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Imaging the Local Charge Environment of Nitrogen-Vacancy Centers in Diamond
Characterizing the local internal environment surrounding solid-state spin defects is crucial to harnessing them as nanoscale sensors of external fields. This is especially germane to the case of defect ensembles which can exhibit a complex interplay between interactions, internal fields, and lattice strain. Working with the nitrogen-vacancy (NV) center in diamond, we show that local electric fields dominate the magnetic resonance behavior of NV ensembles at a low magnetic field. We propose a simple microscopic model that quantitatively captures the observed spectra for samples with NV concentrations spanning more than two orders of magnitude. Motivated by this understanding, we propose and implement a new method for the nanoscale localization of individual charges within the diamond lattice; our approach relies upon the fact that the charge induces a NV dark state which depends on the electric field orientation.
Real-space magnetic imaging of the multiferroic spinels MnV 2 O 4 and Mn 3 O 4
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Pushing the limits: an instrument for hard X-ray imaging below 20 nm
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Optimizing a Compressive Imager for Machine Learning Tasks.
Abstract not provided.
Optimizing Multi-Image Sort-Last Parallel Rendering
Abstract not provided
Additional Capabilities of a Compact Neutron Scatter Camera: Active Interrogation Time-Correlated Pulse-Height Multiplication Measurements and Gamma Imaging.
Abstract not provided.
Learning Shapes on Image Sampled Points with Dynamic Graph CNNs
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Effects of management and pore characteristics on organic matter composition of macroaggregates: evidence from characterization of organic matter and imaging
Macroaggregates are of interest because of their fast response to land management and their role in the loss or restoration of soil organic carbon (SOC). The study included two experiments. In Experiment I, we investigated the effect of long-term (27 years) land management on the chemical composition of organic matter (OM) of macroaggregates. Macroaggregates were sampled from topsoil under conventional cropping, cover cropping and natural succession systems. The OM of macroaggregates from conventional cropping was more decomposed than that of cover cropping and especially natural succession, based on larger δ 15 N values and decomposition indices determined by multiple magic-angle spinning nuclear magnetic resonance ( 13 C CP/MAS NMR) and Fourier transform infrared (FTIR) spectroscopy. Previous research at the sites studied suggested that this was mainly because of reduced diversity and activity of the decomposer community, change in nutrient stoichiometry from fertilization and contrasting formation pathways of macroaggregates in conventional cropping compared with cover cropping and, specifically, natural succession. In Experiment II, we investigated the relation between OM composition and pore characteristics of macroaggregates. Macroaggregates from the natural succession system only were studied. We determined 3-D pore-size distribution of macroaggregates with X-ray microtomography, for which we cut the macroaggregates into sections that had contrasting dominant pore sizes. Then, we characterized the OM of macroaggregate sections with FTIR and δ 15 N methods. The results showed that within a macroaggregate, the OM was less decomposed in areas where the small (13–32 µm) or large (136–260 µm) pores were abundant. This was attributed to the role of large pores in supplying fresh OM and small pores in the effective protection of OM in macroaggregates. Finally, previous research at the site studied had shown increased abundance of large and small intra-aggregate pores following adoption of less intensive management systems. It appears that land management can alter the OM composition of macroaggregates, partly by the regulation of OM turnover at the intra-aggregate scale.
Use Of Convolutional Neural Network Image Classification And High-Speed Ion Probe Data Towards Real-Time Detonation Characterization In A Water-Cooled Rotating Detonation Engine
Conference paper for the ASME 2022 Turbomachinery Technical Conference & ExpositionJune 13-17, 2022, Rotterdam, The Netherlands<br>