductor
interface induced by the polarization flip promotes charge separation
and transfer, thereby enhancing the photocatalytic performance. More
importantly, the reactants can be selectively adsorbed on the surface
of ferroelectric materials depending on the polarization direction,
which can effectively lift the basic limitations as imposed by Sabatier’s
principle on catalytic activity. This Review summarizes the latest
developments of ferroelectric materials and introduces ferroelectric-related
catalytic applications. The possible research directions of 2D ferroelectric
materials in chemical catalysis are discussed at the end. The Review
is expected to inspire extensive research interests from physical,
chemical, and materials science communities.
To enhance the efficiency of antenna optimization, surrogate model methods can usually be used to replace the full-wave electromagnetic simulation software. Broad learning system (BLS), as an emerging network with strong extraction ability and remarkable computational efficiency, has revolutionized the conventional artificial intelligence (AI) methods and overcome the shortcoming of excessive time-consuming training process in deep learning (DL). However, it is difficult to model the regression relationship between input and output variables in the electromagnetic field with the unsatisfactory fitting capability of the original BLS. In order to further improve the performance of the model and speed up the design of microwave components to achieve more accurate prediction of hard-to-measure quality variables through easy-to-measure parameter variables, the conception of auto-context (AC) for the regression scenario is proposed in this paper, using the current BLS training results as the prior knowledge, which are taken as the context information and combined with the original inputs as new inputs for further training. Based on the previous prediction results, AC learns an iterated low-level and context model and then iterates to approach the ground truth, which is very general and easy to implement. Three antenna examples, including rectangular microstrip antenna (RMSA), circular MSA (CMSA), and printed dipole antenna (PDA), and 10 UCI regression datasets are employed to verify the effectiveness of the proposed model.
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