Highly sensitive plasmonic paper substrate fabricated via amphiphilic polymer self-assembly in microdroplet for detection of emerging pharmaceutical pollutants.

We report an innovative and facile approach to fabricating an ultrasensitive plasmonic paper substrate for surface-enhanced Raman spectroscopy (SERS). The approach exploits the self-assembling capability of poly(styrene-b-2-vinyl pyridine) block copolymers to form a thin film at the air-liquid interface within the single microdroplet scale for the first time and the subsequent in situ growth of silver nanoparticles (AgNPs). The concentration of the block copolymer was found to play an essential role in stabilizing the droplets during the mass transfer phase and formation of silver nanoparticles, thus influencing the SERS signals. SEM analysis of the morphology of the plasmonic paper substrates revealed the formation of spherical AgNPs evenly distributed across the surface of the formed copolymer film with a size distribution of 47.5 nm. The resultant enhancement factor was calculated to be 1.2 × 107, and the detection limit of rhodamine 6G was as low as 48.9 pM. The nanohybridized plasmonic paper was successfully applied to detect two emerging pollutants-sildenafil and flibanserin-with LODs as low as 1.48 nM and 3.45 nM, respectively. Thus, this study offers new prospects for designing an affordable and readily available, yet highly sensitive, paper-based SERS substrate with the potential for development as a lab-on-a-chip device.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics.

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.