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Xiong, Wei

Publications and source records attributed to Xiong, Wei.

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin

Bioactivity Profiling of Chemical Mixtures for Hazard Characterization

Abstract The assessment and regulation of chemical toxicity to protect human health and the environment are done one chemical at a time and seldom at environmentally relevant concentrations. However, chemicals are found in the environment as mixtures, and their toxicity is largely unknown. Understanding the hazard posed by chemicals within the mixture is critical to enforce protective measures. Here, we demonstrate the application of bioactivity profiling of environmental water samples using the sentinel and ecotoxicology model species Daphnia to reveal the biomolecular response induced by exposure to real-world mixtures. We exposed a Daphnia strain to 30 sampled waters of the Chaobai River and measured the gene expression response profiles. Using a multiblock correlation analysis, we establish correlations between chemical mixtures identified in 30 water samples with gene expression patterns induced by these chemical mixtures. We identified 80 metabolic pathways putatively activated by mixtures of inorganic ions, heavy metals, polycyclic aromatic hydrocarbons, industrial chemicals, and a set of biocides, pesticides, and pharmacologically active substances. Our data-driven approach discovered both known bioactivity signatures with previously described modes of action and new pathways linked to undiscovered potential hazards. This study demonstrates the feasibility of reducing the complexity of real-world mixture toxicity to characterize the biomolecular effects of a defined number of chemical components based on gene expression monitoring of the sentinel species Daphnia.

Engineering

Characterization of Oil and Gas Drill Cuttings for Critical Mineral Recovery and Reuse Potential as Soil Supplements

Expansion of unconventional oil and natural gas production over the last decade in the United States has resulted in record production of natural gas and oil in 2024. Millions of tons of drill cuttings generated from shale gas development are currently disposed of in landfills. Converting these cuttings into value product(s) such as critical minerals (CMs) and soil supplements, has the potential of reducing environmental impact of fossil fuel exploration. In this study we characterize the CM distribution and extractability from collected drill cuttings and core samples from major U.S. shale formations. A four-step sequential extraction, consisting of a sonicated soap step, sonicated EDTA step , mildly reducing agent, and a oxidizing agent, was developed to simultaneously extract CMs and convert the drill cuttings into soil supplements. Viability of converted drill cuttings as soil supplement was determined with a seedling growth experiment. Results show high concentrations of vanadium (V) (up to 1575 ppm, barite (up to 5 wt. %), and rare earth elements (REE) concentrations (up to 253 ppm). High extractability of selected critical minerals such as REE (19-50%) and Ba (10-58%) show promising results. Preliminary results show seedling growth in a mixture of converted drill cuttings with soil. These results show the potential for recovery of CMs from drill cuttings as an alternative domestic and the reduction of the environmental impact of fossil fuel production.

Barczok, Maximilian

Carbonated Brine Injection for a Pilot Site as a Low-Risk Geologic Carbon Storage Strategy: A Simulation Study

This technical report presents a preliminary study for CBI implementation in the Pena Creek pilot site in Dimmit County, Texas, targeting the Edwards Limestone Formation for SWD, in collaboration with NGL Energy Partners. The objective of this study is to find the conservative CO 2 molality for safe storage and potential risks for CBI at this pilot site. Geomodel was performed to get site-specific reservoir geology information. Reservoir modeling was performed to investigate dissolved CO 2 molality and pressure changes at relevant injection conditions. Wellbore corrosion and compatibility was discussed with corrosion modeling. Dissolved CO 2 long-term fate in the reservoir was examined with reactive transport modeling. This plan would set the scientific foundation as the initial step for future implementation in the field.

58 GEOSCIENCES

Carbonated Brine Injection Pilot Plan

This is a three-year effort to develop a plan for carbonated brine injection, with an end goal of a path for pilot scale implementation. Carbonated brine injection (CBI) strategy was planned for a potential pilot site through simulation study. Methods include geology model, reservoir modeling, wellbore materials compatibility and corrosion modeling, reactive transport modeling.

Xiong, Wei

Giant optical second- and third-order nonlinearities at a telecom wavelength

A material platform that excels in both optical second- and third-order nonlinearities at a telecom wavelength is theoretically and experimentally demonstrated. In this TiN-based coupled metallic quantum well structure, electronic subbands are engineered to support doubly resonant inter-subband transitions for an exceptionally high second-order nonlinearity and provide single-photon transitions for a remarkable third-order nonlinearity within the 1400–1600 nm bandwidth. The second-order susceptibility χ (2) reaches 2840 pm/V at 1440 nm, while the Kerr coefficient n 2 arrives at 2.8 × 10 −10 cm 2 /W at 1460 nm. The achievement of simultaneous strong second- and third-order nonlinearities in one material at a telecom wavelength creates opportunities for multi-functional advanced applications in the field of nonlinear optics.

Chen, Ching-Fu (ORCID:0000000262510722)