Overview
Professor Dalton joined Duke University in August 2022 after obtaining her PhD from North Carolina State University, MS and BS from West Virginia University. Dr. Dalton is an experimentalist and her research interests include studying and understanding reactive, multiphase transport in porous media. She is particularly interested in understanding and manipulating chemical and physical processes that occur during reactive, multiphase transport in both engineered (cementitious) and natural (geological) porous materials. To this end, she uses quantitative imaging approaches such as X-ray computed tomography (CT), neutron tomography, and electrical imaging modalities including electrical capacitance tomography (ECT). She is interested in using hybrid and simultaneous imaging modalities because complementary and temporal information can be obtained using these approaches to better understand complex processes such as developing innovative methods to sequester CO2.
Current Duke Appointments & Affiliations
Recent Scholarly Works
Combined FI-SAM-Micro-CT analysis of entrained air void systems in processed and blended fly ash concrete
Journal article Construction and Building Materials · September 5, 2026 Motivated by the dwindling supply of high-quality fly ash (FA) and the growing interest to use harvested coal combustion ash (CCA), this research investigates how a processed Class F fly ash blended with Class C fly ash compares to a blend of standard Clas ... Full text CiteEcosystem technology (ecotech): Harnessing natural processes to address global challenges.
Journal article Science advances · May 2026 Over the past 80 years, biotechnology has advanced agriculture, health care, and economic development by harnessing biological processes from the organism inward, i.e., from the organ system to the molecular scale. Today's global challenges, including biod ... Full text CiteLearning latent hardening: enhancing deep learning with domain knowledge for material inverse problems.
Journal article Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · September 2025 Advancements in deep learning (DL) and machine learning (ML) have improved the ability to model complex, nonlinear relationships, such as those encountered in complex material inverse problems. However, the effectiveness of these methods often depends on l ... Full text CiteRecent Grants
Developing Composite Concrete Formulations for Thermal Energy Storage
ResearchPrincipal Investigator · Awarded by Electric Power Research Institute · 2026 - 2026View All Grants