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Reaction mechanisms for selective hydrogenation of aromatic hydrocarbons to cycloolefins and cycloalkanes over noble metal catalysts were studied at the molecular level. Reactive intermediates were identified by combining experimental IR and Raman spectroscopic measurements with DFT calculations and related to catalytic activity and selectivity differences. Improved catalyst formulations were developed. 

This work proves the feasibility of utilizing steady state and transient in situ/operando spectroscopy to extract mechanistic information that reduces and leads to robust kinetic models. It also opens new avenues to explore kinetics and mechanisms with charge transfer data in heterogeneous catalysis. 

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. 

The development of simple in situ spectrokinetic techniques to assess intermediate species nature and adsorption location can benefit catalytic studies by providing insights into the reasons for different catalysts performance and facilitate mechanistic proposals. 

Abstract There is ample evidence for magnetic reconnection in the solar system, but it is a nontrivial task to visualize, to determine the proper approaches and frames to study, and in turn to elucidate the physical processes at work in reconnection regions from in-situ measurements of plasma particles and electromagnetic fields. Here an overview is given of a variety of single- and multi-spacecraft data analysis techniques that are key to revealing the context of in-situ observations of magnetic reconnection in space and for detecting and analyzing the diffusion regions where ions and/or electrons are demagnetized. We focus on recent advances in the era of the Magnetospheric Multiscale mission, which has made electron-scale, multi-point measurements of magnetic reconnection in and around Earth’s magnetosphere. 

With the proliferation of connected internet of things (IoT) devices, trusted communications between such devices is an increasing concern. While researchers have spent significant resources to address this challenge, most solutions impose significant energy, delay, and complexity overhead on energy-constrained IoT devices. In this paper, we first provide an overview of some of the techniques used to incorporate security and trust features into IoT devices. Then, we propose and demonstrate an innovative encryption approach for wireless IoT communications which is low-energy, low-complexity, and lowlatency. The proposed cryptography integrates the encryption into the RF front-end of a wireless transceiver and is energyefficient, making it suitable for real-time and energy-limited IoT connectivity applications. 

Abstract We present observations that suggest the X-line of guide-field magnetic reconnection is not necessarily orthogonal to the plane in which magnetic reconnection is occurring. The plane of magnetic reconnection is often referred to as theL–Nplane, whereLis the direction of the reversing and reconnecting magnetic field andNis normal to the current sheet. The X-line is often assumed to be orthogonal to theL–Nplane (defined as theM-direction) in the majority of theoretical studies and numerical simulations. The four-satellite Magnetospheric Multiscale (MMS) mission, however, observes a guide-field magnetic reconnection event in Earth’s magnetotail in which the X-line may be oblique to theL–Nplane. This finding is somewhat opportune as two of the MMS satellites at the sameNlocation report nearly identical observations with no significant time delays in the electron diffusion region (EDR) even though they have substantial separation inL. A minimum directional derivative analysis suggests that the X-line is between 40° and 60° fromM, adding support that the X-line is oblique. Furthermore, the measured ion velocity is inconsistent with the apparent motion of the MMS spacecraft in theL-direction through the EDR, which can be resolved if one assumes a shear in theL–Nplane and motion in theM-direction. A nonorthogonal X-line, if somewhat common, would call for revisiting theory and simulations of guide-field magnetic reconnection, reexamination of how the reconnection electric field is supported in the EDR, and reconsidering the large-scale geometry of the X-line. 

We present LBW-Net, an efficient optimization based method for quantization and training of the low bit-width convolutional neural networks (CNNs). Specifically, we quantize the weights to zero or powers of 2 by minimizing the Euclidean distance between full-precision weights and quantized weights during back-propagation (weight learning). We characterize the combinatorial nature of the low bit-width quantization problem. For 2-bit (ternary) CNNs, the quantization of N weights can be done by an exact formula in O(N log N) complexity. When the bit-width is 3 and above, we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally inexpensive. The free parameter is further determined by network retraining and object detection tests. The LBW-Net has several desirable advantages over full-precision CNNs, including considerable memory savings, energy efficiency, and faster deployment. Our experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart, the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks, and can even do better in real world visual scenes, while empirically enjoying more than 4× faster deployment.