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arXiv 2202.11010cond-mat.mes-hall
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纳米磁体阵列中的熵驱动有序

Entropy-driven order in an array of nanomagnets

Hilal Saglam, Ayhan Duzgun, Aikaterini Kargioti, Nikhil Harle, Xiaoyu Zhang, Nicholas S. Bingham, Yuyang Lao, Ian Gilbert, Joseph Sklenar, Justin D. Watts, Just… 展开作者

Hilal Saglam, Ayhan Duzgun, Aikaterini Kargioti, Nikhil Harle, Xiaoyu Zhang, Nicholas S. Bingham, Yuyang Lao, Ian Gilbert, Joseph Sklenar, Justin D. Watts, Justin Ramberger, Daniel Bromley, Rajesh V. Chopdekar, Liam O'Brien, Chris Leighton, Cristiano Nisoli, Peter Schiffer

摘要:

长程有序通常与熵的减少有关。然而,在某些特殊情况下,它也可以由熵增加驱动。我们证明,人工自旋冰阵列的单域纳米磁体可以设计产生熵驱动的顺序。我们重点关注俄罗斯方块人工自旋冰结构,这是一种具有零点泡利熵的高度受挫阵列几何结构,它是通过在正则方形冰晶格上选择性地创造规则空位而形成的。我们探测热活性俄罗斯方块人工自旋冰实验和模拟,测量单个纳米磁体的磁矩。我们在这些矩的一个子集中发现了二维磁有序,我们证明这是由无序引起的(即,增加的熵)在另一个时刻的子集中。与其他熵驱动的系统相比,俄罗斯方块人工自旋冰中的离散自由度是二元的,并且在微观尺度上既可设计又可直接观察,并且系统的熵可以在模拟中精确计算。在这个例子中,系统的相互作用和基态熵都得到了很好的定义,扩展了熵驱动有序化研究的实验范围。

英文摘要:

Long-range ordering is typically associated with a decrease in entropy. Yet, it can also be driven by increasing entropy in certain special cases. We demonstrate that artificial spin ice arrays of single-domain nanomagnets can be designed to produce entropy-driven order. We focus on the tetris artificial spin ice structure, a highly frustrated array geometry with a zero-point Pauli entropy, which is formed by selectively creating regular vacancies on the canonical square ice lattice. We probe thermally active tetris artificial spin ice both experimentally and through simulations, measuring the magnetic moments of the individual nanomagnets. We find two-dimensional magnetic ordering in one subset of these moments, which we demonstrate to be induced by disorder (i.e., increased entropy) in another subset of the moments. In contrast with other entropy-driven systems, the discrete degrees of freedom in tetris artificial spin ice are binary and are both designable and directly observable at the microscale, and the entropy of the system is precisely calculable in simulations. This example, in which the system's interactions and ground state entropy are well-defined, expands the experimental landscape for the study of entropy-driven ordering.

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