Jessica Anne Krogstad
· Associate ProfessorVerifiedUniversity of Illinois Urbana-Champaign · Materials Science and Engineering
Active 2011–2026
About
Jessica Anne Krogstad is an associate professor in the Department of Materials Science and Engineering at the University of Illinois, Urbana-Champaign. She received her BS with Honors in Materials Science and Engineering from the University of Illinois in 2007 and her PhD in Materials from the University of California, Santa Barbara in 2012, where her doctoral work explored phase evolution and structural stability in zirconia-based thermal barrier coatings. Following her PhD, she completed a postdoctoral appointment at Johns Hopkins University in the Department of Mechanical Engineering, focusing on high temperature metallic systems for MEMS applications and high temperature micro-mechanical testing for experimental validation of multi-scale damage models of superalloy and composite materials within the framework of integrated computational materials engineering (ICME). She joined the Department of Materials Science and Engineering at UIUC as an Assistant Professor in August 2014. Her current research explores the interplay between phase or morphological evolution and material functionality in structural materials under extreme conditions. Her work aims to understand how materials behave in nonequilibrium configurations and how their evolution can be harnessed to generate and optimize functionality for operation in dynamic and extreme environments. This research is vital for advancing technologies in transportation, communication, energy conversion, and other critical fields where materials are subjected to harsh conditions that induce significant changes in chemistry, scale, and morphology over time. Professor Krogstad has received several awards, including the DOE Early Career Award, the ACerS Robert L. Coble Award for Young Scholars, and the TMS Early Career Faculty Fellow Award. She has also contributed to teaching courses such as Statics, Advanced Mechanical Behavior, and Diffraction Physics for Materials, emphasizing her commitment to education in her field.
Research topics
- Materials science
- Nanotechnology
- Engineering physics
- Physical chemistry
- Physics
- Mathematics
- Composite material
- Thermodynamics
- Geometry
- Mechanical engineering
- Engineering
- Metallurgy
- Chemical physics
- Chemistry
Selected publications
SSRN Electronic Journal · 2026-01-01
preprintOpen accessRadiation Tolerance of Nanoporous Gadolinium Titanate
Journal of the American Ceramic Society · 2026-04-01
articleOpen accessSenior authorCorrespondingABSTRACT Defect sinks play a crucial role in reducing radiation‐induced damage accumulation, but their effectiveness varies. This study directly compares the efficacy of grain boundaries and pores (free surfaces) in gadolinium titanate (Gd 2 Ti 2 O 7 ) using in situ ion irradiation transmission electron microscopy (TEM). A unique sample configuration is developed, where each of the three distinct regions, each containing only one primary sink of interest, is in the same TEM lamella for simultaneous irradiation. Regular assessments of crystallinity in each region via nanobeam electron diffraction show that, at both room temperature and 600°C, free surface (pore) defect sinks are more effective than grain boundaries in capturing radiation‐induced defects and delaying the onset of amorphization. These findings have significant implications for further microstructural engineering and the design of radiation‐resistant microstructures.
Scripta Materialia · 2026-03-25
articleMgH2 thin films and nanowires deposited by CVD from a magnesium diamidodiboranate precursor
Materials Today Communications · 2026-01-01
articleOpen accessMagnesium hydride, MgH 2 , is deposited by chemical vapor deposition (CVD) at substrate temperatures of 140-300 °C from the newly synthesized magnesium diamidodiboranate precursor, Mg(NMe 2 BH 2 NMe 2 BH 3 ) 2 , abbreviated Mg(NBNB) 2 . The resulting films are pure, single-phase MgH 2 as evidenced by X-ray diffraction and Raman spectroscopy. Depending on the growth conditions, the morphology of the deposits can be varied from a granular film to islands to a continuous film with either protruding pillars or nanowires. The deposits are nanocrystalline and consist of pure, polycrystalline α-MgH 2 under all conditions. The formation of MgH 2 from Mg(NBNB) 2 is thought to involve a hydrogen elimination mechanism involving the BH 2 or BH 3 groups.
