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73 results for “Nitriding”
Etching of Nitrides-NbN Heterostructures
<p>Niobium Nitride (NbN) is metallic at room temperature. NbN / III-N heterostructures are of interest for<br> new devices applications such as the Metal Base Transistor (MBT). Niobium Nitride etching in fluorine plasma is documented by Reactive Ion Etching (RIE). In this document we present the etching of a III-Nitride NbN heterostructure with fluorine<br> chemistry Inductively Coupled Plasma (ICP).</p>
Growth of hexagonal boron nitride from molten nickel solutions: a reactive molecular dynamics study
<p>Authors: Amin Ahmadisharaf and Jeffrey Comer</p> <p><br>Publication: Amin Ahmadisharaf, Bin Liu, James H. Edgar, and Jeffrey Comer (2025) Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study. ACS Applied Materials & Interfaces. <a href="https://doi.org/10.1021/acsami.4c16991">doi.org/10.1021/acsami.4c16991</a></p> <p>Funding: Department of Energy Office of Science, grant DE-SC0021264, <a>https://pamspublic.science.energy.gov/WebPAMSExternal/Interface/Common/ViewPublicAbstract.aspx?rv=5e6ffff5-0daa-47e8-a3d0-20f594b7bfb8&rtc=24&PRoleId=10</a></p> <p>**************************************</p> <p>This data set for the manuscript entitled "Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software LAMMPS, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text. They include force field parameter files (in ReaxFF format), LAMMPS configuration files (*.in), ReaxFF control files (*.control), LAMMPS log files (*.log), and LAMMPS output including restart files (in binary LAMMPS format) and trajectories in dcd format (downsampled to 12.5 or 25 ps per frame) and also PDB and PSF files are useful for visualization with VMD. Analysis is performed by python and shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>The species analysis is performed by the VMD Tcl script "Figure3/analysis/count_hBN_species_NNB_BN.tcl" using the parameters given in "Figure3/analysis/doCount.sh".</p> <p>The directory contents are as follows.</p> <p>--------------------------------------------------------------<br>Figure-1: Parallel tempering simulations of the boron-nickel system and calculation of the boron concentration along the z-dimension of the nickel slab.</p> <p>The analysis of the boron concentration profile is performed by the VMD Tcl scripts calcRatioZRef.tcl and calcConcZRef.tcl using the parameters given in "Figure1/analysis/step3_conc_profile.sh". Also, the reorganization of the parallel tempering trajectories into frames at a single temperature is performed by the VMD Tcl script extractReplicaFrames.tcl based on "Figure1/analysis/step1_sort_frames.sh".</p> <p><br>--------------------------------------------------------------<br>Figure-2: Simulation of hBN sheet growth at 1750 K and calculation of largest cluster.</p> <p><br>--------------------------------------------------------------<br>Figure-3: Simulations of different boron-to-nickel ratios at varying nitrogen pressures at 1750 K and the calculation of the largest hBN cluster formed under different scenarios. The suffixes "liu", "long_liu", and "low_liu" correspond to pressures of 100.0, 50.0, and 25.0 atm respectively.</p> <p>The analysis of hBN clusters is performed by the VMD Tcl script "Figure3/analysis/" using the parameters given in "Figure3/analysis/doCount.sh". The related simulations files and outputs for panel A in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-4: Recognition and counting the different boron-nitrogen motifs in the simulation was performed in Figure 2.</p> <p>The related simulations files and outputs for panel B and C in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-5: Simulation of the temperature effect on hBN growth, and recognition and counting of the different boron-nitrogen motifs at 1750, 1800, 1900, 2000, 2200, and 2700 K.. </p> <p><br>--------------------------------------------------------------<br>Figure-6: Recognition of existing motifs for nitrogen atoms in the growth path of hBN and calculation of the probabilities of transitions between different motifs across all nitrogen atoms.</p> <p>The related simulations files and outputs for all panels in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-7: Comparing the ReaxFF and ab initio simulations of small B-N motifs(B--N--B and B--N) in a nickel slab and calculation of bond lengths and angle values.</p> <p><br>--------------------------------------------------------------<br>Figure-8: Diffusion simulations of four different systems at 1800 K: nickel with a single B atom, nickel with a single N atom, nickel with a free B-N-B molecule, and nickel with a small hBN sheet and Mean Squared Displacement (MSD) values were calculated and compared to assess the surface mobility of the different particles.</p> <p> </p>
