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In this paper, we numerically optimize broadband pulse shapes that maximize Hahn echo amplitudes. Pulses are parameterized as neural networks (NN), nonlinear amplitude limited Fourier series (FS), and discrete time series (DT). These are compared to an optimized choice of the conventional hyperbolic secant (HS) pulse shape. A power constraint is included, as are realistic shape distortions due to power amplifier nonlinearity and the transfer function of the microwave resonator. We find that the NN, FS, and DT parameterizations perform equivalently, offer improvements over the best HS pulses, and contain a large number of equivalent optimal maxima, implying the flexibility to include further constraints or optimization goals in future designs. 

Long electron spin coherence lifetimes are crucial for high sensitivity and resolution in many pulse electron paramagnetic resonance (EPR) experiments aimed at measuring hyperfine and dipolar couplings, as well as in potential quantum sensing applications of molecular spin qubits. In immobilized systems, methyl groups contribute significantly to electron spin decoherence as a result of methyl torsional quantum tunneling. We examine the electron spin decoherence dynamics of the nitroxide radical 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) in both a methyl-free solvent and a methyl-containing solvent at cryogenic temperature. We model nitroxide and solvent methyl effects on decoherence using cluster correlation expansion (CCE) simulations extended to include methyl tunneling and compare the calculations to experimental data. We show that by using the methyl tunneling frequency as a fit parameter, experimental Hahn echo decays can be reproduced fairly well, allowing structural properties to be investigated in silico. In addition, we examine the Hahn echo of a hypothetical system with an unpaired electron and a single methyl to determine the effect of geometric configuration on methyl-driven electron spin decoherence. The simulations show that a methyl group contributes the most to electron spin decoherence if it is located between 2.5 and 6–7 Å from the electron spin, with its orientation being of secondary importance. 

Double electron−electron resonance (DEER) spectroscopy measures distance distributions between spin labels in proteins, yielding important structural and energetic information about conformational landscapes. Analysis of an experimental DEER signal in terms of a distance distribution is a nontrivial task due to the ill-posed nature of the underlying mathematical inversion problem. This work introduces a Bayesian probabilistic inference approach to analyze DEER data, assuming a nonparametric distance distribution with a Tikhonov smoothness prior. The method uses Markov Chain Monte Carlo sampling with a compositional Gibbs sampler to determine a posterior probability distribution over the entire parameter space, including the distance distribution, given an experimental data set. This posterior contains all of the information available from the data, including a full quantification of the uncertainty about the model parameters. The corresponding uncertainty about the distance distribution is visually captured via an ensemble of posterior predictive distributions. Several examples are presented to illustrate the method. Compared with bootstrapping, it performs faster and provides slightly larger uncertainty intervals. 

Herein, we develop an efficient rotamer library-based approach to predict distance distributions from Cu(ii) protein labels.