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13 results for “organic chemistry”
Mass and Chemistry of Organic Horizons and Surface Mineral Soils on Watershed 6 at the Hubbard Brook Experimental Forest, 1976 - present
The forest floor of Watershed 6 was first sampled in 1969-70. These data include forest floor thickness, soil mass, organic matter content, and major-element composition for samples collected since 1976. Watershed 6 has been resampled at intervals varying from one to ten years. Sampling at five to ten year intervals is expected to continue. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Mass and Chemistry of Organic Horizons and Surface Mineral Soils on Watershed 1 at the Hubbard Brook Experimental Forest 1996-present
This data set includes chemistry of O-horizons ("forest floor") and the 0-10 cm mineral soil layer in Watershed 1 at Hubbard Book. Calcium in the form of wollastonite (CaSiO3) was added to Watershed 1 in October 1999. The application rate was 1028 kg Ca per ha, and the application was relatively uniform across the watershed. Pre-treatment forest floor surveys were completed in 1996 and 1998. The first post-treatment forest floor survey was completed in 2000. This data set includes mass and thickness data for the sampled layers. Chemical data include concentrations and pools of organic matter, C, N, Ca, Mg, K, P, Mn, Fe, Al, Cu, Pb, and Zn. Soil pH and exchangeable Al, Ca, Mg, K, and H are also included. Sampling is intended to continue at 4 or 5 year intervals. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Data accessibility in the chemical sciences: an analysis of recent practice in organic chemistry journals
<div> <p>Data is the analysis of the data outputs of 240 randomly selected research papers from 12 top-ranked journals published in early 2023. We investigate author compliance with recommended (but not compulsory) data policies, whether there is evidence to suggest that authors apply FAIR data guidance in their data publishing, and if the existence of specific recommendations for publishing NMR data by some journals encourages compliance. Files in the data package have been provided in both human and machine-readable forms. The main dataset is available in the Excel file Data worksheet.XLSX, the contents of which can also be found in Main_dataset.CSV, Data_types.CSV, and Article_selection.CSV with explanations of the variable coding used in the studies in Variable_names.CSV, Codes.CSV, and FAIR_variable_coding.CSV. The R code used for the article selection can be found in Article_selection.R. Data about article types from the journals that contain original research data is in Article_types.CSV. Data collected for analysis in our sister paper[4] can be found in Extended_Adherence.CSV, Extended_Crystallography.CSV, Extended_DAS.CSV, Extended_File_Types.CSV, and Extended_Submission_Process.CSV. A full list of files in the data package and a short description for each is given in README.TXT.</p> </div>
Forest Floor Mass, Organic Matter and Chemistry in Watershed 5 at the Hubbard Brook Experimental Forest, 1982 Pre-Harvest Collection
Watershed 5 was surveyed in 1982 and clearcut in 1983. A pre-cut forest floor survey was done in 1982. This data set includes organic matter mass, thickness and trace metals for samples collected in the summer of 1982 as two separate collections, one in July and one in August. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Water and soil chemistry and molecular composition data of dissolved organic matter from soils in the central Amazon rainforest, 2019-2021
This dataset was created based on sampling campaigns in the central Amazon rainforest. It covers four field sites in vicinity of the Amazon Tall Tower Oberservatory project (ATTO). The field sites are comprised of pristine, old-growth rainforest with distinct forest types: terra firme forest (Plateau and Terrace sites) and white-sand forest (Campina and Campinarana sites). Freely percolating soil porewater was sampled repeatedly in soil profiles from 0 to 30 cm in the wet seasons (October to May) of 2019, 2020 and 2021. Solid soil samples were taken in vicinity to the plots in October 2017. The datasets include chemical and texture (particle size fractions) and chemical (metals, cation exchange capacities and pH) data from soil samples. Soil porewater samples were measured for pH, electrical conductivity and dissolved organic carbon (DOC) concentrations. There are some missing values for these data, particularly for DOC concentrations. Soil porewater samples were used for solid-phase extraction of dissolved organic matter (DOM). The molecular composition was measured by ultra-high resolution mass spectrometry (Orbitrap Elite MS). The detected masses were used to assign molecular formulas. The data include the assigned molecular formulas and sum-normalized intensites of all samples. This data is associated with a Ph.D. dissertation chapter (Lange, 2025) and a journal article (Lange et al., accepted).