Minimum dataset for "Liquid-activated quantum emission from pristine hexagonal boron nitride for nanofluidic sensing"
<p>Frames and (linked) localization table used to produce Fig. 2 of the manuscript https://www.nature.com/articles/s41563-023-01658-2.</p> <p>Details are given in 'README.txt'.</p> <p>The rest of the data is provided with the paper at the publisher website.</p>
Non-destructive thickness mapping of wafer-scale hexagonal boron nitride down to a monolayer
<p>Raw data associated with the publication "Non-Destructive Thickness Mapping of Wafer-Scale Hexagonal Boron Nitride<br> Down to a Monolayer" published in ACS Applied Materials & Interfaces under the DOI: 10.1021/acsami.8b08609</p>
Data for article: Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation
<p>This is the data presented in the article titled 'Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation', published in the Journal of the American Chemical Society. DOI:10.1021/jacs.7b01547</p> <p>http://pubs.acs.org/doi/abs/10.1021/jacs.7b01547</p> <p> </p>
Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates
<p>The data of paper "Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates"<br>1. The BAs file include lammps input file of EMD, BAs model file and NNP model. One can use follw command to perform simulation.<br> lmp -in in.thermal<br> Besides, the ShengBTE results from 3-phonon and 4-phono are included in ShengBTE file.<br>2. The GaN file include lammps input file of EMD, GaN model file and NNP model. One can use follw command to perform simulation.<br> lmp -in in.thermal<br> Besides, the ShengBTE results from 4-phonon are included in ShengBTE file.<br>3. The BAs-GaN file include lammps input file of NEMD, heterostructure model file and NNP model. One can use follw command to perform simulation.<br> lmp -in in.thermal<br>4. The active learning framework that includes the initial file, running file, machine configuration file and other input files.It is worht to note that the machine configuration file may not normally work, duo to different cluster environment. </p> <p>5. The FEM file contains python scripts for building finite element models.</p>
Supporting data for "Benchmarking the integration of hexagonal boron nitride crystals and thin films into graphene-based van der Waals heterostructures"
<p>Dataset for the publication "Benchmarking the integration of hexagonal boron nitride crystals and thin films into graphene-based van der Waals heterostructures"</p>
Thickness dependence of the mechanical properties of piezoelectric high-Q_m nanomechanical resonators made from aluminium nitride
<p>Data used for figures in "Thickness dependence of the mechanical properties of piezoelectric high-Q_m nanomechanical resonators made from aluminium nitride" Anastasiia Ciers et.al <a href="https://doi.org/10.1088/2633-4356/ad9b64">Materials for Quantum Technology <strong>4</strong>, 046301 (2024)</a>; <a href="https://doi.org/10.48550/arXiv.2410.03944">arXiv:2410.03944 [cond-mat.mes-hall] (2024)</a></p>
Data set for the journal article Structural Analysis of Metal Coordination Sites in Single-Atom Catalysts Based on Carbon Nitrides
<p>The data is organized according to the figure in the manuscript. </p>
(DATASET) Functionalized boron–nitride nanotubes: First-principles calculations.
<p>Boron nitride nanotubes (BNNTs), a type of nanomaterial that was first made in 1995, have gained attention in the last two and a half decades, having been characterized as promising inorganic nanoparticles for the application and development of devices in nanomedicine (such as drug delivery systems), and chemical sensors, among other applications. With the use of density functional theory (DFT) calculations, we study the effect of functionalizing the surface of a BNNT with hydroxyl (-OH) and carboxyl (-COOH) organic groups at the N and B sites. The results indicate that the -OH radical remains adsorbed in the B site whereas it is detached from the surface when added to the N site. Very differently, the -COOH remains attached for both adsorption sites. Both functionalizations induced a magnetic moment, produced spin-polarized electronic bands, created flat impurity-like bands, decreased the band gaps, and modified the electron density of the nanotubes. All these changes can be used to control and modify the interaction of BNNTs with other molecules of interest like drugs, heavy metals, and greenhouse gasses, among other substances.</p>
Iron Nitride Nanoparticles for Rapid Dechlorination of Mixed Chlorinated Ethene Contamination: DFT Calculations
