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Manzoor, Anus

Publications and source records attributed to Manzoor, Anus.

Nanoprecipitates to Enhance Radiation Tolerance in High-Entropy Alloys

The growth of advanced energy technologies for power generation is enabled by the design, development, and integration of structural materials that can withstand extreme environments, such as high temperatures, radiation damage, and corrosion. High-entropy alloys (HEAs) are a class of structural materials in which suitable chemical elements in four or more numbers are mixed to typically produce single-phase concentrated solid solution alloys (CSAs). Many of these alloys exhibit good radiation tolerance like limited void swelling and hardening up to relatively medium radiation doses (tens of displacements per atom (dpa)); however, at higher radiation damage levels (>50 dpa), some HEAs suffer from considerable void swelling limiting their near-term acceptance for advanced nuclear reactor concepts. In this study, we developed a HEA containing a high density of Cu-rich nanoprecipitates distributed in the HEA matrix. The Cu-added HEA, NiCoFeCrCu 0.12 , shows excellent void swelling resistance and negligible radiation-induced hardening upon irradiation up to high radiation doses (i.e., higher than 100 dpa). The void swelling resistance of the alloy is measured to be significantly better than NiCoFeCr CSA and austenitic stainless steels. Density functional theory simulations predict lower vacancy and interstitial formation energies at the coherent interfaces between Cu-rich nanoprecipitates and the HEA matrix. The alloy maintained a high sink strength achieved via nanoprecipitates and the coherent interface with the matrix at a high radiation dose (~50 dpa). From our experiments and simulations, the effective recombination of radiation-produced vacancies and interstitials at the coherent interfaces of the nanoprecipitates is suggested to be the critical mechanism responsible for the radiation tolerance of the alloy. Furthermore, the materials design strategy based on incorporating a high density of interfaces can be applied to high-entropy alloy systems to improve their radiation tolerance.

36 MATERIALS SCIENCE↗

Charge-density based evaluation and prediction of stacking fault energies in Ni alloys from DFT and machine learning

A combination of high strength and high ductility has been observed in multi-principal element alloys due to twin formation attributed to low stacking fault energy (SFE). In the pursuit of low SFE alloys, a key bottleneck is the lack of understanding of the composition–SFE correlations that would guide tailoring SFE via alloy composition. Using density functional theory (DFT), we show that dopant radius, which have been postulated as a key descriptor for SFE in dilute alloys, does not fully explain SFE trends across different host metals. Instead, charge density is a much more central descriptor. It allows us to (1) explain contrasting SFE trends in Ni and Cu host metals due to various dopants in dilute concentrations, (2) explain the large SFE variations observed in the literature even within a given alloy composition due to the nearest neighbor environments in “model” concentrated alloys, and (3) develop a machine learning model that can be used to predict SFEs in multi-elemental alloys. Further, this model opens a possibility to use charge density as a descriptor for predicting SFE in alloys.

36 MATERIALS SCIENCE↗