High-Resolution Metalens Imaging Polarimetry.

Imaging polarimeters find many critical applications in applications ranging from remote sensing to biological detection. Metasurfaces have been proposed as a compact approach for imaging polarimeters, but prior strategies suffer from low imaging resolution. Here, we propose an interleaved metalens configuration for polarization imaging where three-row metasurface units within a group individually interact with three pairs of orthogonal polarization channels. The optical paths between the object and adjacent three-row metasurfaces are nearly equal, allowing the construction of a metalens polarimeter with an unlimited numerical aperture (NA), which is beneficial for high-resolution polarization imaging. The metalens polarimeter fabricated by crystalline silicon nanostructures has a NA of 0.51 at 632.8 nm and achieves an imaging resolution of up to a 1.2-fold wavelength. Polarimetric microscopy experiments demonstrate that metalens polarimeters can realize high-resolution polarization imaging for various microscopic samples. This study offers a promising solution for high-resolution metasurface polarization imaging, with the potential for widespread applications.

Hypophosphite addition to alkenes under solvent-free and non-acidic aqueous conditions.

Hypophosphite adds to alkenes in high yields under solvent-free conditions at elevated temperature, including α,ß-unsaturated carboxylates. The reaction proceeds by a radical mediated pathway. Hypophosphite addition is also effective under non-acidic aqueous conditions employing radical initiators. These methods complement other hypophosphite addition reactions and simplify the synthesis of polyfunctional H-phosphinates.

A framework for automated structure elucidation from routine NMR spectra.

Methods to automate structure elucidation that can be applied broadly across chemical structure space have the potential to greatly accelerate chemical discovery. NMR spectroscopy is the most widely used and arguably the most powerful method for elucidating structures of organic molecules. Here we introduce a machine learning (ML) framework that provides a quantitative probabilistic ranking of the most likely structural connectivity of an unknown compound when given routine, experimental one dimensional 1H and/or 13C NMR spectra. In particular, our ML-based algorithm takes input NMR spectra and (i) predicts the presence of specific substructures out of hundreds of substructures it has learned to identify; (ii) annotates the spectrum to label peaks with predicted substructures; and (iii) uses the substructures to construct candidate constitutional isomers and assign to them a probabilistic ranking. Using experimental spectra and molecular formulae for molecules containing up to 10 non-hydrogen atoms, the correct constitutional isomer was the highest-ranking prediction made by our model in 67.4% of the cases and one of the top-ten predictions in 95.8% of the cases. This advance will aid in solving the structure of unknown compounds, and thus further the development of automated structure elucidation tools that could enable the creation of fully autonomous reaction discovery platforms.