Industrial Catalysis
1. Alkane dehydrogenation

●Rational design and fabrication of efficient Pt-based catalysts and alternative catalysts
●Determination of structure-performance relationship for designed catalysts using density functional theory (DFT) calculations and operando/in situ characterization techniques
●Scale-up production of dehydrogenation catalysts with well-defined structures
2. COx and Alkyne hydrogenation

●Rational design of interfacial and synergistic structures for catalysts
●Electronic interaction between oxide and support
●Identification of the reaction pathway and active sites in CO2 hydrogenation

●Encapsulation structure of catalysts for the activation and spillover of hydrogen
●Influence of electronic structures on the hydrogen activation process with higher catalytic performance
3. Chemical looping process

●Design of redox catalysts via structure engineering to upgrade light alkane for the production of value-added chemicals
●Determination of the active site for alkane activation and identification of active oxygen species for selective conversion or complete oxidation of alkanes by in situ techniques
●Kinetics of lattice oxygen for surface reaction and bulk oxygen migration
4. Reactor design and process optimization

●Development of multiphase modelling methods
●Construction of in-line measurement for multiphase reactors
●Process intensification via external fields
Photocatalysis
1. Hydrogen production via photoelectrochemical water splitting

●Hierarchical bottom-up approach to a new generation of photoanodes
●Fabrication of nanostructured photoanodes via reactive angle deposition
2. Fuel generation by photocatalytic CO2 reduction
●Investigation on the relationship between selectivity and CO2 reduction co-catalyst
●Surface modification of the photocatalysts to reduce the activation energy or overpotential for CO2 reduction
3. Well-controlled nanocatalysts for fuel cell-related reactions
Fuel cells are receiving extensive attention owing to their numerous advantages, including high efficiency, high specific energy density, and low pollution. The performance of a fuel cell is directly determined by the activity of its catalysts. Among various types of catalysts, the platinum-group metals (PGMs) are broadly used as the key catalysts in several kinds of fuel cells, such as proton exchange membrane fuel cell (PEMFC), direct formic acid fuel cell (DFAFC) and direct methanol fuel cell (DMFC). However, their low abundance in the earth’s crust and their ever increasing prices have created a major roadblock for the large-scale commercialization of PGMs-related fuel cells. We aimed to develop more active catalysts through tuning the utilization efficiency, exposed surfaces and composition of Pt- or Pd- based nanocrystals. The mechanism for relationship between the well-controlled nanocrystals and their performance in fuel cell-related electrocatalytic reactions are carefully investigated via DFT calculations. Current projects include:
●Shape-controlled synthesis of Pd-Au alloy with high-energy surfaces
●Preparation of Pt-based core-shell structure to achieve a high mass activity towards oxidation reduction reaction
●Rational design of Pt-free catalysts for oxidation reduction reaction
4. Selective oxidation of alcohols via photocatalysis
●Photocatalytic oxidation of benzyl alcohol over metal-oxides with expanded absorption range of irradiation
●Mechanism of photocatalytic oxidation over various photocatalysts
●Photocatalytic oxidation over oxides with oxidation and reduction cocatalysts which could improve the separation of electrons and holes.
Electrocatalysis
1. Electrocatalytic CO2 Reduction

