Neutronic Analysis for Transmutation of Minor Actinides and Long-Lived Fission Products in a Fusion-Driven Transmuter (FDT)

This study presents the transmutations of both the minor actinides (MAs: ²³⁷Np, ²⁴¹Am, ²⁴³Am and ²⁴⁴Cm) and the long-lived fission products (LLFPs: ⁹⁹Tc, ¹²⁹I and ¹³⁵Cs), discharged from high burn-up PWR-MOX spent fuel, in a fusion-driven transmuter (FDT) and the effects of the MA and LLFP volume fractions on their transmutations. The blanket configuration of the FDT is improved by analyzing various sample blanket design combinations with different radial thicknesses. Two different transmutation zones (TZ_MA and TZ_FP which contain the MA and LLFP nuclides, respectively) are located separately from each other. The volume fractions of the MA and the LLFP are raised from 10 to 20% stepped by 2% and from 10 to 80% stepped by 5%, respectively. The calculations are performed to estimate neutronic parameters and transmutation characteristics per D–T fusion neutron. The conversion ratios (CRs) for the whole of all MAs are about 65–70%. The transmutation rates of the LLFP nuclides increase linearly with the increase of volume fractions of the MA, and the ⁹⁹Tc nuclide among them has the highest transmutation rate. The variations of their transmutation rate per unit volume in the radial direction are quasi-concave parabolic.     

Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.     

A multilayer tissue engineered meniscus substitute

Various methods have been tried to treat the main meniscus problem, meniscal tears, for which we believe tissue engineering could be a viable solution. In this study, a three dimensional, collagen-based meniscus substitute was prepared by tissue engineering using human fibrochondrocytes and a collagen based-scaffold. This construct was made with 3 different collagen-based foams interspaced with two electrospun nano/microfibrous mats. The top layer was made of collagen type I–chondroitin sulfate–hyaluronic acid (Coll–CS–HA), and the middle and the bottom layers were made of only collagen type I with different porosities and thus with different mechanical properties. The mats of aligned fibers were a blend of collagen type I and poly(l-lactic acid-co-glycolic acid) (PLGA). After seeding with human fibrochondrocytes, cell attachment, proliferation, and production of extracellular matrix and glucoseaminoglycan were studied. Cell seeding had a positive effect on the compressive properties of foams and the 3D construct. The 3D construct with all its 5 layers had better mechanical properties than the individual foams.