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9 results for “metal gas”
Data and Code for "Metal-enriched, sub-kiloparsec gas clumps in the circumgalactic medium of a faint z = 2.5 galaxy"
<p>This repository has code and data used in the paper "Metal-enriched, sub-kiloparsec gas clumps in the circumgalactic medium of a faint z = 2.5 galaxy" (http://arxiv.org/abs/1406.4239). If you find any of the data or code useful for a publication, please consider citing that paper.</p>
Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors
<p>The "Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors" can be used to test machine learning approaches on their capability of interpreting sensor patterns of commercially available MOS gas sensors, i.e., SGP40 (Sensirion AG, Stäfa, Switzerland), to predict multiple different gas concentrations and the relative humidity. Furthermore, the dataset can be used to test the transferability between sensors. <br> The dataset was recorded with the help of a custom-built gas mixing apparatus (GMA). The GMA allows applying well-known gas mixtures to multiple gas sensors. For this experiment, three SGP40 with four sub-sensors each were exposed to 900 different unique gas mixtures (UGMs) consisting of ten different gases. In detail, the dataset consists of eight volatile organic compounds (VOCs) (acetic acid, acetone, ethanol, ethyl acetate, formaldehyde, isopropanol, toluene, and xylene), two background gases (carbon monoxide and hydrogen), and the relative humidity at 20 °C. During exposure, the sensors are operated in a temperature-cycled operation. The temperature cycle consists of alternating high and low-temperature phases. The high-temperature phases are set at 400 °C and have a duration of 5 seconds, while the low-temperature steps increase in 25 °C steps from 100 °C-375 °C, where each step has a duration of 7 seconds. The only exception is sub-sensor 4, where the temperature is only alternated between 250 °C and 300 °C. The total duration of the temperature cycle is 144 seconds, and during this time, the logarithmic sensor resistance is read out at 10 Hz. Each gas mixture was recorded for ten temperature cycles to ensure that stable gas mixtures were applied to the sensor. Only stable samples 6 (not always),7,8, and 9 were used for further evaluation. The 900 UGMs can be separated into three parts, and for each part, the mixtures were generated based on Latin hypercube sampling and the ranges specified in Table 1.</p> <table> <caption>Tabel 1: Uniform distributed ranges for all gasses within the gas mixtures</caption> <tbody> <tr> <td> </td> <td>UGM 1-200</td> <td>UGM 201-500</td> <td>UGM501-900</td> </tr> <tr> <td>Carbon monoxide</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> </tr> <tr> <td>Hydrogen</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> </tr> <tr> <td>Relative humidity</td> <td>25 - 80 %</td> <td>25 - 80 %</td> <td>25 - 80 %</td> </tr> <tr> <td>Acetic acid</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Acetone</td> <td>3 - 50 ppb</td> <td>3 - 150 ppb</td> <td>3 - 500 ppb</td> </tr> <tr> <td>Ethanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Ethyl acetate</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Formaldehyde</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 300 ppb</td> </tr> <tr> <td>Isopropanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Toluene</td> <td>1 - 75 ppb</td> <td>1 - 75 ppb</td> <td>1 - 250 ppb</td> </tr> <tr> <td>Xylene</td> <td>2 - 150 ppb</td> <td>2 - 150 ppb</td> <td>2 - 500 ppb</td> </tr> </tbody> </table> <p>To be able to use this dataset for transfer learning, the dataset consists of three different SPG40; two are from the same batch (sensor A and sensor B), and sensor C is from a different batch. <br> The dataset consists of the sensors' data and a target for evaluation. The data is already split into training and Validation and is stored in cells for each sensor: <br> sensorA_train<br> sensorA_test<br> sensorB_train<br> sensorB_test<br> sensorC_train<br> sensorC_test</p> <p> Each sensor cell contains four arrays, one for each sub-sensor within one SGP40. The number of rows in the arrays represents the number of observations (693 for test and 2401 for training), and the number of columns represents the number of samples per observation (1440).<br> The targets, i.e., the concentrations of each gas, are given in the target_train and targe_test structs. Since the data were recorded simultaneously, those structs can be used as targets for all sensors. The ten different gases, relative humidity, and TVOCsens are actual targets, while the range parameter represents the specific unique gas mixture ID.</p> <p>Although this is a mat file, it can be opened as an hdf5 file.</p>
Dataset for "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"
<p>Dataset covering the DFT simulations performed for the journal article "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"</p>
The Data For Inside-Out versus Upside-Down: The Origin and Evolution of Metallicity Radial Gradients in FIRE Simulations of Milky Way-mass Galaxies and the Essential Role of Gas Mixing
<p>The files titled Graf et al. 2024b store the x-axis and y-axis values for each line in each figure. The files which end in .py are the scripts which produced the data in the figures.</p> <p>This data abides by CC-BY.</p>
Preparation of Phosphorus doping modification of porous boron nitride and its adsorption characteristics for heavy metals in flue gas
Boron nitride, known as "white graphene", have attracted extensive attention to the fields of adsorption, catalysis and hydrogen storage due to their excellent chemical properties. In this paper, the phosphorus doped boron nitride material (P-BN), was successfully prepared by red phosphorus as a dopant for the preparation of porous boron nitride precursors, the phosphorus content in P-BN was adjusted by the addition rate of phosphorus. The tendency of specific surface area of P-BN increased firstly and then decreased with the increasing of phosphorus addition rate, and the maximum specific surface area was 837.08m2/g when the phosphorus addition rate was 0.50. The P-BN, prepared by the experimental, used as an adsorbent had been proved its adsorption capacity for heavy metal flue gas. In particular, P-BN had a stronger adsorption selectivity for Zinc compared with other heavy metals, and the adsorption capacity of P-BN for Zinc reached 5~38 times higher than that of other heavy metals. However, the maximum adsorption capacities of P-BN for Zinc and Copper in the single heavy metal atmosphere were 69.45mg/g and 53.80mg/g, respectively.
