Equimolar CoCrNi is driven towards a long-range structure with transformation characteristics similar to that of a strain glass alloy due to the specific stoichiometry and applied aging conditions. This work illustrates the frustrated and kinetically arrested state of this alloy, which develops nano-size, single-phase, isostructural ordered domains at temperatures above 1273 K within a matrix of solid solution. Upon aging at lower temperatures, both atomistic simulation and TEM investigation demonstrate the chemical sensitivity of the matrix by localized symmetry changes which suppress any long-range transformation, mirroring the kinetics observed in strain-glass alloys. Careful quantification of experimental and simulated diffraction patterns from various aging conditions reveal the degree of order in CoCrNi to increase given longer aging times, with achievement of longer domain length scales only when subjected to temperatures below 873 K. This evidence indicates a kinetically constrained, chemically sensitive transition from a disordered fcc to a partially ordered, lower symmetry structure given adequate aging time and temperature. Magnetic effects on the transformation are dictated on the specific alloy stoichiometry and aging temperature, which act to amplify any effects of the glassy kinetics.

Research Highlights
- Home
- Research Highlights
Elaf A. Anber, David Beaudry, Charlie Brandenburg, Sebastian Lech, Lavina Backman, Daniel L. Foley, Emily L. Wang, Michael Joseph Waters, Loic Perriere, Jean- Philippe Couzinie, James M. Rondinelli, Elizabeth Opila, Mitra L. Taheri
Refractory high entropy alloys (RHEAs) hold the promise of superior mechanical properties at high temperatures but are plagued by a lack of oxidation resistance. In this work, the role of Al additions (4.8, and 13 at.%) to HfNbTaTiZr is explored as a means of improving protective oxide scale formation in RHEAs. Oxide formation was resolved using STEM-EDS/EELS, precession electron diffraction, and computational predictions of formation energy. Our results show that oxides with large negative formation energies (i.e., HfO2/ZrO2) always formed near the metal/oxide interface while Al2O3 and oxides with less negative formation energies formed near the surface. The addition of Al prevented pesting in both alloys and formed Al2O3 with 13 at.% Al. While the Al2O3 formed was not continuous, we report the lowest threshold yet to prevent pesting with only 4.8 at.% Al added. These findings provide guidance to future alloy development of Al2O3-forming alloys.
Jonathan D Hollenbach, Cassandra M Pate, Haili Jia, James L Hart, Paulette Clancy, Mitra L Taheri
In-situ Electron Energy Loss Spectroscopy (EELS) is an instrumental technique that has traditionally been used to understand how the choice of materials processing has the ability to change local structure and composition. However, more recent advances to observe and react to transient changes occurring at the ultrafast timescales that are now possible with EELS and Transmission Electron Microscopy (TEM) will require new frameworks for characterization and analysis. We describe a machine learning (ML) framework for the rapid assessment and characterization of in operando EELS Spectrum Images (EELS-SI) without the need for many labeled training datapoints as typically required for deep learning classification methods. By embedding computationally generated structures and experimental datasets into an equivalent latent space through Variational Autoencoders (VAE), we effectively predict the structural changes at latency scales relevant to closed-loop processing within the TEM. The framework described in this study is a critical step in enabling automated, on-the-fly synthesis and characterization which will greatly advance capabilities for materials discovery and precision engineering of functional materials at the atomic scale.
David C. Beaudry et al. Exceptional hardness in multiprincipal element alloys via hierarchical oxygen heterogeneities.Sci. Adv.10,eado9697(2024).DOI:10.1126/sciadv.ado9697
Refractory multiprincipal element alloys (RMPEAs) are potential successors to incumbent high-temperature structural alloys, although efforts to improve oxidation resistance with large additions of passivating elements have led to embrittlement. RMPEAs containing group IV and V elements have a balance of properties including moderate ductility, low density, and the necessary formability. We find that oxidation of group IV-V RMPEAs induces hierarchical heterogeneities, ranging from nanoscale interstitial complexes to tertiary phases. This microstructural hierarchy considerably enhances hardness without indentation cracking, with values ranging between 12.1 and 22.6 GPa from the oxide-adjacent metal to the surface oxides, a 3.7 to 6.8× increase over the interstitial-free alloy. Our fundamental understanding of the oxygen influence on phase formation informs future alloy design to enhance oxidation resistance and obtain exceptional hardness while preserving plasticity.
Hollenbach, J.D., Pate, C.M., Jia, H. et al. Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning. Sci Rep 14, 17881 (2024). https://doi.org/10.1038/s41598-024-66902-4
In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.
William H. Blades, Debashish Sur, Howie Joress, Brian DeCost, Emily F. Holcombe, Ben Redemann, Tyrel M. McQueen, Rohit Berlia, Jagannathan Rajagopalan, Mitra L. Taheri, John R. Scully, Karl Sieradzki, High-throughput aqueous passivation behavior of thin-film vs. bulk multi-principal element alloys in sulfuric acid, Corrosion Science, Volume 236, 2024, 112261, ISSN 0010-938X, https://doi.org/10.1016/j.corsci.2024.112261.
