Release Rates of Short-lived Fission Gases from Modern Spherical Fuel Elements with TRISO-coated Particles

Measurement and prediction of fission product release is important in design, licensing and operation of HTRs. Latest development status is near zero particle manufacturing defects and in-pile failures combined with very low matrix contamination levels. In-pile Release over Birth rate measurements will be described and evaluated. Quantitative predictive analyses of short-lived gas release will be provided for MTR irradiation testing with a focus on the most recent experiment with Chinese fuel from HTR-PM production. For irradiation tests with no single particle failure, models describing particle kernel release are not applicable; instead modeling of matrix contamination release is essential. Good agreement between post calculations and in-line R/B measurements has been achieved.

Amorphous Drug-Polymer Salts

Amorphous formulations provide a general approach to improving the solubility and bioavailability of drugs. Amorphous medicines for global health should resist crystallization under the stressful tropical conditions (high temperature and humidity) and often require high drug loading. We discuss the recent progress in employing drug–polymer salts to meet these goals. Through local salt formation, an ultra-thin polyelectrolyte coating can form on the surface of amorphous drugs, immobilizing interfacial molecules and inhibiting fast crystal growth at the surface. The coated particles show improved wetting and dissolution. By forming an amorphous drug–polymer salt throughout the bulk, stability can be vastly enhanced against crystallization under tropical conditions without sacrificing the dissolution rate. Examples of these approaches are given, along with suggestions for future work.

Programming Self-Assembled Materials With DNA-Coated Colloids

Introducing the concept of programmability paves the way for designing complex and intelligent materials, where the materials’ structural information is pre-encoded in the components that build the system. With highly tunable interactions, DNA-coated particles are promising building elements to program materials at the colloidal scale, but several grand challenges have prevented them from assembling into the desired structures and phases. In recent years, the field has seen significant progress in tackling these challenges, which has led to the realization of numerous colloidal structures and dynamics previously inaccessible, including the desirable colloidal diamond structure, that are useful for photonic and various other applications. We review this exciting progress, focusing in detail on how DNA-coated colloids can be designed to have a sophisticatedly tailored surface, shape, patches, as well as controlled kinetics, which are key factors that allow one to program in principle a limitless number of structures. We also share our view on how the field may be directed in future.

Local Resonant Attenuation of Stress Waves in Particulate Composites

The attenuation of stress waves due to the local resonance is numerically studied using the finite element method (FEM) in this work. The natural frequency of a representative composite unit embedded with coated particles is analyzed and the major factors that influence the natural frequency are examined. Local resonance is inspired when the frequency of the incident stress wave is close to the natural frequency of the particles in the composite. Significant reduction in the amplitude of the stress is obtained when the local resonance occurs, because a large amount of the incident energy is converted to the kinetic energy of the particles, which is rapidly dissipated through the strong oscillations of those particles. It is also observed that the attenuation for the incident stress waves with a range of frequencies can be achieved by using the particles with various local natural frequencies in a composite.

Protocol to generate DNA aptamer coated particles and utilization for affinity-based screening with particle display

Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we present a step-by-step protocol for utilizing particle display to select DNA aptamers for a 25 kDa protein biomarker neutrophil gelatinase-associated lipocalin (NGAL).