Choi, Minjun | Search NASA

DOE OSTI2024

Snowflake relocated Cu 2 O electrocatalyst on an Ag backbone template for the production of liquid C 2+ chemicals from CO 2

In this study, we performed the CO 2 reduction reaction (CO 2 RR) using a structural composite catalyst of cuprous oxide (Cu 2 O) and silver (Ag) that was simultaneously electrodeposited. While the underneath Ag electrodeposits maintained their spiky backbone structures even after the CO 2 RR, the Cu 2 O deposits were reduced to Cu(111) and relocated on the backbone template. Here, the structural changes in Cu 2 O to Cu increase the active area of the Cu–Ag interface, resulting in a remarkable production rate of 125.01 μmol h -1 of liquid C 2+ chemicals via the stabilization of the C–C coupling of the key intermediate species of acetaldehyde. This study provides new insights into designing a bimetallic catalyst for producing sustainable C 2+ products from CO 2 without any selectivity towards the production of methane.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing

Fusion energy experiments and simulations provide critical information needed to plan future fusion reactors. As next-generation devices like ITER move toward long-pulse experiments, analyses, including AI and ML, should be performed in a wide range of time and computing constraints, from near-real-time constraints, between-shot analysis, and to campaign-wide long-term analysis. However, the data volume, velocity, and variety make it extremely challenging for analyses using only local computational resources. Researchers need the ability to compose and execute workflows spanning edge resources to large-scale high-performance computing facilities.We present Delta, a system to address data analysis challenges, including AI/ML, in fusion science, by leveraging the ADIOS I/O library and middleware, to support executing science workflows over the wide area network for near-real-time streaming. We discuss the data federation challenges in performing remote workflows, focusing on on-going research work in (1) managing, reducing, and streaming data to minimize I/O and data movement overheads, (2) decompressing and reorganizing data for analysis, and (3) executing workflows for automated data analysis. We introduce examples for deep-learning based data analysis for the fusion domain and demonstrate how we use Delta to construct end-to-end workflows for a fusion device in Korea, connecting a remote DOE facility in the USA. The capability demonstrated by this project is the basis for improving the state of the art for near-real-time data federation amongst remote facilities.

Choi, Jong Youl↗