Engineering topics

Rogers, Matthew

Publications and source records attributed to Rogers, Matthew.

2 records

DOE OSTI2022

The Toughness of High-Strength Steel Weld Metals

Low-temperature phase transformation (LTPT) welding consumables are a new class of welding wires developed to mitigate hydrogen-induced cracking in the welding of high-strength steels without preheating or postweld heat treatment. LTPT weld metals have a high strength, but their toughness needs further investigation. LTPT weld metals predominately contain a martensite microstructure, which is necessary to achieve high strength; however, martensitic weld metals containing oxide inclusions have relatively poor toughness. For this study, three welding processes — gas metal arc welding (GMAW), gas tungsten arc welding (GTAW), and hot wire GTAW — were investigated. Optical microscopy, scanning electron microscopes, and transmission electron microscopes were employed for characterization. The role of the shielding gas in the formation of oxide inclusions in LTPT weld metals was investigated. The formation of oxide inclusions in the weld metals was related to the CO 2 in the shielding gas. When 100% Ar or a pure inert shielding gas mixture was used for all three welding processes, oxide inclusions were greatly reduced, and the weld metal toughness improved considerably, matching the base metal toughness. The mechanism by which inclusions promote fracture propagation in the weld metal was proposed.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