<p>This dataset contains input and output files of density functional theory calculations on which the computational part of the peer-reviewed article "Iron nitride nanoparticles for rapid dechlorination of mixed chlorinated ethene contamination" by M. Brumovský et al., doi 10.1016/j.jhazmat.2022.129988, is based. Please cite this article when using the dataset.</p> <p>The dataset includes:<br> * Structures and energies of adsorbed cis-DCE and PCE molecules on the Fe(110), Fe4N(001), and FeS(001) surfaces in the folder "Adsorption_calculations", including structures and energies of adsorbed TCE on the FeS(001) surface<br> * cis-DCE and PCE homolytic bond dissociation energies calculated using VASP and TURBOMOLE in the folder "BDE_calculations"<br> * Relaxed structures of dechlorinated intermediates on the Fe(110), Fe4N(001), and FeS(001) surfaces in the folder "Intermediates"<br> * Transition state calculations of chloroethene dechlorination reactions, including frequency calculations of transitions states, in the folder "NEB_calculations"<br> * Energies of reactants, products, and transitions states calculated with the inclusion of the solvent effect in the folder "VASPsol_calculations"</p> <p>The final geometries and energies calculated using VASP are reported in the CONTCAR and OUTCAR files, respectively. Note that the POTCAR files are not allowed to be made publicly accessible. However, their description is given in the OUTCAR files. Please consult VASP/TURBOMOLE manual for more information regarding input and output files.</p> <p>The fully relaxed unit cells of Fe, Fe4N, and FeS, as well as adsorption calculations of TCE, and transition state calculations of TCE chemisorption and its first dechlorination step on the Fe4N(001) surface are accessible in a previously published dataset, doi 10.5281/zenodo.6338412.</p> <p>Funding: This work was supported by the Austrian Science Fund (FWF) project M 2892-N. The Vienna Scientific Cluster (Project No. 70544) is gratefully acknowledged for providing computational resources.</p> <p>Terms of use: These data are provided "as is", without any warranty. The data are provided under the Creative Commons Attribution 4.0 International license.</p>
Cathodoluminescence monitoring of quantum emitter activation in hexagonal boron nitride
<p>Data from the article of S. Roux, C. Fournier, K. Watanabe, T. Taniguchi, J.-P. Hermier, J. Barjon, A. Delteil,<br> "Cathodoluminescence monitoring of quantum emitter activation in hexagonal boron nitride", <a href="https://doi.org/10.1063/5.0126357">Appl. Phys. Lett. <strong>121</strong>, 184002 (2022)</a></p> <p>[<a href="https://arxiv.org/abs/2210.05028">arXiv</a>:2210.05028]</p>
Data and code for article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides"
<p>This dataset contains the data presented in the figures of the article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides" (doi:10.1038/s41566-018-0144-1).</p> <p>The raw data in figures (curved plots) is packaged as an independent OriginLab project file (.opj). </p> <p>The layout of the design of the silicon nitride nano-photonic waveguide is presented. Fabrication process card (shown as a diagram) is provided as well.</p> <p>The source code for simulations presented in the article is also presented.</p>
Data for the publication "A polarization-induced 2D hole gas in undoped gallium nitride quantum wells"
<p>This upload contains the data presented in the manuscript "A polarization-induced 2D hole gas in undoped gallium nitride quantum wells", which reports the observation of a high conductivity 2DHG in an epitaxially grown GaN/AlN heterostructure. This upload contains 4 files :</p> <ul> <li>1_tabulated_raw_data.xlsx : Both the raw experimental data along with the simulation results used in the Figures 1A, 2B, 3, 4, S2 in the paper. The data is encapsulated in a Excel file, with separate sheets used for each figure.</li> <li>2_nextnano_GaN_AlN_band_diagram.in : Input file for nextnano software (<a href="https://www.nextnano.de/">https://www.nextnano.de/</a>) for generating the band diagrams in the paper.</li> <li>3_README_XRD_sim.txt : Readme file with instructions for the XRD simulation for Fig 2</li> <li>4_XRD_sim_run.py : Python script for running the XRD simulation</li> </ul>
Data and code for figures in "Thermo-refractive noise in silicon nitride microresonators"
<p>Data and script used to produce the figures in "Thermo-refractive noise in silicon nitride microresonators".</p><p>Readout of some data files requires @MyTrace function from <a href="https://github.com/engelsen/Instrument-control">https://github.com/engelsen/Instrument-control</a>.</p><p>The Matlab live script is tested with Matlab_R2018a. The COMSOL file is tested with COMSOL Multiphysics 5.3a.</p>
Magnetic-Free Silicon Nitride Integrated Optical Isolator (Original Data)
