Keller K, Ritsch I, Hintz H, Hülsmann M, Qi M, Breitgoff F D, Klose D, Polyhach Y, Yulikov M, Godt A, Jeschke G
Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland.
Faculty of Chemistry and Center for Molecular Materials (CM2), Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany.
Phys Chem Chem Phys. 2020 Oct 7;22(38):21707-21730. doi: 10.1039/d0cp03105d.
Determining distributed exchange couplings is important for understanding the properties of synthetic magnetic molecules. Such distributions can be determined from pulsed dipolar spectroscopy (PDS) data, but this is challenging due to the similar influence of both exchange and dipolar couplings on such data. In this work we introduce two models that aim to identify these two contributions to the spin-spin couplings from frequency-domain PDS data of shape-persistent molecules having either two Cu(ii) ions, or a Cu(ii) ion and a nitroxide radical as the paramagnetic moieties. The first model assumes correlated Lorentzian or Gaussian exchange and dipole-dipole coupling distributions whose parameters are the model's unknowns. The second model relies on prior knowledge of the distance distribution and by performing Tikhonov regularization along the exchange coupling dimension yields the latter distribution model-free. Both models were able to differentiate between the absence and the presence of exchange interaction, to determine the coupling regime (ferro- or antiferromagnetic) and to estimate the distribution shape. In contrast, calculations within the exchange resilient model of the neural network analysis implemented in DeerAnalysis2018 were not able for our data to identify exchange couplings and return correct distance distributions. However, the generic model was able to identify and separate the strongly curved intermolecular background in the relaxation-induced dipolar modulation enhancement (RIDME) experiments. Our analysis revealed that in such systems exchange coupling may be present up to at least 3.3 nm in π-conjugated systems involving Cu(ii)-PyMTA, while it is negligible for distances r ≥ 4.5 nm between Cu(ii) ions and r ≥ 3.8 nm between a Cu(ii) ion and an unpaired electron of a nitroxide radical. Disruption of the π-conjugation between the ligand of the Cu(ii) complex and the nitroxide leads to negligible exchange coupling at distances r ≥ 2.6 nm in the corresponding [Cu(ii)-TAHA]-nitroxide ruler. Overall, for cases with known distance distributions, the presented analysis techniques allow to determine distributions of exchange couplings from PDS data.
确定分布式交换耦合对于理解合成磁性分子的性质至关重要。这种分布可以从脉冲偶极光谱(PDS)数据中确定,但由于交换耦合和偶极耦合对这类数据的影响相似,这具有挑战性。在这项工作中,我们引入了两个模型,旨在从具有两个Cu(II)离子或一个Cu(II)离子和一个氮氧自由基作为顺磁部分的形状持久分子的频域PDS数据中识别自旋 - 自旋耦合的这两种贡献。第一个模型假设相关的洛伦兹或高斯交换和偶极 - 偶极耦合分布,其参数是模型的未知量。第二个模型依赖于距离分布的先验知识,并通过沿交换耦合维度执行蒂霍诺夫正则化,得到无模型的后者分布。这两个模型都能够区分交换相互作用的有无,确定耦合 regime(铁磁或反铁磁)并估计分布形状。相比之下,在DeerAnalysis2018中实现的神经网络分析的交换弹性模型内的计算无法为我们的数据识别交换耦合并返回正确的距离分布。然而,通用模型能够在弛豫诱导偶极调制增强(RIDME)实验中识别并分离出强烈弯曲的分子间背景。我们的分析表明,在这样的系统中,在涉及Cu(II) - PyMTA的π共轭系统中,交换耦合可能存在至至少3.3 nm,而对于Cu(II)离子之间距离r≥4.5 nm以及Cu(II)离子与氮氧自由基的未配对电子之间距离r≥3.8 nm时,交换耦合可忽略不计。Cu(II)配合物的配体与氮氧自由基之间的π共轭破坏导致在相应[Cu(II) - TAHA] - 氮氧标尺中距离r≥2.6 nm时交换耦合可忽略不计。总体而言,对于具有已知距离分布的情况,所提出的分析技术允许从PDS数据确定交换耦合的分布。