Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification.

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra.

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets.

Fullerene-Assisted Photoinduced Charge Transfer of Single-Walled Carbon Nanotubes through a Flavin Helix.

One of the greatest challenges with single-walled carbon nanotube (SWNT) photovoltaics and nanostructured devices is maintaining the nanotubes in their pristine state (i.e., devoid of aggregation and inhomogeneous doping) so that their unique spectroscopic and transport characteristics are preserved.