The crystallization of paramagnetic species in a magnetic field gradient under microgravity-like conditions is an area of interest for both fundamental and applied science. In this paper, a setup for the crystallization of paramagnetic species in the magnetic field up to 7 T generated by a superconducting magnet is described. The research includes calculations of the conditions necessary to compensate for the gravitational force for several types of paramagnetic substances using the magnetic field of superconducting magnets (4.7 T, 7 T, 9.4 T, and 16.4 T). Additionally, for the first time, the crystallization of copper sulfate and cobalt sulfate, as well as a mixture of copper sulfate and cobalt sulfate under gravitational force compensation in a superconducting magnet, was performed. This paper experimentally demonstrates the feasibility of growing paramagnetic crystals within the volume of a test tube on the example of copper and cobalt sulfate crystals. A comparison of crystals grown from the solution of a mixture of copper and cobalt sulfates under the same conditions, with and without the presence of a magnetic field, showed changes in both the number and size of crystals.

The development and technological applications of molecular spin systems require versatile experimental techniques to characterize and control their static and dynamic magnetic properties. In the latter case, bulk spectroscopic and magnetometric techniques, such as AC magnetometry and pulsed electron paramagnetic resonance, are usually employed, showing high sensitivity, wide dynamic range, and flexibility. They are based on creating a nonequilibrium state either by changing the magnetic field or by applying resonant microwave radiation. Another possible source of perturbation is a laser pulse that rapidly heats the sample. This approach has proven to be one of the most useful techniques for studying the kinetics and mechanism of chemical and biochemical reactions. Inspired by these works, we propose an inductive detection of temperature-induced magnetization dynamics as applied to the study of molecular spin systems and describe the general design and construction of a particular induction probehead, taking into account the constraints imposed by the cryostat and electromagnet. To evaluate the performance, several coordination compounds of VO2+, Co2+, and Dy3+ were investigated using low-energy pulses of a terahertz free electron laser of the Novosibirsk free electron laser facility as a heat source. All measured magnetization dynamics were qualitatively or quantitatively described using a proposed basic theoretical model and compared with the data obtained by alternating current magnetometry. Based on the results of the research, the possible scope of applications of inductive detection and its advantages and disadvantages in comparison with standard methods are discussed.

Photons carrying orbital angular momentum, also known as twisted photons, hold the potential to reveal new insights in atomic and molecular spectroscopy measurements. In this work, we explored magnetic dipole (M1) transitions induced by twisted photons for their application in electron paramagnetic resonance spectroscopy. We calculated the corresponding transition matrix elements and compared them with those of conventional plane wave photons. Our study showed that the matrix elements of twisted photons qualitatively differ from those of plane wave photons and linked this difference to the fact that twisted photon states are a superposition of states with photon spin projections onto the direction of propagation of −1, 0, and 1. According to our study, M1 excitations induced by twisted photons exhibit a spatial dependence on the perpendicular momentum, the radial position of the sample, and the total angular momentum projection of the photon. In addition, we illustrated the characteristic features of M1 transitions induced by twisted photons using model systems with total spin S = 1/2 and S = 3/2, which are the representative of typical organic radicals and high-spin transition metal complexes, respectively. In particular, the S = 3/2 system with zero-field splitting, where the energy levels increase or decrease depending on the spin projection (from −3/2 to +3/2), clearly highlights the qualitative distinctions in excitation behavior introduced by twisted light. Finally, numerical simulations performed for point-like and pellet-like samples under experimentally relevant THz beam conditions corroborate the theoretical predictions and provide a practical framework for future experimental validation.

