Accelerated multi-property screening of Fe–Co–Ni alloy libraries by hyper-heuristic combinatorial flow synthesis and high-throughput spark plasma sintering

High-throughput (HT) chemical synthesis facilitates accelerated materials discovery products. However, HT methods are limited by the need for expensive robotic systems, complicated methodology, and low yield. Hence, we developed a hyper-heuristic combinatorial flow synthesis (HCFS) device capable of...

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Bibliographic Details
Main Authors: Padhy, Shakti P., Tan, Li Ping, Varma, Vijaykumar B., Chaudhary, V., Tsakadze, Zviad, Ramanujan, Raju V
Other Authors: School of Materials Science and Engineering
Format: Article
Language:English
Published: 2024
Subjects:
Online Access:https://hdl.handle.net/10356/173025
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Institution: Nanyang Technological University
Language: English
Description
Summary:High-throughput (HT) chemical synthesis facilitates accelerated materials discovery products. However, HT methods are limited by the need for expensive robotic systems, complicated methodology, and low yield. Hence, we developed a hyper-heuristic combinatorial flow synthesis (HCFS) device capable of composition gradient generation and production of an adequate mass of Fe–Co–Ni alloy nanoparticles. A library of 91 Fe–Co–Ni powder compositions was synthesized using this technique. A high-throughput spark plasma sintering (HT-SPS) methodology, along with the die design, was developed for combinatorial screening of multiple properties. 56 compositions were down-selected and consolidated into compositionally graded bulk samples using HT-SPS and subsequent annealing. The crystallographic, magnetic, electrical, and magnetic properties of the bulk library were assessed. The saturation magnetization (Ms) varied from 83.3 emu/g to 225.2 emu/g, coercivity (Hc) from 17.5 Oe to 78.4 Oe, resistivity (ρ) from 17.2 μΩ·cm to 986.7 μΩ·cm, and Vickers hardness (HV) from 41.9 HV to 281.7 HV. Novel Fe–Co–Ni compositions, e.g., Fe36.5Co55.1Ni8.4 and Fe22.6Co73.4Ni4, with a promising multi-property set, were identified for the first time. This study demonstrated that promising new compositions exhibiting multi-property optimization can be successfully discovered by our hyper-heuristic combinatorial chemical synthesis methodology.