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Sun, Yuting

Publications and source records attributed to Sun, Yuting.

Constructing a square-like copper cluster to boost C–C coupling for CO 2 electroreduction to ethylene

The CO 2 electroreduction reaction (CO 2 ER) to ethylene (C 2 H 4 ) offers the dual promise of lowering CO 2 emission while storing energy from renewable electricity, for which the development of highly efficient electrocatalysts is of great significance. Herein, by means of density functional theory (DFT) computations, we designed an electrocatalyst for CO 2 -to-C 2 H 4 conversion by anchoring a Cu 5 cluster supported on a MoS 2 monolayer with an S monovacancy (Cu 5 @MoS 2 ). Our results revealed that one Cu atom of the Cu 5 cluster was embedded into the framework of the defective MoS 2 monolayer, while the other four Cu atoms form a square-like island over the substrate surface. Interestingly, the C–C coupling between two *CO species can easily occur on the unique square-like active site with a low kinetic barrier of 0.56 eV to form the key *C 2 O 2 intermediate, which can then be hydrogenated to the C 2 H 4 product with a very low limiting potential (–0.32 eV). Significantly, alkaline conditions (pH = 13) are beneficial to further promote C 2 H 4 synthesis. Finally, our work may offer a new avenue to precisely modulate the structures of Cu clusters for converting CO 2 into high-value target products.

30 DIRECT ENERGY CONVERSION↗

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Abstract In a manufacturing system, production control‐related decision‐making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation‐based virtual testbed that implements dynamic optimization, automatic information exchange, and decision‐making from the process‐level, system‐level, and factory‐level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python‐based numerical computation program, discrete‐event simulation software (Simul8), and an optimization solver (CPLEX) via a third‐party master program. The application of this simulation‐based virtual testbed is illustrated by a case study in a machining shop.

Sun, Yuting↗