Hierarchically Porous Reduced Graphene Oxide Coated with Metal-Organic Framework HKUST-1 for Enhanced Hydrogen Gas Affinity
<p>Metal organic frameworks (MOFs) are crystalline porous materials with interconnected pores and have been actively explored for various gas storage, separation and conversion applications due to their structural tunability. While the micropores (<2 nm) in MOFs are essential for increased gas affinity, these small pores significantly decrease the mass transport kinetics. One way to address this challenge is to develop hierarchically porous MOFs with interconnected micro-, meso- and macropores. Whereas these MOFs can be formed by using soft/hard templates or by creating pores through post-modification, it can also be achieved by growing MOFs on structural templates such as porous carbons i.e., reduced graphene oxide. The latter strategy can enable the introduction of hierarchical porosity, while creating a synergistic effect to simultaneously improve both mechanical property and gas affinity by creating pores at the interface. In this direction, we demonstrated that the coating of HKUST-1 onto a porous reduced graphene oxide (HRGO) led to the formation of a hierarchically porous structure, namely, HKUST-1@HRGO with increased affinity towards H2 gas. While the isosteric heats of adsorption (<em>Q</em><sub>st</sub>) values for H2 were found to be 7.7, 6.9 and 6.7 kJ mol<sup>-1</sup> for HRGO, HKUST-1 and the physical mixture of HKUST-1 and HRGO, respectively, at zero coverage, that of HKUST-1@HRGO composite revealed a significant increase up to 9.26 kJ mol<sup>-1 </sup>,<sup> </sup>thus clearly demonstrating not only the synergetic effect between HKUST-1 and the reduced graphene oxide and also the critical role interfacial pores as high affinity binding sites.</p> <p> </p>
Digital Design and Discovery of Biological Metal-Organic Frameworks for Gas Signaling
<p>This repository contains the structures, features and compositions of Bio-hMOFs database.</p> <ol> <li>Fragments and Composition: Contains the building block fragments used to generate the Bio-hMOFs.</li> <li>Structures-CIFs: Contains the structures of Bio-hMOFs</li> <li>Geometric and RACs: Contains the geomtric and RACs features of Bio-hMOFs</li> <li>Adsorption Capacity: Contains the adsorption uptake of NO and CO adsorption simulated under 298K under 1 bar and 10 bar.</li> <li>Mechanical Properties; Computed mechanical properties</li> </ol>
Dataset for 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations'
<p> </p> <p>This dataset is associated with the research entitled: 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. It contains raw data from six low-cost sensor loggers and one anemometer, collected during an experiment consisting of a series of controlled releases conducted in October 2019 at the TADI (TotalEnergies Anomaly Detection Initiative) platform.</p> <p><strong>Dataset Structure:</strong></p> <p>- `time`: Timestamp, marking the exact time the data was collected.<br> - `CH4`: Methane concentration measured by the reference instrument in parts per million (ppm).<br> - `2611C`: Voltage variation measured by the Figaro TGS 2611C-00 MOS sensor in volts (V).<br> - `2600`: Voltage variation measured by the Figaro TGS 2600 MOS sensor in volts (V).<br> - `2611E`: Voltage variation measured by the Figaro TGS 2611E-00 MOS sensor in volts (V).<br> - `RH_DHT22`: Relative humidity measured by the DHT22 sensor in percentage (%).<br> - `RH_SHT75`: Relative humidity measured by the SHT75 sensor in percentage (%).<br> - `T_DHT22`: Air temperature measured by the DHT22 sensor in degrees Celsius (°C).<br> - `T_SHT75`: Air temperature measured by the SHT75 sensor in degrees Celsius (°C).<br> - `T_BMP180`: Air temperature measured by the BMP180 sensor in degrees Celsius (°C).<br> - `T_BMP280`: Air temperature measured by the BMP280 sensor in degrees Celsius (°C).<br> - `P_BMP180`: Atmospheric pressure measured by the BMP180 sensor in pascals (Pa).<br> - `P_BMP280`: Atmospheric pressure measured by the BMP280 sensor in pascals (Pa).<br> - `Release`: Number of the controlled release.</p> <p><strong>Acknowledgment:</strong></p> <p>When using this dataset, please reference the original research paper titled ‘Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’.</p> <p><strong>Contact Information:</strong></p> <p>Olivier Laurent (olivier.laurent@lsce.ipsl.fr)</p> <p> </p>
Preparation of Phosphorus doping modification of porous boron nitride and its adsorption characteristics for heavy metals in flue gas
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