Minerals Engineering · 2026-04-07
articleOpen access• First demonstration of hydrogen plasma reduction of the lower grade iron ore, taconite. • Direct reduction of taconite without external heating or pretreatment steps. • Complete conversion of iron oxide phase in taconite to iron in as little as 45 s. • Reduction of iron oxide phase in taconite with no modification of silica impurity. • Role of plasma species elucidated via process conditions, diagnostics, and thermal controls. The depletion of high-grade iron ores such as hematite and magnetite motivates the application of low-grade iron ores to steelmaking. Here, we demonstrate the direct reduction of taconite by an atmospheric-pressure, microwave-powered hydrogen plasma. The ability of non-thermal plasmas to increase the reactivity of molecular hydrogen by creating species such as hydrogen radicals is highlighted by the suppressed reduction observed in our control thermal reduction (without plasma) experiments. The presence of the majority impurity, silica, is found to have a negligible effect on plasma reduction, and post-treatment analysis shows complete conversion of iron oxides to iron with little chemical or morphological change to the silica. Our study establishes for the first time reduction of taconite by a hydrogen plasma process which can increase rate, lower temperature, and avoid pretreatment steps.
Materials Science and Engineering A · 2025-12-31
articleOpen accessUnderstanding and predicting mechanical properties such as proof stress (yield strength), ultimate tensile strength, elongation to failure, and reduction in area are essential for screening the application of austenitic stainless steels in adverse chemomechanical environments. However, experimental determination of these properties is time-consuming, labor-intensive, and costly—especially under extreme conditions, which requires advanced experimental capabilities. In this study, we leverage a large-scale, curated dataset comprising 2180 experimental entries of austenitic alloys to develop machine learning (ML) models capable of predicting these key mechanical properties as functions of composition, solution treatment condition, and testing temperature . We systematically evaluate a range of ML algorithms, namely linear regression, kernel ridge regression, extreme gradient boosting, and artificial neural network. Among these, the extreme gradient boosting achieves the highest predictive accuracy, with R 2 scores of 0.946 and 0.985 for proof stress and ultimate tensile strength, respectively. To further enhance model performance, we explore ensemble learning and transfer learning strategies. The transfer learning approach that leverages interdependencies between mechanical properties reduces the mean percentage error by 21.0% and increases R 2 score by 3.1% in predicting reduction in area, compared to the original artificial neural network model. Our results show that ML models trained on well-structured experimental data can serve as an efficient and reliable tool for exploring the effect of composition on screening metrics. This work highlights the potential of data-driven approaches to accelerate the design and optimization of high-performance austenitic alloys. Furthermore, obtaining feature importance and insights from extreme gradient boosting using Shapley Additive Explanation tool provides valuable understanding of how features contribute to the prediction of mechanical properties. • Developed machine learning models to predict key mechanical properties of austenitic stainless steels as functions of compositions, solution treatment conditions, and testing temperature, leveraging 2180 curated experimental data points. • Achieved high predictive accuracy with R 2 scores of 0.95, 0.99, 0.90, and 0.88 for yield strength, ultimate tensile strength, elongation to failure, and reduction in area, respectively. • Implemented advanced ensemble and transfer learning techniques to further enhance predictive performance beyond conventional ML models. Transfer learning leveraging knowledge from ultimate tensile strength reduced the mean absolute percentage error in predicting reduction in area by 21% compared to the baseline Artificial Neural Network (ANN) model. • Demonstrated the capability of transfer learning to effectively transfer knowledge across related mechanical property domains. • Employed SHapley Additive exPlanations (SHAP) to interpret feature importance, identifying the roles of Nb, Ti, Mo, C, N, and B in influencing strength and ductility, consistent with metallurgical understanding. • Highlighted that the developed ML models successfully capture complex relationships between composition, processing, and mechanical properties, offering a pathway to accelerate austenitic alloy design.
