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Hydrogen

Hydrogen is a versatile element with wide-ranging applications in energy, industry, and research.
It serves as a clean fuel source, a key component in chemical processes, and an essential element for advancing hydrogen-based technologies.
Researchers and scientists leveraging hydrogen in their work can optimize their efforts through tools like PubCompare.ai, which enhances reproducibility and accuracy by locating the most effective protocols from literature, pre-prints, and patents using AI-driven comparisons.
By streamlining the research process, PubCompare.ai empowers hydrogen projects to reach new levels of efficiency and innovation.
This concise, informative overveiw highlights hydrogen's diverse applications and the benefits of utilizing advanced tools to support related research and development.

Most cited protocols related to «Hydrogen»

1
AutoDockTools: A Versatile Molecular Docking GUI
With the release of AutoDock3, it became apparent that the tasks of coordinate preparation, experiment design, and analysis required an effective graphical user interface to make AutoDock a widely accessible tool. AutoDockTools was created to fill this need. AutoDockTools facilitates formatting input molecule files, with a set of methods that guide the user through protonation, calculating charges, and specifying rotatable bonds in the ligand and the protein (described below). To simplify the design and preparation of docking experiments, it allows the user to identify the active site and determine visually the volume of space searched in the docking simulation. Other methods assist the user in specifying search parameters and launching docking calculations. Finally, AutoDockTools includes a variety of novel methods for clustering, displaying, and analyzing the results of docking experiments.
AutoDockTools is implemented in the object-oriented programming language Python and is build from reusable software components15 ,16 . The easy-to-use graphical user interface has a gentle learning curve and an effective self-taught tutorial is available online. Reusable software components are used to represent the flexible ligand, the sets of parameters and the docking calculation, enabling a range of uses from a single use to thousands of docking experiments involving many different sets of molecules, facilitating automated high-throughput applications. For example, converting the NCI diversity database of small molecules into AutoDock-formatted ligand files was possible with a short Python script of less than 20 lines by leveraging the existing software components underlying AutoDockTools.
AutoDockTools exists in the context of a rich set of tools for molecular modeling, the Python Molecular Viewer (PMV)16 ,17 (link). PMV is a freely distributed Python-based molecular viewer. It is built with a component-based architecture with the following software components: ViewerFramework, a generic OpenGL-based 3-dimensional viewing component; and MolKit, a hierarchical data representation of molecules. AutoDockTools consists of a set of commands dynamically extending PMV with commands specific to the preparation, launching and analysis of AutoDock calculations. Hence, all PMV commands (such as reading/writing files, calculating and displaying secondary structure, adding or deleting hydrogens, calculating charges and molecular surfaces, and many others) are also naturally available in AutoDockTools. PMV also provides access to the Python-interpreter so that commands or scripts can be called interactively. PMV commands log themselves, producing a session file that can be rerun. In summary, AutoDockTools is an example of a specialization of the generic molecular viewer PMV for the specific application of AutoDock.
Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S, & Olson A.J. (2009). AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of computational chemistry, 30(16), 2785-2791.
Publication 2009
Generic Drugs Hydrogen Learning Curve Ligands Proteins Python
2
Comprehensive Structural Model Analysis
MolProbity is implemented in PHP as a web server located at http://molprobity.biochem.duke.edu. It provides a graphical interface to a collection of Richardson lab programs for validation and structure correction. However, MolProbity is not a mere job-submission form; it is a complex web application that offers multiple modes of use, integrates many different kinds of information and suggests courses of action based on that information.
MolProbity uses a variety of physics- and knowledge-based algorithms to analyze a structure. The primary basis of its enhanced effectiveness is all-atom contact analysis, as implemented in Probe (16 (link)). All-atom contacts are exquisitely sensitive to a wide variety of local misfittings, but they are not yet available in other validation systems. They do require explicit hydrogen atoms, but MolProbity can add and optimize these using Reduce (17 (link)), while at the same time detecting and automatically fixing flipped Asn, Gln and His sidechains. MolProbity also uses carefully filtered, high-accuracy Ramachandran and rotamer distributions to check mainchain and sidechains for conformational outliers. Finally, it reports on some novel geometric indicators of misfitting, such as the Cβ deviation (18 (link)) and the base-phosphate perpendicular distance. The different types of analysis are synthesized into two integrated reports on the structural model: one tabular and one graphical.
