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...
Saved in:
Main Authors: | , , , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173025 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
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. |
---|