Biosensing based on nanophotonic structures has shown a great potential for cost-efficient, high-speed and compact personal medical diagnostics. While plasmonic nanosensors offer high sensitivity, their intrinsically restricted resonance quality factors and strong heating due to metal absorption impose severe limitations on real life applications. Here, we demonstrate an all-dielectric sensing platform based on silicon nanodisks with strong optically-induced magnetic resonances, which are able to detect a concentration of streptavidin of as low as 10 M (mol L) or 5 ng mL, thus pushing the current detection limit by at least two orders of magnitudes. Our study suggests a new direction in biosensing based on bio-compatible, non-toxic, robust and low-loss dielectric nanoresonators with potential applications in medicine, including disease diagnosis and drug detection.

Paramagnetic chemical probes have been used in electron paramagnetic resonance (EPR) and nuclear magnetic resonance (NMR) spectroscopy for more than four decades. Recent years witnessed a great increase in the variety of probes for the study of biological macromolecules (proteins, nucleic acids, and oligosaccharides). This Review aims to provide a comprehensive overview of the existing paramagnetic chemical probes, including chemical synthetic approaches, functional properties, and selected applications. Recent developments have seen, in particular, a rapid expansion of the range of lanthanoid probes with anisotropic magnetic susceptibilities for the generation of structural restraints based on residual dipolar couplings and pseudocontact shifts in solution and solid state NMR spectroscopy, mostly for protein studies. Also many new isotropic paramagnetic probes, suitable for NMR measurements of paramagnetic relaxation enhancements, as well as EPR spectroscopic studies (in particular double resonance techniques) have been developed and employed to investigate biological macromolecules. Notwithstanding the large number of reported probes, only few have found broad application and further development of probes for dedicated applications is foreseen.

Narrow proton signals, high sensitivity, and efficient coherence transfers provided by fast magic-angle spinning at high magnetic fields make automated projection spectroscopy feasible in protein solid-state NMR. We present the first ultra-high dimensional implementation of this approach where 5D peak lists are reconstructed from a number of 2D projections for protein samples of different molecular size and aggregation state, featuring limited dispersion of chemical shifts or inhomogeneous broadenings. The resulting datasets are particularly suitable to automated analysis, yielding rapid and unbiased backbone resonance assignments. under the European Union's Horizon 2020 research and innovation programme (ERC-2015-CoG GA 648974), by the CNRS (IR-RMN FR3050), and by the EU-project iNext (GA 653706). HO was supported by the Westpac Foundation with a Future Leaders Scholarship, and JS by the EC's REA with a MSCA fellowship (GA 661799).

Abstract. Paramagnetic metal ions with fast-relaxing electrons generate pseudocontact shifts (PCSs), residual dipolar couplings (RDCs), paramagnetic relaxation enhancements (PREs) and cross-correlated relaxation (CCR) in the nuclear magnetic resonance (NMR) spectra of the molecules they bind to. These effects offer long-range structural information in molecules equipped with binding sites for such metal ions. Here we present the new open-source software Paramagpy, which has been written in Python 3 with a graphic user interface. Paramagpy combines the functionalities of different currently available programs to support the fitting of magnetic susceptibility tensors using PCS, RDC, PRE and CCR data and molecular coordinates in Protein Data Bank (PDB) format, including a convenient graphical user interface. Paramagpy uses efficient fitting algorithms to avoid local minima and supports corrections to back-calculated PCS and PRE data arising from cross-correlation effects with chemical shift tensors. The source code is available from https://doi.org/10.5281/zenodo.3594568 (Orton, 2019).