An electron beam is detected by a 1D floating potential probe array in a relatively high density (10e12 − 10e13 cm−3) and low temperature (∼ 5 eV) plasma of the Magnetic Reconnection Experiment (MRX). Clear perturbations in the floating potential profile by the electron beam are observed. Based on the floating potential profile and a current balance equation to the probe array tips, the effective width of the electron beam is determined, from which we determine the radial and toroidal beam current density profiles. After the profile of the electron beam is specified from the measured beam current, we demonstrate the consistency of the current balance equation and the location of the perturbation is also in agreement with field line mapping. No significant broadening of the electron beam is observed after the beam propagates for tens of centimeters through the high density plasma. These results prove that the field line mapping is, in principle, possible in high density plasmas.
In our study, we compare the three dimensional (3D) morphologic characteristics of Earth's first reef-building animals (archaeocyath sponges) with those of modern, photosynthetic corals. Within this repository are the 3D image data products for both groups of animals. The archaeocyath images were produced through serial grinding and imaging with the Grinding, Imaging, and Reconstruction Instrument at Princeton University. The images in this repository are the downsampled data products used in our study, and the full resolution (>2TB) image stacks are available upon request from the author. For the coral image data, the computed tomography (CT) images of all samples are included at full resolution. Also included in this repository are the manual and automated outline coordinates of the archaeocyath and coral branches, which can be directly used for morphological study.
The dataset is a compilation of real time ground observations of criteria pollutants monitored at the Central Pollution Control Board (CPCB) continuous stations in India, from 2015-2019. Pollutants included are PM2.5, PM10, SO2, NO2 and O3 and are archived at every hour for all stations across India.
Since 1850 the concentration of atmospheric methane (CH4), a potent greenhouse gas, has more than doubled. Recent studies suggest that emission inventories may be missing sources and underestimating emissions. To investigate whether offshore oil and gas platforms leak CH4 during normal operation, we measured CH4 mole fractions around eight oil and gas production platforms in the North Sea which were neither flaring gas nor off-loading oil. We use the measurements from summer 2017, along with meteorological data, in a Gaussian plume model to estimate CH4 emissions from each platform. We find CH4 mole fractions of between 11 and 370 ppb above background concentrations downwind of the platforms measured, corresponding to a median CH4 emission of 6.8 g CH4 s-1 for each platform, with a range of 2.9 to 22.3 g CH4 s-1. When matched to production records, during our measurements individual platforms lost between 0.04% and 1.4% of gas produced with a median loss of 0.23%. When the measured platforms are considered collectively, (i.e. the sum of platforms’ emission fluxes weighted by the sum of the platforms’ production), we estimate the CH4 loss to be 0.19% of gas production. These estimates are substantially higher than the emissions most recently reported to the National Atmospheric Emission Inventory (NAEI) for total CH4 loss from United Kingdom platforms in the North Sea. The NAEI reports CH4 losses from the offshore oil and gas platforms we measured to be 0.13% of gas production, with most of their emissions coming from gas flaring and offshore oil loading, neither of which were taking place at the time of our measurements. All oil and gas platforms we observed were found to leak CH4 during normal operation and much of this leakage has not been included in UK emission inventories. Further research is required to accurately determine total CH4 leakage from all offshore oil and gas operations and to properly include the leakage in national and international emission inventories.
Zhou, Mi; Peng, Liqun; Zhang, Lin; Mauzerall, Denise L.
Abstract:
This dataset is created for the paper titled 'Environmental Benefits and Household Costs of Clean Heating Options in Northern China' and published on Nature Sustainability. Based on a 2015 regional anthropogenic emission inventory (base case), we propose seven counterfactual scenarios in which all 2015 residential solid fuel heating in northern China switches to one of the following non-district heating options: clean coal with improved stoves (CCIS), natural gas heaters (NGH), resistance heaters (RH), or air-to-air heat pumps (AAHP). This dataset provides the following gridded information for the base case and each clean heating scenario: (1) annual residential heating emissions for PM2.5/NOx/SO2; (2) monthly mean surface PM2.5 concentrations from the WRF-Chem model; (3) annual PM2.5-related premature deaths calculated by the GEMM model; (4) 2015 population in China; (5) mask for provinces in China; (6) longitude and latitude of each grid center.