Raw data files for "3D vs. turbostratic: controlling metal-organic framework dimensionality via N-heterocyclic carbene chemistry" manuscript
<p>Raw data files for a manuscript "3D vs. turbostratic: controlling metal-organic framework dimensionality via N-heterocyclic carbene chemistry" published in Chemical Science. <a href="https://doi.org/10.1039/D2SC01041K">https://doi.org/10.1039/D2SC01041K</a></p> <p>The files are organized by manuscript figure names and are in a simple text or CSV format. The headers contain the necessary information such as column designations, units, etc.</p> <p> </p> <p> </p> <p> </p>
Particle-Phase Uptake and Chemistry of Highly Oxygenated Organic Molecules (HOMs) from α-Pinene OH Oxidation
<p>Secondary organic aerosol (SOA) forms a major part of the tropospheric submicron particle mass. Still, the exact formation mechanisms of SOA have remained elusive. It is now admitted that highly oxygenated organic molecules (HOMs) can contribute to a large fraction of SOA formation. In this study, we performed a set of chamber experiments to investigate the SOA formation, and the HOMs uptake and processing directly formed by OH-radical initiated oxidation of α-pinene under two different aerosol seed conditions. Numerous HOM compounds were identified using advanced online and offline analytical techniques and grouped into four classes according to their different uptake behaviors. For the first time, individual HOMs uptake coefficients ranging from 1.1×10<sup>-2</sup> to 1.5×10<sup>-1</sup> were experimentally determined and analyzed using a resistance model which considers uptake limitations by individual gas- and/or particle-phase processes. This study demonstrates that the uptake coefficients of HOMs strongly depend on their molar mass and their respective O/C ratio. Results show that aerosol seed composition and phase state affect the initial uptake of HOMs. Furthermore, the study demonstrates that the acidity and/or different seed phase-state can significantly enhance the subsequent uptake through occurring acidity-driven reactions reflected in a reactive behavior, particularly under (NH<sub>4</sub>)HSO<sub>4</sub> seed conditions, promoting up to 3 times a higher SOA mass formation including the formation of highly-oxidized organosulfates (HOOS). Overall, the present study implies that HOMs and their subsequent chemical processing can play an important role in both the early growth of newly formed particles and SOA formation when particle acidity is high.</p>
Stomach as the target organ of Rickettsia heilongjiangensis infected C57BL/6 mice identified by click chemistry
<p>Code used for analyses and generate figures in the manuscript *Stomach as the target organ of Rickettsia heilongjiangensis infected C57BL/6 mice identified by click chemistry*.</p> <p>Input and scripts are included in corresponding files.</p> <p> </p>
Dataset: Non-Uniform Chiralization of Metal-Organic Frameworks Using Imine Chemistry
Open the record for dataset details and reuse information.
Data sets used in "Neural network emulation of the formation of organic aerosols based on the explicit GECKO-A chemistry model"
<p>The training, validation, and testing data sets for toluene, dodecane, and alpha-pinene models described in the manuscript. A link to the manuscript will be added here when it becomes available. All trajectories in the data sets were generated using GECKO-A. The source code for using the data sets can be found at https://github.com/NCAR/gecko-ml </p>
Organic aerosol source apportionment in Zurich using extractive electrospray ionization time-of-flight mass spectrometry (EESI-TOF): Part I, biogenic influences and day/night chemistry in summer
<p>Ambient measurement campaign took place during summer 2016 in Zurich. The sources of organic aerosol were disclosed. The novel extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) could provide direct chemical evidence linking ambient SOA to its precursor emissions including a strong influence of biogenic emissions. Additionally provided some insight into the day/night reaction environment and high-detailed chemical composition.</p>
Immersion enthalpies of nanomaterials (metals, metal oxides, organic chemistry structures) in water and octanol through molecular dynamics simulations with GROMACS and LAMMPS software
<p>Molecular dynamics simulations (free energy calculations) of water interface properties of spherical and Wulff structures nanoparticles (metals, metal oxides and carbon structures) with GROMACS and LAMMPS software.</p>
Toward emulating an explicit organic chemistry mechanism with a random forest model: dataset and training code
<p>This repository contains the dataset created with the GECKO-A model and the code (training_gecko_rf_final.py) used to train and test random forests for predicting secondary organic aerosol formation.</p> <p>For each simulation, results are distributed in two separate files identified as such:</p> <ul> <li><precursor>_library_<id>_predictors.csv and <precursor>_library_<id>_outcomes.csv.</li> <li><precursor> is either ARO1 (toluene) or dodecane_4gen (dodecane).</li> <li><id> is a unique simulation identifier.</li> <li>the *predictors.csv files contain the state of the predictors for each timestep at the beginning of the chemical solver integration step.</li> <li>the *outcomes.csv files contain the state of the outcomes at the end of the chemical solver integration step.</li> </ul> <p>The TRAINING_* directories contain training simulations. TRAINING_ALL contains all the training data, used for the default random forest configuration. TRAINING_*NOX contain sorted training data matching LOW, MID and HIGH NOx initial regimes (see associated article) to train the specialized random forests.</p> <p>Similarly, the VALIDATION_* directories contain validation simulations, used to test the random forests after training.</p> <p>The TESTING* directories contain the results of testing the random forest for comparison with the VALIDATION simulations.</p>
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