Chen, Xinyu | Engineering Papers

DOE OSTI2025

Photocatalytic Methanol Dehydrogenation with Switchable Selectivity

Switchable selectivity achieved by altering reaction conditions within the same photocatalytic system offers great advantages for sustainable chemical transformations and renewable energy conversion. In this study, we investigate an efficient photocatalytic methanol dehydrogenation with controlled selectivity by varying the concentration of nickel cocatalyst, using zinc indium sulfide nanocrystals as a semiconductor photocatalyst, which enables the production of either formaldehyde or ethylene glycol with high selectivity. Control experiments revealed that formaldehyde is initially generated and can either serve as a terminal product or intermediate in producing ethylene glycol, depending on the nickel concentration in the solution. Mechanistic studies suggest a unique role of ionic nickel as an additional photoelectron competitor that can significantly influence selectivity, alongside its well-established function as a hydrogen evolution reaction cocatalyst under photocatalytic conditions. The demonstrated switchable selectivity provides a new tool for producing diverse products from methanol, while advancing the understanding of cocatalyst behavior for versatile catalytic performance.

Luo, Jie↗

DOE OSTI2023

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

The goal of influence maximization is to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. The problem has a number of important applications such as viral marketing, information spread, and epidemic control. Although computing optimal seed set is NP-Hard, due to the submodular nature of the problem efficient approximation algorithms exist. However, even state-of-the-art parallel implementations are limited by a sampling step that incurs large memory footprints. This in turn limits the problem size reach and approximation quality. In this work, we study the memory footprint of the sampling process collecting reverse reachability information in the IMM algorithm over large real-world social networks. We present an adaptive and memory-efficient optimization approach for a state-of-the-art multi-threaded parallel influence maximization algorithm. Our approach,HuffMax, uses a portion of the reverse reachable (RR) sets collected by the algorithm to learn the characteristics of the graph. Then, it compresses the intermediate reverse reachability information with Huffman coding, and queries directly on the compressed data to preserve the memory savings obtained through compression. We also propose an efficient sampling strategy based on the distribution of RR sets, which can further reduce the computation time for typical social networks with long-tail distributions. Considering a NUMA architecture, we scale up our solution on 128-core CPUs and reduce the memory footprint by up to 45.7% with negligible time overhead (or even faster) and without perceivable loss of accuracy.

Chen, Xinyu↗