Numerical data is tabulated for all plots (Figures 2, 3a-b, 4-89, S1, S4a-b,d, S5a-b,d, S6-S156) and included as separate spreadsheets categorized by figure in a .zip file in the Supplementary Material. Error bars in Figure 4 show the spread of data observed for 4 and 5 trials on independent samples for MIL-101 and MOF-235, respectively. Figure 6a shows the average of triplicate filtrate test conversions with error propagated based on this spread. Figures 6b and S165 error bars on rate constants are determined based on propagated conversion uncertainty for independent trials and extracted standard deviations of pseudo-first order rate constants from linearized plots. Error bars on other plots represent propagation of experimental uncertainty on single trials.
This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations.
Pacheco, Diego A; Thiberge, Stephan; Pnevmatikakis, Eftychios; Murthy, Mala
Abstract:
Sensory pathways are typically studied starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analysis of activity towards the earliest layers of processing. Here, we present new methods for volumetric neural imaging with precise across-brain registration, to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals, and sexes. We discover that auditory activity is present in most central brain regions and in neurons responsive to other modalities. Auditory responses are temporally diverse, but the majority of activity is tuned to courtship song features. Auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway, and this variability is largely independent of spontaneous movements. This study highlights the power of using an unbiased, brain-wide approach for mapping the functional organization of sensory activity.
Magnetic reconnection is a fundamental process at work in laboratory, space and astrophysical plasmas, in which magnetic field lines change their topology and convert magnetic energy to plasma particles by acceleration and heating. One of the most important problems in reconnection research has been to understand why reconnection occurs so much faster than predicted by MHD theory. Following the recent pedagogical review of this subject [M. Yamada, R. Kulsrud, and H. Ji, Rev. Mod. Phys. {\bf 82}, 603 (2010)], this paper presents a review of more recent discoveries and findings in the research of fast magnetic reconnection in laboratory, space, and astrophysical plasmas. In spite of the huge difference in physical scales, we find remarkable commonality between the characteristics of the magnetic reconnection in laboratory and space plasmas. In this paper, we will focus especially on the energy flow, a key feature of the reconnection process. In particular the experimental results on the energy conversion and partitioning in a laboratory reconnection layer [M. Yamada {\it et al.}, Nat. Commu. {\bf 5}, 4474 (2014)] are discussed and compared with quantitative estimates based on two-fluid analysis. In the Magnetic Reconnection Experiment (MRX), we find that energy deposition to electrons is localized near the X-point and is mostly from the electric field component perpendicular to the magnetic field. The mechanisms of ion acceleration and heating are also identified and a systematic and quantitative study on the inventory of converted energy within a reconnection layer with a well-defined but variable boundary. The measured energy partition in a reconnection region of similar effective size ($L \approx$ 3 ion skin depths) of the Earth's magneto-tail [J. Eastwood {\it et al.}, Phys. Rev. Lett. {\bf 110}, 225001 (2013)] is notably consistent with our laboratory results. Finally, to study the global aspects of magnetic reconnection, we have carried out a laboratory experiment on the stability criteria for solar flare eruptions, including {\textquotedblleft}storage and release{\textquotedblright} mechanisms of magnetic energy. We show that toroidal magnetic flux generated by magnetic relaxation (reconnection) processes generates a new stabilizing force which prevents plasma eruption. This result has lead us to discovery of a new stabilizing force for solar flares [C. E. Myers {\it et al.}, Nature {\bf 528}, 526 (2015)]
This dataset contains example input files, training data sets and potential files related to the publication "First-principles-based Machine Learning Models for Phase Behavior and Transport Properties of CO2." by Mathur et al (2023). In this work, we developed machine learning models for CO2 based on different exchange-correlation DFT functionals. We assessed their performance on liquid densities, vapor-liquid equilibrium and transport properties.
The materials include codes and example input / output files for Monte Carlo simulations of lattice chains in the grand canonical ensemble, for determining phase behavior, critical points, and formation of aggregates.
