Modification of a desktop FFF printer via NIR laser addition for upconversion 3D printing.

Traditional photopolymer-based 3D printing methods require sequential printing of thin layers, due to short penetration depths of UV or blue light sources used by these techniques. In contrast, upconversion 3D printing circumvents the layer-by-layer limitation by taking advantage of upconversion luminescence processes and the high penetration depths offered by near-infrared (NIR) lasers, allowing for selective crosslinking of voxels at any depth or position within the resin container. The implementation of this technique required the construction of a 3D printer with the ability of focusing the laser on any point of the space.

Molecular dynamics simulations of displacement cascades in LiAlO and LiAlO ceramics.

Molecular dynamics was employed to investigate the radiation damage due to collision cascades in LiAlO and LiAlO, the latter being a secondary phase formed in the former during irradiation. Atomic displacement cascades were simulated by initiating primary knock-on atoms (PKA) with energy values = 5, 10 and 15 keV and the damage was quantified by the number of Frenkel pairs formed for each species: Li, Al and O. The primary challenges of modeling an ionic system with and without a core-shell model for oxygen atoms were addressed and new findings on the radiation resistance of these ceramics are presented.

Tag and Snag: A New Platform for Bioactive Natural Product Screening from Mixtures.

Natural products provide an unparalleled diversity of small molecules to fuel drug screening efforts, but deconvoluting the pharmacological activity of natural product mixtures to identify key bioactive compounds remains a vexing and labor-intensive process. Therefore, we have developed a new platform to probe the non-specific pharmacological potential of compounds present in common dietary supplements via shotgun derivatization with isotopically labeled propanoic acid, a live cell affinity assay, which was used to selectively recognize the population of compounds which bind tightly to HeLa cells in culture, and a computational LC-MS data analysis of isotopically labeled compounds from cell lysate. The data analysis showed that hundreds of compounds were successfully derivatized in each extract, and dozens of those compounds showed high affinity for HeLa cells.

Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels.

Three probabilistic methodologies are developed for predicting the long-term creep rupture life of 9-12 wt%Cr ferritic-martensitic steels using their chemical and processing parameters. The framework developed in this research strives to simultaneously make efficient inference along with associated risk, i.e.