ACS Sustainable Chemistry & Engineering · 2025-05-16 · 2 citations
articleMicrowave-powered, atmospheric-pressure plasmas have attracted attention to increase the reactivity of hydrogen for decarbonized reduction of iron oxide. However, the processes are often operated at high temperatures where reactions involve molecular hydrogen, in addition to any plasma-activated species such as atomic hydrogen. In this work, a plasma source was developed by coupling microwave radiation from a solid-state amplifier to an antenna surrounded by gas flow, to produce a free jet that enables treatment of a material surface at low temperatures (<500 °C). The surface temperature during plasma treatment was measured by infrared pyrometry, and control experiments confirmed that reduction by molecular hydrogen at these temperatures was kinetically suppressed. We thus were able to study the reduction of iron oxide at low temperature (∼280 to 500 °C) and the effect of various process conditions. The observed trends were understood in terms of the surface temperature and transport of the plasma-activated species, namely atomic hydrogen. Decoupling these various contributions enabled kinetic analysis and the extraction of an apparent activation energy of 50 kJ/mol for the overall reduction by atomic hydrogen at atmospheric pressure, free from molecular hydrogen and diffusional effects. The results show that reduction is enhanced by atomic hydrogen, but surface temperature continues to play a predominant role, which can guide low-temperature hydrogen plasma reduction of iron or other metal oxides for sustainable and on-demand production of critical resources such as steel.
Nanoscopic Imaging of Biogenic Feedstock-Induced Corrosion in Model Petroleum Infrastructure
ACS Nano · 2025-07-28 · 1 citations
articleBiofeedstocks derived from living organisms or their byproducts have recently emerged as an environmentally benign complement to petroleum, diversifying energy production in the petroleum industry from sole dependence on crude oil while utilizing mostly existing petroleum infrastructure. However, biofeedstocks also bring challenges as they can cause distinct and potentially more severe corrosion in metal-based petroleum infrastructure than crude oils due to their higher molecular oxygen content and the presence of various organic acids. To effectively manage such corrosion, it is crucial to understand the corrosion mechanism, particularly the onset of local corrosion, as well as its relationship with the metallic microstructure. Here, using pentanoic acid─a typical degradation product and representative corrosion contributor from biofeedstocks─as the corrosive medium, we capture the real-time initiation and progression in corrosion of carbon steel lamella, which is a model petroleum infrastructure, at nanometer resolution. We correlate in situ liquid-phase transmission electron microscopy imaging of the corrosion process with ex situ characterization of grain size, orientation, and elemental distribution. Through this correlative, multimodal characterization, we identify the key microstructural features that significantly influence corrosion behavior: galvanic corrosion initiates corrosion, strain accelerates corrosion, and lattice orientation guides corrosion propagation. Contrary to aqueous corrosion, corrosion in pentanoic acid is not heavily influenced by the grain boundaries, with similar rates observed in coarse- and fine-grain lamellae. Our observations highlight the importance of intrinsic structural features of carbon steel and their impact on corrosion in biofeedstock-based organic acids, providing insights for potential corrosion mitigation.
2025-03-10
preprintSenior authorMgH2 Thin Films and Nanowires Deposited by CVD from a Magnesium Diamidodiboranate Precursor
SSRN Electronic Journal · 2025-01-01
preprintOpen access
Recent grants
Towards Nanomanufacturing of Materials with Coherent Interfaces
NSF · $613k · 2018–2022
Frequent coauthors
- 36 shared
André Schleife
National Center for Supercomputing Applications
- 35 shared
Cecília Leal
Friedrich Schiller University Jena
- 30 shared
Matthew D. Goodman
University of Illinois Urbana-Champaign
- 27 shared
Pinshane Y. Huang
University of Illinois Urbana-Champaign
- 26 shared
Kisung Kang
University of Illinois Urbana-Champaign
- 10 shared
Kevin J. Hemker
Johns Hopkins University
- 9 shared
Nicola H. Perry
University of Illinois Urbana-Champaign
- 9 shared
Dallas R. Trinkle
University of Illinois Urbana-Champaign
Labs
Not provided
Education
- 2005
Ph.D., Materials Science & Engineering
University of Illinois Urbana-Champaign
- 2001
M.S., Materials Science & Engineering
University of Illinois Urbana-Champaign
- 1999
B.S., Materials Science & Engineering
University of Illinois Urbana-Champaign
Awards & honors
- DOE Early Career Award
- ACerS Robert L. Coble Award for Young Scholars
- TMS Early Career Faculty Fellow Award
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