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Abstract Agriculture is the dominant source of anthropogenic nitrous oxide (N₂O) –a greenhouse gas and a stratospheric ozone depleting substance. The US Corn Belt is a large global N₂O source, but there remain large uncertainties regarding its source attribution and biogeochemical pathways. Here, we interpret high frequency stable N₂O isotope observations from a very tall tower to improve our understanding of regional source attribution. We detected significant seasonal variability in δ¹⁵N_bulk(6.47–7.33‰) and the isotope site preference (δ¹⁵N^SP = δ¹⁵N^α–δ¹⁵N^β, 18.22–25.19‰) indicating a predominance of denitrification during the growing period but of nitrification during the snowmelt period. Isotope mixing models and atmospheric inversions both indicate that indirect emissions contribute substantially (>35%) to total N₂O emissions. Despite the relatively large uncertainties, the upper bound of bottom‐up indirect emission estimates are at the lower bound of the isotopic constraint, implying significant discrepancies that require further investigation. 

Carbon fiber reinforced polymer (CFRP) composites are uniquely essential materials in the aerospace, automobile, energy, sporting, and an increasing number of other industries. Consequently, we are amassing an accumulation of CFRP waste latent in value. Electrochemical techniques to recycle carbon fiber reinforced polymers have recently emerged as viable methods to remove the composite matrix from these materials and recover fibers. In many of these techniques, the composite is immersed in a solvent and acts as an electrochemical anode while a voltage is applied to the electrolytic cell. Still, few methods leverage the conductivity of the composite to mediate its own disassembly. We have introduced an electrolytic method that leverages this conductivity to electrolyze acetic acid to form methyl radicals that cleave the C-N bonds of the epoxy matrix and cleanly separate ordered fibers from the matrix. This talk will discuss the motivation and development for this new electrochemical method and explain the chemical mechanism through which it works. 

Abstract Differentiable rendering of translucent objects with respect to their shapes has been a long‐standing problem. State‐of‐the‐art methods require detecting object silhouettes or specifying change rates inside translucent objects—both of which can be expensive for translucent objects with complex shapes. In this paper, we address this problem for translucent objects with no refractive or reflective boundaries. By reparameterizing interior components of differential path integrals, our new formulation does not require change rates to be specified in the interior of objects. Further, we introduce new Monte Carlo estimators based on this formulation that do not require explicit detection of object silhouettes. 

This paper details the development and analysis of a computational neuroscience model, known as a Synthetic Nervous System, for the control of a simulated worm robot. Using a Synthetic Nervous System controller allows for adaptability of the network with minimal changes to the system. The worm robot kinematics are inspired by earthworm peristalsis which relies on the hydrostatic properties of the worm’s body to produce soft-bodied locomotion. In this paper the hydrostatic worm body is approximated as a chain of two dimensional rhombus shaped segments. Each segment has rigid side lengths, joints at the vertices, and a linear actuator to control the segment geometry. The control network is composed of non-spiking neuron and synapse models. It utilizes central pattern generators, coupled via interneurons and sensory feedback, to coordinate segment contractions and produce a peristaltic waveform that propagates down the body of the robot. A direct perturbation Floquet multiplier analysis was performed to analyze the stability of the peristaltic wave’s limit cycle. 

The recent report by American Society of Civil Engineers gave the nation's bridges an unimpressive C grade. Across the country, more than 617,000 highway bridges: 46,154 structurally deficient and 42% 50+ years old. Continuous bridge assessment is essential to protect public safety. Federal Highway Administration requires all highway bridges inspected once every 24 months. However, any drastic change on bridges within 24 months will be left undetected. Nonetheless, bridge inspection is time-consuming and labor-intensive. Civil engineers have been using bridge health monitoring (BHM) systems with wired and/or wireless sensors to measure structural response (e.g., displacement, strain, acceleration) of a bridge. The response measurements are then converted to the information related to structural health for assessment. State-of-the-art BHM technology deploys sensor networks to facilitate data connection. Installing cables is expensive and subject to extreme weather. Wireless solutions face challenges such as energy consumption. Sensors are battery-powered. Another not well-publicized problem is security threats inherited in wireless networks. Our approach to wireless BHM is to utilize sensors networkless by collecting data with a drone. Similar to a mail carrier who goes around and picks up the mail, a drone collects data from sensors throughout the bridge. A drone eliminates restrictions for civil engineers on node placement since the drone replaces sink nodes. Networkless makes BHM less prone to attacks such as Jamming and DoS. To secure access, we deploy a Needham-Schroeder authentication protocol for the drone to collect data from sensor nodes securely. Networkless sensing for BHM benefits energy efficiency. It saves battery life as the sensor nodes remain asleep until scheduled transmission or woken up by a drone. It reduces design complexity and operation energy. The system also assures security since there is no vulnerable network to be attacked. 

When subjected to the lap shear testing, spot welds created by brazing, resistance welding, or other techniques may fail either by a plug failure mode (also called pull-out mode) or an interfacial shear failure mode. In the past, plug failure mode was thought to be depend- ent on base metal ultimate tensile strength, spot diameter and plate thickness, while interfacial failure can be determined by interface shear strength and spot area. No fracture mechanics model or failure process is invoked in such an approach, and its predictive capability is often doubted compared to realistic experiments. This work conducts a parametric study to assess the failure behavior as a function of dominant three-dimensional geometric parameters based on the Gurson-Tvergaard-Needleman (GTN) damage mechanics model and no-damage mod- el respectively. Different necking conditions are considered as precursors to the two failure modes in the no-damage model. It is found out that a small ratio of spot diameter to plate thickness promotes interfacial shear failure while a large ratio favors plug failure. Other geometric parameters such as the filler interlayer thickness, if used, play a secondary role. The calculated peak force Fwt is not much different between the GTN and no-damage analyses, and better agreement is shown in the small nugget region. Normalized peak force calculated from the GTN model with the porosity f0 set to 0.01 showed the best agreement with pervious tensile shear tests on spot-welded DP980 lap joints in comparison to that calculated from the GTN model with f0 at 0.02 and the no-damage model. Note that heterogeneous distribution of materi- al strength across the joint region was considered in the GTN model, which was estimated based on the hardness map measured across the joint cross section.