Multi-principal element alloys have the potential to show excellent passivation behavior. However, the detailed compositional and crystal structure design of these alloys requires a high-throughput strategy. We used combinatorial thin-film libraries of single-phase (FeCoNi)1-x-yCrxAly alloys and compared their passivation behaviors to corresponding bulk alloys. Our results demonstrate that the detailed passivation behaviors of thin-films and bulk alloys are different which is related to both nanoscale porosity within the thin-films and grain boundary dissolution. Nevertheless, we found that comparisons made among suitably designed sets of thin-film alloys can be used to determine the best corrosion performing bulk alloy composition.
Sebastian Lech, Elaf A Anber, Emily Holcombe, Jason Hattrick-Simpers, Howie Joress, Mitra L Taheri, High-Throughput Screening and Design Guidelines for Single-Phase Refractory High Entropy Alloys from the Nb-Ti-Zr System, Microscopy and Microanalysis, Volume 30, Issue Supplement_1, July 2024, ozae044.647, https://doi.org/10.1093/mam/ozae044.647
Refractory high entropy alloys (RHEAs) hold promise as a candidate, next-generation material for high-temperature applications challenging widely used nickel-based superalloys [1-4]. Both single- and dual-phase RHEAs demonstrated the ability to deliver sufficient mechanical properties at temperatures exceeding superalloy applications. However, RHEAs are currently limited by insufficient oxidation resistance leading to fast structural degradation from their surface. Partially, it is caused by the low structure and phase stability of multicomponent alloys, especially in the presence of oxygen. Exploration of large refractory compositional space with a limited amount of elements offers a bottom-up approach to establishing fundamental guidelines for the design of stable refractory high entropy alloys. One of the ways to accelerate alloy design is combinatorial material libraries screening obtained by thin-film magnetron sputtering [5]. A large number of compositional variants combined with high-throughput materials characterization allows for the identification of relationships and patterns significantly faster than conventional alloy design.
Emily H Mang, Annie K Barnett, Sebastian Lech, Mitra L Taheri, Deep Learning Object Detection Video Analysis to Determine Grain Boundary Defect Sink Efficacy in Ion Irradiated Specimens, Microscopy and Microanalysis, Volume 30, Issue Supplement_1, July 2024, ozae044.843, https://doi.org/10.1093/mam/ozae044.843
Improvements to transmission electron microscopy (TEM) instrumentation and data collection capability have driven an increase in demand for improved, automated analysis of TEM images and in situ video [1]. Studies employing deep learning-based segmentation and object detection approaches for irradiation-induced defect characterization have become more commonplace in hopes of working toward real-time detection of in situ TEM irradiation experiments [2-3]. Frame-to-frame resolution analysis provides an opportunity to resolve nuances in microstructural feature evolution unachievable by classical human-counting methods. In this work, we explore the localized impact of grain boundary (GB) defect sinks and their ability to modulate defect microstructure in a radiation condition. Temporally resolved defect density measurements are enabled by YOLOv5 deep learning object detection models.
Elaf A Anber, Sebastian Lech, Jodie Baris, David Beaudry, Zhaoxi Cao, Ian Daniel McCue, Jonah Erlebacher, Mitra L Taheri, Interfacial Phase Evolution during In Situ TEM Dealloying Approach of Ti30Cr/Ni, Microscopy and Microanalysis, Volume 30, Issue Supplement_1, July 2024, ozae044.849, https://doi.org/10.1093/mam/ozae044.849
Dealloying has emerged as a promising route to fabricate nanostructured metallic materials [1]. Here, we examined the interfacial phase evolution of two-phase NiTi/Cr films made on engineering alloys by a kind of liquid metal dealloying using in situ TEM observations. These materials exhibit interesting responses associated with their complex two-phase nanostructure formed during the dealloying reaction [1-3].
Daniel L Foley, Partha P Das, Barnaby D A Levin, Bryan W Reed, Daniel J Masiel, Runlai Wang, John D Tovar, Alejandro Gómez-Pérez, Monika Budayova-Spano, Wai Li Ling, Anna Mian, Sergi Plana-Ruiz, Stavros Nicolopoulos, Mitra L Taheri, Electrostatic Dose Modulation Improves Lifespan of Beam-Sensitive Specimens For Advanced Electron Crystallography Techniques, Microscopy and Microanalysis, Volume 30, Issue Supplement_1, July 2024, ozae044.897, https://doi.org/10.1093/mam/ozae044.897
The development of electron diffraction-based techniques such as 3DED/MicroED has allowed researchers to solve the structure of hundreds of different types of nanometer size crystals [1-3]. The use of electrons provides certain advantages over the more conventional x-ray diffraction (XRD)-based techniques. Chief among these is the requirement in XRD that the crystals being probed are large, even at synchrotron facilities which provide very small and bright X-ray beams but are rather difficult to access for most research [4]. Electron crystallography carried out in a transmission electron microscope (TEM), on the other hand, can easily extract structural information from nanometer-sized crystals, and the availability of TEMs is higher.