The reaction of Cu(hfac)₂ and imino nitroxide 2-(pyridin-4-yl)-4,4,5,5,5-tetramethyl-4,5-dihydro-1H-imidazol-1-oxyl (IN) gives the complex [Cu₂(hfac)₄(IN)]_n (1), which has the structure of a chain-polymer with side branches. IN, acting as a μ₃-O,N,N-tridentate ligand, bridges the intrachain Cu atoms through the O_NO atoms of the nitroxide groups and the N_Im atoms of the imidazoline ring of IN, while the N_Py atom of the pyridine cycle binds to the branching Cu atom. The specific disorder of the imino nitroxide fragment in IN and the intrachain Cu(hfac)₂ moieties makes the structure of 1 almost similar to that of the complex [Cu₂(hfac)₄(NN)]_n (2) with nitronyl nitroxide NN, an analog of IN. Thermally induced hysteretic change in the χT value (T↓ = 109 K and T↑ = 119 K) and transformations of EPR spectra were found for the complex 1. Applying pressure above 1.9 kbar not only completly suppresses this magnetic anomaly, but also manifests significant differences in the magnetic behavior of the low-temperature and pressure-stabilized modifications at χT(T) curve. Comparing magnetostructural correlations for 1 at atmospheric and hydrostatic pressure with those for an analogue of nitronyl nitroxide 2 using quantum chemical calculations made it possible to clarify the nature of the magnetic anomaly. It was established that the magnetic transition of 1 is related to the transformation of the trigonal-bipyramidal environment of the branched Cu atom into a square-pyramidal one with a change in the position of the N_Py atom of the pyridine ring from equatorial to axial. EPR spectroscopy clearly indicates the changes of g-tensor components of the branching Cu²⁺ ion, while the g_iso parameter remaining almost unchanged. Magnetic anomaly in 1 revealed by SQUID magnetometry is a unique case of switching the indirect magnetic superexchange {Cu²⁺...IN} through the pyridine fragment during the rearengement of peripheral fragments of the structure.

MaRs is an open-source Python library for the simulation and fitting of electron paramagnetic resonance (EPR) spectra. MaRs supports simulations of field-swept and frequency-swept continuous-wave EPR, accounting for various spin-sublevel polarizations. Moreover, MaRs is capable of simulating complete 2D time-resolved EPR spectra (in field-time domain). The latter can be computed in different levels of theoretical complexity. First, the time evolution can be computed either with kinetic rate equations, which describe population transfer, or in full Liouville space, which captures coherence dynamics. Second, the relaxation pathways in timeresolved simulations can be specified either through intuitive rate parameters that are automatically expanded into the Lindblad master-equation form, or derived microscopically using the Bloch-Redfield formalism. To handle physically complex relaxation scenarios, MaRs provides automatic thermal-balance enforcement and composition operations that allow multicomponent relaxation models to be assembled from simple building blocks. Built on PyTorch (popular platform for machine learning development), MaRs leverages operations on both CPU and CUDA (GPU) backends. We demonstrate MaRs time-resolved EPR capabilities as well the differences and limitations of each built-in approach to time-domain simulations on the examples of coupled triplet-doublet pairs and systems exhibiting the reverse intersystem crossing phenomenon. Separately, we validate stationary EPR simulations across diverse spin systems, including high-spin complexes, achieving excellent agreement with established packages. We also show the possibilities of parameter fitting framework, applicable to all spectrum types implemented in MaRs, that supports multiple optimization strategies and uncertainty quantification.

MaRs is an open-source Python library for the simulation and fitting of electron paramagnetic resonance (EPR) spectra. MaRs supports simulations of field-swept and frequency-swept continuous-wave EPR, accounting for various spin-sublevel polarizations. Moreover, MaRs is capable of simulating complete 2D time-resolved EPR spectra (in field-time domain). The latter can be computed in different levels of theoretical complexity. First, the time evolution can be computed either with kinetic rate equations, which describe population transfer, or in full Liouville space, which captures coherence dynamics. Second, the relaxation pathways in timeresolved simulations can be specified either through intuitive rate parameters that are automatically expanded into the Lindblad master-equation form, or derived microscopically using the Bloch-Redfield formalism. To handle physically complex relaxation scenarios, MaRs provides automatic thermal-balance enforcement and composition operations that allow multicomponent relaxation models to be assembled from simple building blocks. Built on PyTorch (popular platform for machine learning development), MaRs leverages operations on both CPU and CUDA (GPU) backends. We demonstrate MaRs time-resolved EPR capabilities as well the differences and limitations of each built-in approach to time-domain simulations on the examples of coupled triplet-doublet pairs and systems exhibiting the reverse intersystem crossing phenomenon. Separately, we validate stationary EPR simulations across diverse spin systems, including high-spin complexes, achieving excellent agreement with established packages. We also show the possibilities of parameter fitting framework, applicable to all spectrum types implemented in MaRs, that supports multiple optimization strategies and uncertainty quantification.