Access to well-defined, model-like, non-noble metal intermetallic compound nanomaterials (<10 nm) with phase pure bulk, bulk-like 1st-atomic-layer surface composition, and unique electronic and surface chemical properties is critical for the fields of catalysis, electronics, and sensor development. Non-noble metal intermetallic compounds are compositionally ordered solid compounds composed of transition metals and semimetals or post-transition metals. Their synthesis as model-like high-surface-area supported nanoparticles is challenging due to the elevated reactivity of the constituent elements and their interaction with the support material. In this study, we have developed a systematic understanding of the fundamental phenomena that control the synthesis of these materials such that phase pure bulk nanoparticles (<10 nm) may be produced with bulk-like surface terminations. The effects of the precursor and support choice, chemical potential of H 2 , reduction temperature, and annealing procedures were investigated to understand the fundamental kinetics of particle formation and interactions that dictate phase purity and stability and 1st-atomic-layer surface composition. The understanding developed may serve as a foundation for further developing advanced synthesis procedures for well-defined nanoparticles with increasing compositional complexity.
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Computational Assessment of Novel Predicted Compounds in Ni-Re Alloy System
Ab initio high-throughput efforts are continuously identifying new intermetallic compounds in a wide range of alloy systems that were previously thought to be well-characterized. While such predictions are likely valid near absolute zero, they carry the risk that such phases become unstable at the higher temperature relevant to typical synthesis conditions. We illustrate how this possibility can be rapidly tested by integrating Calphad modeling into the high-throughput loop. As an example, we investigate the Ni-Re system, in which D019 and D1a phases were predicted as possible intermetallic compounds. We confirm that these phases are indeed stable at practical synthesis temperatures and explain how they could have been overlooked in prior assessments.
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- Award ID(s):
- 2001411
- NSF-PAR ID:
- 10222507
- Date Published:
- Journal Name:
- Journal of Phase Equilibria and Diffusion
- ISSN:
- 1547-7037
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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An overview of the mechanical bonding of dissimilar bulk engineering metals through high‐pressure torsion (HPT) processing at room temperature is described in this Review. A recently developed procedure of mechanical bonding involves the application of conventional HPT processing to alternately stacked two or more disks of dissimilar metals. A macroscale microstructural evolution involves the concept of making tribomaterials and, for some dissimilar metal combinations, microscale microstructural changes demonstrate the synthesis of metal matrix nanocomposites (MMNCs) through the nucleation of nanoscale intermetallic compounds within the nanostructured metal matrix. Further straining by HPT during mechanical bonding provides an opportunity to introduce limited amorphous phases and a bulk metastable state. The mechanically bonded nanostructured hybrid alloys exhibit an exceptionally high specific strength and an enhanced plasticity. These experimental findings suggest a potential for using mechanical bonding for simply and expeditiously fabricating a wide range of new alloy systems by HPT processing.
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Abstract Intermetallic compounds formed from non-precious transition metals are promising cost-effective and robust catalysts for electrochemical hydrogen production. However, the development of monolithic nanoporous intermetallics, with ample active sites and sufficient electrocatalytic activity, remains a challenge. Here we report the fabrication of nanoporous Co7Mo6and Fe7Mo6intermetallic compounds via liquid metal dealloying. Along with the development of three-dimensional bicontinuous open porosity, high-temperature dealloying overcomes the kinetic energy barrier, enabling the direct formation of chemically ordered intermetallic phases. Unprecedented small characteristic lengths are observed for the nanoporous intermetallic compounds, resulting from an intermetallic effect whereby the chemical ordering during nanopore formation lowers surface diffusivity and significantly suppresses the thermal coarsening of dealloyed nanostructure. The resulting ultrafine nanoporous Co7Mo6exhibits high catalytic activity and durability in electrochemical hydrogen evolution reactions. This study sheds light on the previously unexplored intermetallic effect in dealloying and facilitates the development of advanced intermetallic catalysts for energy applications.
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Abstract Forming metallurgical phases has a critical impact on the performance of dissimilar materials joints. Here, we shed light on the forming mechanism of equilibrium and non-equilibrium intermetallic compounds (IMCs) in dissimilar aluminum/steel joints with respect to processing history (e.g., the pressure and temperature profiles) and chemical composition, where the knowledge of free energy and atomic diffusion in the Al–Fe system was taken from first-principles phonon calculations and data available in the literature. We found that the metastable and ductile (judged by the presently predicted elastic constants) Al6Fe is a pressure (
P ) favored IMC observed in processes involving high pressures. The MoSi2-type Al2Fe is brittle and a strongP -favored IMC observed at high pressures. The stable, brittle η-Al5Fe2is the most observed IMC (followed by θ-Al13Fe4) in almost all processes, such as fusion/solid-state welding and additive manufacturing (AM), since η-Al5Fe2is temperature-favored, possessing high thermodynamic driving force of formation and the fastest atomic diffusivity among all Al–Fe IMCs. Notably, the ductile AlFe3, the less ductile AlFe, and most of the other IMCs can be formed during AM, making AM a superior process to achieve desired IMCs in dissimilar materials. In addition, the unknown configurations of Al2Fe and Al5Fe2were also examined by machine learning based datamining together with first-principles verifications and structure predictions. All the IMCs that are notP- favored can be identified using the conventional equilibrium phase diagram and the Scheil-Gulliver non-equilibrium simulations. -
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Abstract Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu6Sn5intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rank
σ SI ,σ IL , andσ IL , respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that showsσ SI andσ SL alter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11669-021-00884-y