Sur D., Smith N.C., Connors P.F., Blades W.H., Taheri M.L., Wolverton C.M., Sieradzki K., Scully J.R. Investigating the synergistic benefits of Al on Cr(III) in the passive films of FeCoNi-Cr-Al CCAs in sulfuric acid (2025) Electrochimica Acta, 513, art. no. 145523 DOI: 10.1016/j.electacta.2024.145523
The synergistic effects of Al on Cr(III) passivation in a family of (FeCoNi)100-x-yCrxAly (at.%) complex concentrated alloys (CCAs), where x = (0, 4, 10, 13, 16) and y = (0, 3, 6, 9, 13) were investigated. Homogenized solid solution alloys containing 10 at.% Cr intentionally selected below the stainless steel threshold of 12 at.% achieved excellent passivation by forming a Cr(III) rich film containing Al(III) when alloyed with small amounts of Al. For example, CCAs containing 10 at.% Cr and 6 at.% Al can attain comparable re-passivation properties as seen in 304L stainless steel in sulfuric acid. Potentiostatic re-passivation over 10 ks exposure revealed the benefit of Al addition leading to greater Cr(III) cation fractions in the film, better corrosion protection, and faster film formation. Two underlying mechanisms are proposed to explain the effect. The observed Al-Cr synergy can be understood as a type of third element effect due to Al where enrichment of Cr(III) in the passive films occurs due to the small but beneficial presence of Al(III). Al is also a secondary passivator detected in the passive film. Moreover, the potential effects of other alloying elements in CCAs were examined through the investigation of binary and ternary alloys containing Cr and Al. These studies also focused on the influence of Al additions on Cr-Cr clustering in solid solution during steady-state passivation, facilitated by chemical short-range ordering.
Roy A., Hubbard L., Overman N.R., Fiedler K.R., Horangic D., Hilty F., Taheri M.L., Schreiber D.K., Olszta M.J. Machine learning and molecular dynamics simulations aided insights into condensate ring formation in laser spot welding (2024) Scientific Reports, 14 (1), art. no. 30068 DOI: 10.1038/s41598-024-79755-8
Condensate ring formation can be used as a benchmark in welding processes to assess the efficiency and quality of the weld. Condensate formation is critical as the resulting condensate settles into the powder thereby altering the quality of unconsolidated powder. This study investigates the intricate relationship between alloy composition, vapor pressure, and condensate ring thickness as seen in a two-dimensional micrograph. To study the process, laser spot welding was performed on 9 different alloys, and the inner spot weld diameter along with the condensate ring formation was studied. Leveraging machine learning models, experimental observations, and molecular dynamics simulations, we explore the fundamental factors governing condensate ring formation. The models, adept at predicting weld spot diameter and condensate ring thickness, identify laser power as a primary determinant for weld spot diameter followed by physical properties like hardness and density. Conversely, for condensate ring thickness, vapor pressure and melting point descriptors consistently emerge as paramount, as validated across all models. Molecular dynamics simulations on Ni-Cr alloys elucidate the vaporization dynamics, confirming the role of vapor pressure in governing surface vaporization. Our findings underscore the pivotal influence of vapor pressure and melting point descriptors in condensate ring formation. The convergence of machine learning predictions and simulation insights elucidates the dominance of these descriptors, offering crucial insights into alloy design strategies to minimize condensate ring formation in laser welding processes.
Śliwa M., Zhang H., Gao J., Stephens B.O., Patera A.J., Raciti D., Hanrahan P.D., Warecki Z.A., Foley D.L., Livi K.J., Brintlinger T.H., Taheri M.L., Hall A.S., Kempa T.J. Selective CO2 Reduction Electrocatalysis Using AgCu Nanoalloys Prepared by a “Host-Guest” Method (2024) Nano Letters, 24 (44), pp. 13911 – 13918 DOI: 10.1021/acs.nanolett.4c02638
Multimetallic nanoalloy catalysts have attracted considerable interest for enhancing the efficiency and selectivity of many electrochemically driven chemical processes. However, the preparation of homogeneous bimetallic alloy nanoparticles remains a challenge. Here, we present a room-temperature and scalable, host-guest approach for synthesis of dilute Cu in Ag alloy nanoparticles. In this approach, an ionic silver bromide precursor harboring exogenous Cu cations is reduced to yield ∼20 nm diameter AgCu alloy nanoparticles wherein the % Cu loading can be tuned precisely. AgCu nanoparticles with a 5% nominal loading of Cu exhibit peak activity (−0.23 mA/cm2 normalized partial current density) and selectivity (83.2% faradaic efficiency) for CO product formation from electrocatalytic reduction of CO2 at mild overpotentials. These AgCu nanoalloys exhibit a higher mass activity compared to Ag- and Cu-containing nanomaterials used for similar electrocatalytic transformations. Our host-guest synthesis platform holds promise for production of other nanoalloys with relevance in electrocatalysis and optics. © 2024 American Chemical Society.