Davis I.W., Leaver-Fay A., Chen V.B., Block J.N., Kapral G.J., Wang X., Murray L.W., Arendall WB I.I.I., Snoeyink J., Richardson J.S, & Richardson D.C. (2007). MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Research, 35(Web Server issue), W375-W383.
Publication 2007
Hydrogen Phosphates
3
Improved AutoDock4 Docking Protocol
Docking experiments were performed with AutoDock4 and compared with docking experiments with AutoDock3. For each complex, 50 docking experiments were performed using the Lamarckian genetic algorithm with the default parameters from AutoDock3. A maximum of 25 million energy evaluations was applied for each experiment. The results were clustered using a tolerance of 2.0 Å.
In the HIV cross dockings, ligand flexibility was limited to 10 torsional degrees of freedom, picking torsions that allowed the fewest number of atoms to move (freezing the core of the molecule). Flexible docking was performed allowing three torsions to rotate in residue ARG8, in both the A and B chains. The structural water (water 301) was included in complexes that included this water in the crystallographic structure, and hydrogen atoms were added in geometry that allowed hydrogen bonding to the flaps.
We have also changed the default model for the unbound system in the current version of AutoDock. Our previous method calculated internal energies for an extended form of the molecule, mimicking a conformation that might be expected when fully solvated11 (link). Results from beta testers, however, showed that this protocol has severe limitations when used for virtual screening. In cases where the ligand is sterically crowded, the artificial force field used to drive the ligand into an extended conformation tends to lead to conformations with sub-optimal energy. When the difference is calculated between this unbound conformation and the bound conformation, it leads to artificially favorable predictions of the free energy of binding. In response to this problem, we have returned to the default model of assuming that the unbound conformation of the ligand is the same as the bound conformation. Other options in AutoDock allow the user to use an energy-minimized conformation of the ligand as the unbound model.
Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S, & Olson A.J. (2009). AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of computational chemistry, 30(16), 2785-2791.
Publication 2009
Biological Models Crystallography Hydrogen Immune Tolerance Ligands Reproduction Surgical Flaps
4
Protein Simulation in Crystal Environments
Simulations of the proteins in their crystal environments (Table 1), which were used previously during optimization of the C22/CMAP force field 40 (link), were performed using CHARMM on full unit cells with added waters and counterions to fill the vacuum space. Once the full unit cell was constructed based on the coordinates in the protein databank, a box of water with dimensions that encompassed the full unit cell was overlaid onto the crystal coordinates while preserving crystal waters, ions, and ligands. Water molecules with oxygen within 2.8 – 4.0 Å of any of the crystallographic non-hydrogen atoms were removed, as described below, as well as those occupying space beyond the full unit cell. To neutralize the total charge of each system, sodium or chloride ions were added to the system at random locations at least 3.0 Å from any crystallographic non-hydrogen atom or previously added ions and 0.5 Å from any water oxygen. Final selection of the water molecule deletion distance was performed by initially applying a 2.8 Å criteria to all systems followed by system equilibration and an NPT production run of 5 ns following which the lattice parameters were analyzed. The deletion distances were then increased and the equilibration and 5 ns production NPT simulation were repeated until the final lattice parameters were in satisfactory agreement with experimental data. The final water deletion distances and unit cell parameters from the full 40 ns production simulations are presented in Table S2 of the SI. For the minimization and MD simulations, electrostatic interactions were treated with PME using a real space cutoff of 10 Å. The LJ interactions were included with force switching from 8 Å to 10 Å, while the list of nonbonded atoms was kept for interatomic distances of up to 14 Å and updated heuristically. Each crystal system was first minimized with 100 steps of steepest-decent (SD) with non-water, non-ion crystallographic atoms held fixed followed by 200 steps of SD with harmonic positional restraints of 5 kcal/mol/Å2 on solute non-hydrogen atoms. The minimized system was then subject to an equilibration phase consisting of 100 ps of NVT simulation41 in the presence of harmonic positional restraints followed by 5 ns (100 ps for 135L and 3ICB) of fully relaxed NVT simulation with a time step of 2 fs. During the simulations all covalent bonds involving hydrogens were constrained using SHAKE42 . Production phase simulations were conducted for 40 ns in the isothermal and isobaric NPT ensemble43 . The only symmetry enforced was translational (i.e. periodic boundaries). Reference temperatures were set to match the crystallographic conditions (Table S2) and maintained by the Nosé-Hoover thermostat with a thermal piston mass of 1,000 kcal ps2/mol while a pressure mass of 600 amu was used with the Langevin piston. The first 5 ns of the production simulations were considered as equilibration and therefore discarded from analysis, which was performed on coordinate sets saved every 5 ps. The boundaries for α helices and β strands were obtained from a consensus of author annotations and structural assignments calculated by DSSP44 (link) and STRIDE45 (link) from the crystal structures.
