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Plastic scintillators, a class of solid-state materials used for radiation detection, were additively manufactured with vat photopolymerization. The photopolymer resins consisted of a primary dopant and a secondary dopant dissolved in a bisphenol A ethoxylate diacrylate-based matrix. The absorptive dopants significantly influence important print parameters, for example, secondary dopants decrease the light penetration depth by a factor > 12 ×. The primary dopant 2,5-diphenyloxazole had minimal impact on the printing process even when loaded at 25 % by mass of the resin. Working curve measurements, which relate energy dose to cure depth, were performed as a function of feature size to further assess the influence of dopants. Photopatterns smaller than 150 µm width had apparent increases in critical energy dose compared to larger photopatterns, while all resins maintained printed features in line gratings with 50 µm of separation. Printed scintillator monoliths were compared to scintillators cast by traditional molding, demonstrating that the layer-by-layer printing process does not decrease scintillation response. A maximum light output of 31 % of a benchmark plastic scintillator (EJ-200) and successful pulse shape discrimination were achieved with 20 % by mass 2,5-diphenyloxazole as the primary dopant and 0.1 % by mass 9,9-dimethyl-2,7-distyrylfluorene as the secondary dopant in printed scintillator samples.

Exchanging the native surface ligands of CdSe nanocrystals with phenyldithiocarbamate molecules is known to red-shift the absorption spectrum and improve the conductivity of nanocrystal films. However, the mechanism of exchange and the details on the interaction between the nanocrystal surface and phenyldithiocarbamates have not been fully resolved. Using NMR and density functional theory calculations, we show that phenyldithiocarbamates decompose during exchange with native ligands. Phenyldithiocarbamate salts decompose when the cation (triethylammonium in this study) acts as an acid, donating a proton to the 3,5-dimethylphenyldithiocarbamate ligand (DMPTC) producing 3,5-dimethylaniline, carbon disulfide, and other decomposition products. While most decomposition products negligibly interact with the nanocrystal surface, 3,5-dimethylaniline chemically binds to the CdSe nanocrystals. This work demonstrates that the ligand exchange between colloidal nanocrystals and phenyldithiocarbamate ligands occurs in a dynamic system with a variety of molecular species.

Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicansand Pseudomonas aureginosagenomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicansand D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.