<p>This is the raw dataset for the paper titled "Magnetic-Free Silicon Nitride Integrated Optical Isolator", which includes the original data and theoretical code. </p>
Open data source for "Optically reconfigurable quasi-phase-matching in silicon nitride microresonators"
<p>The folder includes includes the raw data as well as codes that were used for generation of all Figures in the paper "Optically reconfigurable quasi-phase-matching in silicon nitride microresonators".</p>
Nitride protonation and NH3 binding versus N–H bond cleavage in uranium nitrides
<p>The conversion of metal nitrides to NH3 is an essential step in dinitrogen fixation, but there is limited knowledge of the reactivity of nitrides with protons (H+). Herein, we report comparative studies for the reactions of H+ and NH3 with uranium nitrides, containing different types of ancillary ligands. We show that the differences in ancillary ligands, leads to dramatically different reactivity. The nitride group, in nitride-bridged cationic and anionic diuranium(IV) complexes supported by –N(SiMe3)2 ligands, is resistant toward protonation by weak acids, while stronger acids result in ligand loss by protonolysis. Moreover, the basic –N(SiMe3)2 ligands promote the N–H heterolytic bond cleavage of NH3, yielding a “naked” diuranium complex containing three bridging ligands, a nitride (N3) and two NH2 ligands. Conversely, in the nitride-bridged diuranium(IV) complex supported by –OSi(OtBu)3 ligands, the nitride group is easily protonated to afford NH3, which binds the U(IV) ion strongly, resulting in a mononuclear U–NH3 complex, where NH3 can be displaced by addition of strong acids. Furthermore, the U– OSi(OtBu)3 bonds were found to be stable, even in the presence of stronger acids, such as NH4BPh4, therefore indicating that –OSi(OtBu)3 supporting ligands are well suited to be used when acidic conditions are required, such as in the H+/e mediated catalytic conversion of N2 to NH3.</p>
Implementing electronic signatures of graphene and hexagonal boron nitride in twisted bilayer molybdenum disulfide
<p><strong>Abstract</strong></p> <p>Angeli and MacDonald reported a superlattice-imposed Dirac band in twisted bilayer molybdenum disulphide (tBL MoS2) for small twist angles towards the R_h^M (parallel) stacking. Using a hierarchical set of theoretical methods, we show that the superlattices differ for twist angles with respect to metastable R_h^M (0°) and lowest-energy H_h^h (60°) configurations. When approaching R_h^M stacking, identical domains with opposite spatial orientation emerge. They form a honeycomb superlattice, yielding Dirac bands and a lateral spin texture distribution with opposite-spin-occupied K and K’ valleys. Small twist angles towards the H_h^h configuration (60°) generate H_h^h and H_h^X stacking domains of different relative energies and, hence, different spatial extensions. This imposes a symmetry break in the moiré cell, which opens a gap between the two top-valence bands, which become flat already for relatively small moiré cells. The superlattices impose electronic superstructures resembling graphene and hexagonal boron nitride into trivial semiconductor MoS<sub>2</sub>.</p> <p>The data set published in this repository was used to create the preprint published at <strong>https://doi.org/10.26434/chemrxiv-2023-rx2fz</strong>.</p> <p><strong>Content of repository</strong></p> <ul> <li>"ReaxFF_structure_optimization.zip": contains the inputs and outputs for all structure optimizations for ML, BL, and tBL systems using the Reax force field, performed using LAMMPS.</li> <li>" bilayer_verification_ReaxFF_with_DFT.zip": contains the inputs and outputs for verifying the results of the Reax force field by running DFT geometry optimization and total energy calculations in FHI-aims for the high-symmetry bilayer stackings.</li> <li>"QATK_band_structures_and_eff_mass.zip": contains the inputs and outputs of all calculations done via QuantumATK (QATK), including calculations for ML, BL, and tBL systems on DFT and DFTB level of theory.</li> <li>"TB_fit.zip": contains the Python scripts and input data (DFTB band structure) used to fit the TB Hamiltonians as described in the Methods section and shown in the Supplementary Material.</li> <li>"effective_masses_from_bands.zip": contains the extraction of the effective hole masses from the bands calculated at the DFTB level of theory in QATK.</li> </ul>
(DATA) Möbius boron-nitride nanobelts interacting with heavy metal nanoclusters
<p>Molecular dynamics movies of systems BNNB+Cd1DL, BNNB+Ni1DZ, BNNB+Pb2D, MBNNB+Cd1DL, MBNNB+Ni2D and MBNNB+Pb2D.</p> <p>Spinning structures with the topological information for each system.</p> <p>Vibrational spectrum for optimized complexes.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.