Search for: All records

We present results of a search for spin-independent dark matter-nucleus interactions in a $1{\mathrm{cm}}^2$by 1 mm thick (0.233 g) high-resolution silicon athermal phonon detector operated above ground. For interactions in the substrate, this detector achieves an rms baseline energy resolution of $361.5\left(4\right)\mathrm{m}\mathrm{eV}$(statistical error), the best for any athermal phonon detector to date. With an exposure of $0.233\mathrm{g}\times 12$hours, we place the most stringent constraints on dark matter masses between 44 and $87\mathrm{M}\mathrm{eV}/{\mathrm{c}}^2$, with the lowest unexplored cross section of $4\times {10}^{-32}{\mathrm{c}\mathrm{m}}^2$at $87\mathrm{M}\mathrm{eV}/{\mathrm{c}}^2$. We employ a conservative salting technique to reach the lowest dark matter mass ever probed via direct detection experiment. This constraint is enabled by two-channel rejection of low energy backgrounds that are coupled to individual sensors. 

Permeability of binary mixtures of soils is important for several industrial and engineering applications. Previous models for predicting the permeability of a binary mixture of soils were primarily developed from Kozeny–Carman equation with an empirical approach. The permeability is predicted based on an equivalent particle size of the two species. This study is aimed to develop a model using a more fundamental approach. Instead of an equivalent particle size, the permeability is predicted based on the bimodal void sizes of the binary mixture. Because the bimodal void sizes are not available as commonly measured physical properties. We first develop an analytical method that has the capability of predicting the bimodal void sizes of a binary mixture. A permeability model is then developed based on the bimodal void sizes of the binary mixture. The developed permeability model is evaluated by comparing the predicted and experimentally measured results for binary mixtures of glass beads, crush sand, and gravel sand. The findings can contribute to a better understanding of the important influence of pore structure on the prediction of permeability.