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 public MS/MS spectra. Annotations were propagated 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 through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Even though raw mass spectrometry data is information rich, the vast majority of the data is underutilized. The ability to interrogate these rich datasets is handicapped by the limited capability and flexibility of existing software. We introduce the Mass Spec Query Language (MassQL) that addresses these issues by enabling an expressive set of mass spectrometry patterns to be queried directly from raw data. MassQL is an open-source mass spectrometry query language for flexible and mass spectrometer manufacturer-independent mining of MS data. We envision the flexibility, scalability, and ease of use of MassQL will empower the mass spectrometry community to take fuller advantage of their mass spectrometry data and accelerate discoveries.

Novel multiple emitting amphiphilic conjugated polythiophene‐coated CdTe quantum dots for picogram level determination of the 2,4,6‐trinitrophenol (TNP) explosive are developed. Four biocompatible sensors, cationic polythiophene nanohybrids (CPTQDs), nonionic polythiophene nanohybrids (NPTQDs), anionic polythiophene nanohybrids (APTQDs), and thiophene copolymer nanohybrids (TCPQDs), are designed using an in situ polymerization method, which shows highly enhanced fluorescence intensity and quantum yield (up to 78%). All sensors are investigated for nitroexplosive detection to provide a remarkable fluorescence quenching for TNP and the quenching efficiency reached 96% in the case of TCPQDs. The fluorescence of the sensors are quenched by TNP through inner filter effect, electrostatic, π−π, and hydrogen bonding interactions. Under optimal conditions, the detection limits of CPTQDs, NPTQDs, APTQDs, and TCPQDs are 2.56, 7.23, 4.12, and 0.56 × 10−9 m, respectively, within 60 s. More importantly, portable, cost effective, and simple to use paper strips and chitosan film are successfully applied to visually detect as little as 2.29 pg of TNP. The possibility of utilizing a smartphone with a color‐scanning APP in the determination of TNP is also established. Moreover, the practical application of the developed sensors for TNP detection in tap and river water samples is described with satisfactory recoveries of 98.02−107.50%.

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