Bilinear rotations like BIRD, TANGO, BANGO, and BIG-BIRD are essential building blocks in modern nuclear magnetic resonance (NMR) spectroscopy that allow the rotation of an isolated spin without couplings (i.e., bilinear interactions) in one way, while rotating spins with a matched coupling in another way. Two ways for constructing particularly robust bilinear rotations (compensated for couplings, offsets, and B₁ inhomogeneities, or COB/COB3) are provided and demonstrated in both theory and experiment.

This research reveals the hidden rules that connect the perfect mirror-image shape of a high-resolution nuclear magnetic resonance spectrum to the properties of a molecule's nuclear spin system. We found that this symmetry occurs only when specific, balanced conditions are met within the spin system. Testing these rules on various theoretical models confirmed their universal nature, providing a new framework for interpreting molecular symmetry from spectral patterns.

The developed probe head, which is a combination of the bimodal resonator operating in transmission mode and the low-noise amplifier, led to at least a 2-fold improvement in the sensitivity of the electron paramagnetic resonance spectrometer independent of sample temperature. The probe head is compatible with commercial Bruker spectrometers without their modification.

Magic-angle-spinning nuclear magnetic resonance (NMR) is ideal for studying protein structure and dynamics. Introducing fluorine atoms offers advantages due to the NMR properties of ¹⁹F and the absence of natural fluorine. However, the slow spin polarisation recovery of ¹⁹F causes long recycle delays between scans. We demonstrate that adding paramagnetic co-solutes to solid protein samples largely accelerates these experiments, and we identify optimal conditions for this approach.

We developed a free online tool that helps researchers and students analyze nuclear magnetic resonance signals and extract meaningful information about materials such as foods, plants, and soils. The platform simplifies complex data processing and removes the need for specialized software. Tests with simulated and real data show reliable results, making advanced analysis more accessible for science and education.