Large Cryogenic Magnetostriction Induced by Hydrostatic Pressure in MnCo0.92Ni0.08Si Alloy

Abstract

Giant magnetostriction could be achieved in MnCoSi-based alloys due to the magneto-elastic coupling accompanied by the meta-magnetic transition. In the present work, the effects of hydrostatic pressure on magnetostrictive behavior in MnCo0.92Ni0.08Si alloy have been investigated. The saturation magnetostriction (at 30,000 Oe) could be enhanced from 577 ppm to 5034 ppm by the hydrostatic pressure of 3.2 kbar at 100 K. Moreover, under a magnetic field of 20,000 Oe, the reversible magnetostriction was improved from 20 ppm to 2112 ppm when a hydrostatic pressure of 6.4 kbar was applied at 70 K. In all, it has been found that the magnetostrictive effect of the MnCo0.92Ni0.08Si compound is strongly sensitive to external hydrostatic pressure. This work proves that the MnCoSi-based alloys as a potential cryogenic magnetostrictive material can be modified through applied hydrostatic pressure.

STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion

“…Some recent works are combining GNNs with text classifiers to take advantage of both topology and semantic modeling. For example, GLEM (Zhao et al, 2023) proposes a variational expectation maximization framework to alternatively updates the text and graph modules separately. Nevertheless, different from previous works, our STINMatch method focuses on the semisupervised integration for text-graph joint learning framework, as well as the multi-label diffusion upon typologies for text-attributed GNN works.…”

“…GNN models specialized for MLTC tasks such as MAGNET (Pal et al, 2020) and LC-GAT (Xu et al, 2020) are also included for comparison. GLEM (Zhao et al, 2023) is a latest text-graph co-training method, which has also been tested. Some details of GLEM have been modified to adapt to our scenario for fair comparison (e.g.…”

“…In order to address this problem, we propose a new model, semantic-topological iteration network (STIN), to estimate the 'risk diffusion' on the established news-enterprise knowledge graph (NEKG). Unlike previous graph neural network (GNN) methods for text analysis (Yang et al, 2021b,a;Pang et al, 2022;Zhao et al, 2023), our STIN model focus on the multi-label-correlation guided text-graph joint learning, hoping to capture the dissemination for various types of financial risks following the NEKG topological structure.…”

Understanding the effect of foreign atoms occupation on the metamagnetic behaviors in MnCoSi-based alloys: taking Pt-doping as an example

“…To achieve reversible magnetostrain through a magneticfield-induced phase transition, our attention is focused on MnCoSi-based alloys with a TiNiSi-type orthorhombic structure [12][13][14][15][16][17][18][19]. The stoichiometric MnCoSi alloy exhibits a nonlinear antiferromagnetic state below its Néel temperature (∼381 K) but undergoes a transition into a ferromagnetic state when the external magnetic field reaches a critical value (µ 0 H cri ) [13][14][15][16][17]19].…”

“…Consequently, the magnetostrain at room temperature is derived from the high-field-induced first-order transition, accompanied by significant hysteresis [16]. Fortunately, both µ 0 H cri and T cri can be reduced by doping, such as replacing Si by Ge and B, Co by Ni and Fe, Mn by Ni, Fe and Co [17,18,[20][21][22][23][24]. In these doped MnCoSi-based alloys, the magnetostrain at room temperature becomes reversible because it is generated from second-order metamagnetic transition [17,18,[20][21][22][23][24].…”