According to Onsager's phenomenological theory, the diffusion fluxes of coadsorbed particles are expressed via the gradients of their chemical potentials and can contain the off-diagonal terms. In the conventional two-dimensional lattice-gas models with jumps of monomers to nearest-neighbour vacant sites, such terms are absent at the mean-field (MF) level or in the framework including the statistical correlations related to lateral interaction between particles but neglecting the dynamical correlations. The role of the off-diagonal terms in these models with dynamical correlations and in other unconventional models is still not clear. Herein, the situation in this area is clarified from two perspectives by using Monte Carlo simulations and analytically, respectively. First, the scale of the related effects is shown for conventional diffusion with emphasis on the concentration profiles under steady-state conditions and diffusion-limited associative desorption under transient conditions. In these cases, the role of the off-diagonal terms is well manifested at the level above the MF approximation provided the coverages are appreciable and the difference between the jump rates of different particles is appreciable as well. Second, the off-diagonal terms are demonstrated to be available and important already at the MF level in unconventional diffusion occurring via concerted exchange and pair jumps.

中文翻译：

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Onsager非对角线通量在共吸附颗粒扩散中的表现

根据 Onsager 的现象学理论，共吸附粒子的扩散通量通过其化学势的梯度表示，并且可以包含非对角项。在单体跳跃到最近邻空位的传统二维晶格气体模型中，这些术语在平均场 (MF) 水平或框架中不存在，包括与粒子之间的横向相互作用相关的统计相关性，但忽略了动态相关性。非对角项在这些具有动态相关性的模型和其他非常规模型中的作用仍不清楚。在此，该领域的情况分别通过使用蒙特卡罗模拟和分析从两个角度来阐明。第一的，显示了常规扩散的相关影响的规模，重点是稳态条件下的浓度分布和瞬态条件下扩散限制的结合解吸。在这些情况下，非对角项的作用在高于 MF 近似的水平上得到很好的体现，前提是覆盖范围是可观的，并且不同粒子的跳跃率之间的差异也是可观的。其次，非对角线项已被证明在 MF 水平上已经可用，并且在通过协同交换和对跳跃发生的非常规扩散中已经很重要。非对角项的作用在 MF 近似值以上的水平上得到很好的体现，前提是覆盖率是可观的，并且不同粒子的跳跃率之间的差异也是可观的。其次，非对角线项已被证明在 MF 水平上已经可用，并且在通过协同交换和对跳跃发生的非常规扩散中已经很重要。非对角项的作用在 MF 近似值以上的水平上得到很好的体现，前提是覆盖率是可观的，并且不同粒子的跳跃率之间的差异也是可观的。其次，非对角线项已被证明在 MF 水平上已经可用，并且在通过协同交换和对跳跃发生的非常规扩散中已经很重要。