Critical Behavior of the Competing Magnetic Nature in the Antiperovskite Compound

Abstract

Antiperovskite materials are deemed as a promising candidate for data storage and efficient energy conversion, exemplified by giant magnetoresistance (GMR) and magnetocaloric effect (MCE). In particular, the abundant magnetic configurations of antiperovskites endow them with a high degree of diversity in probing novel phenomena. Herein, we report the critical behavior of the antiperovskite Sn0.6NFe3.4 with an obvious second-order magnetic transition around Curie temperature (T C = 230 K). The obtained critical exponents (β, γ, and δ) are well consistent for different theoretical methods, which are also verified by the Widom scaling law and scaling equation. It is found that the critical exponent of β is much in accordance with the value of the mean-field model, indicating a long-range FM coupling in the system, whereas the other γ and δ are accessible to the three-dimensional (3D) Heisenberg model. Our work promotes the understanding of antiperovskite magnetism, providing insight for further fundamental research and resulting applications.

Comprehensive Review on the Synthetic Strategies toward Anti-SARS-CoV-2 Drug Ensitrelvir

“…The following N 3 -alkyaltion with 2a was initially achieved using K 2 CO 3 and N , N -dimethylformamide (DMF) by Shionogi & Co., Ltd . As mentioned above, based on the stability of 2a in basic conditions, although different bases and solvents were tried by many researchers, compound 32 was generated only in a moderate yield on a gram scale. ,, The obtained 32 was converted into the final target ensitrelvir via substitution with the key indazole 4 . The initial yield was only 25.0% under the action of lithium hexamethyldisilazide (LiHMDS) and THF .…”

Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine‐Learning‐Based Technique

“…[11] The machine-learning (ML) technique has been recommended as a powerful tool for various fields, such as autonomous vehicles, [12] medical treatments, [13] smart factories, [14] and AM. [15][16][17] In the field of manufacturing, it can potentially optimize the processes with manufacturing parameters, find unperceivable features in images of the on-process object, and predict issues in processes from a high volume of process-related data. [18] Recently, it has been introduced into the field of AM in terms of process and design optimization, quality control, and enhancement of the cost-effectiveness.…”