●Investigation of electrocatalysts and manipulation of multi-physics fields within CO2 reduction devices
●Design of novel reactors to break mass transfer limitations for the industrialization of CO2 reduction systems
2. Electrochemical Production of Green Hydrogen
●Corrosion-resistant catalysts for the electrolysis of seawater
●Modulation of the growth and desorption behaviors of bubbles
Interfacial & Surface Catalysis
1. Mechanistic study by computational chemistry
Exploration of catalyst reaction mechanisms is of fundamental importance in improving known catalysts or designing new catalysts. In recent years, a great number of the catalytic processes have been computationally studied using Density Functional Theory (DFT) calculations and other relative methods. Along with the development of computational chemistry methods, parallel computing and high-performance computing cluster, state-of-the-art computational chemistry researches not only uncover the essence of a known catalytic process, but also are used as a fast and low-cost pre-screening technic to assist new catalyst design.Currently, we are focusing on following projects
i. Mechanistic research on propane dehydrogenation to propylene: In order to gain deeper understanding of dehydrogenations of saturated hydrocarbons, we selected propane dehydrogenation reactions as our model reactions, which was catalyzed by Pt based catalyst. To facilitate determination of systematic trends in the propane dehydrogenationon Pt based catalyst, we will first investigate propane dehydrogenation on flat and stepped Pt and PtM alloy surfaces as well as interfaces of metal oxides, including MgO, Al2O3, and TiO2. They were selected for initial study as supports with at least one surface which matches the shape and size of the Pt(100) or Pt(111) surface. In a practical sense, the matched lattice dimensions provide a more uniform geometry between systems, thus facilitating initial development of correlations and Brønsted–Evans–Polanyi (BEP) relationships at the metal/support interfaces. By systematically permuting the alloy metal and nature of the substrates, we will establish correlations and reactivity patterns, including the development of BEP relationships. In the future, the analysis can be extended to other supported transition metal catalysts, and "volcano" relationships can be constructed between the predicted activity of different metal alloy/support structures and key catalytic parameters that are identified through the analysis.
ii. Development of Global Optimization Algorithm: One challenge to build a realistic catalyst model is to determining its most stable structure (global minimum) or set of lowest energy structures of a catalyst under reaction conditions. Although local minimum optimization technics have already been well developed, it could not guarantee the optimized structure to be the most stable one in case with complicated environment, like high adsorbate coverage, surface reconstruction, etc. Ideally, the global minimum can be located by exploring the whole potential energy surface with conducting numbers of local optimizations. We are interested in improvement of global optimization efficiency by reducing the number of local optimizations to obtain the desired global minimum with advanced computational algorithms like genetic algorithm, machine learning.
iii. Electrocatalysis of CO2 reduction: The insights of catalytic structures and exploration of candidate catalysts in our group mainly also focus on (photo)electrocatalysis for CO2 reduction (CO2RR). For example, atomic structure motifs for product-specific active sites on OD-Cu catalysts OD-Cu are detected by the molecular dynamic simulation with neural network (NN) potential. As for metal oxide, the surface defect was utilized to detect the regulatory factor for the catalytic performance. In detail, surface defects can control the adsorption strength of key intermediates of CO2RR by surface hydroxyl (Cu2O) or oxygen vacancies (SnOx), thereby providing theoretical guidance for improving the selectivity of target products. Our further analysis would continue to explore the influence on the reaction mechanisms around the regulation of surface morphology, electronic structure and extend to the model construction consistent with the realistic environment.

iv. Machine learning potentials: Recently, the data-driven ML technique is emerging as a useful tool and surrogate model to accelerate the time-consuming simulation. Machine learning potentials (MLP), which directly learns the potential surface from ab initio calculations, have been developed to act as an energy calculator with high accuracy while maintaining the large speedup. Using MLP as a surrogate model, not only the thermodynamics for active sites, the evaluation of kinetic properties like reaction rate can also be accelerated. MLP can significantly extend the spatial and time scale of atomistic simulations, providing more opportunities to simulate large-scale catalyst systems while maintaining high accuracy. Besides, MLP can accelerate enhanced sampling simulations to predict the long-time-scale surface reaction using ab initio calculation. In our group, we tend to develop and apply MLP to accelerate the time-consuming, large-scale, and long-term simulation of catalytic system.
Batteries

●Other Li-ion battery anodes: Si, Bi, Li
●Li-ion battery cathodes: metal oxides, sulfur
●Li-ion battery electrolytes: esters, ethers, high concentration salts
●Zn-ion battery: Zn metal anodes and metal oxides cathodes, respectively