Best R.B., Zhu X., Shim J., Lopes P.E., Mittal J., Feig M, & MacKerell AD J.r. (2012). Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. Journal of chemical theory and computation, 8(9), 3257-3273.
Publication 2012
ARID1A protein, human Cells Chlorides Crystallography Deletion Mutation Deuterium Electrostatics Helix (Snails) Hydrogen Hydrogen-4 Ligands Oxygen Pressure Protein Biosynthesis Proteins Sodium STEEP1 protein, human Tritium Vacuum
5
Efficient Scattering Curve Calculation
The programs CRYSOL (Svergun et al., 1995 ▶ ) for X-rays and CRYSON (Svergun et al., 1998 ▶ ) for neutrons evaluate the solution scattering from macromolecules with known atomic structure and fit a predicted curve to experimental scattering data by minimizing the discrepancy (Feigin & Svergun, 1987 ▶ ) where c is a scaling factor, N is the number of points and σ denotes the experimental errors. In the fitting process, the excess scattering density of the hydration shell, the average atomic group radius and the related total excluded volume can be adjusted. With the recent progress in high-resolution structure determination and advances in structure prediction and docking algorithms, tremendous numbers of structural models are becoming available. The screening of multiple models against experimental scattering data (typically SAXS) is often performed to select the best configuration in solution. The performance of these programs is crucial when applied to large numbers of structures and to deal with the increased number of angular data points in scattering profiles resulting from the improved resolution of detectors employed at the modern SAXS beamlines (e.g. PILATUS from DECTRIS; http://pilatus.web.psi.ch/pilatus.htm). In order to speed up CRYSOL calculations, experimental scattering intensities and associated errors are automatically remapped into a sparser grid for the search of the best fitting parameters. Depending on the number of experimental points, the regridding operation speeds up the fitting procedure by up to a factor of five. The final fits are recalculated for the optimum parameters for the original experimental data points.
Practice shows that in some cases (e.g. as a result of buffer mismatch) the higher-angle positions of the scattering data may contain systematic deviations, which can be accounted for by subtraction/addition of a constant term to the experimental data. An option of background constant adjustment has been added to CRYSOL to allow for the correction of such over- or under-subtracted buffer signal. A linear least-squares minimization with boundaries (Lawson & Hanson, 1995 ▶ ) is used to find the scaling coefficient and the background constant value when fitting a theoretical curve to experimental data.
Typically, CRYSOL and CRYSON skip all H atoms present in the PDB files and instead make an assignment of the number of bound H atoms for each atomic group based on the chemical compound library (ftp://ftp.wwpdb.org/pub/pdb/data/monomers/components.cif) in order to compute the scattering. If a full-atom model containing all H (or deuterium) atoms is available, the user has the option to take all the atoms ‘as is’, which can be specified in both interactive and batch modes [in the latter case the input parameters are specified on the command line (Konarev et al., 2006 ▶ )]. Since the remediation of the PDB archive in 2007–2008 (http://www.rcsb.org/pdb/static.do?p=general_information/news_publications/index.html) the nomenclature of many heteroatoms had been changed, and the assignments of bound hydrogen to an atom became ambiguous, such that the hydrogen assignment may be incorrect in some cases. To resolve this problem, both the new (after version 3.1) and the old (before version 3.0) PDB formats are now supported. By default, the new format is assumed, but the user can also enforce the old format by using the ‘/old’ key in the command line input.
Petoukhov M.V., Franke D., Shkumatov A.V., Tria G., Kikhney A.G., Gajda M., Gorba C., Mertens H.D., Konarev P.V, & Svergun D.I. (2012). New developments in the ATSAS program package for small-angle scattering data analysis. Journal of Applied Crystallography, 45(Pt 2), 342-350.
Full text: Click here
Publication 2012
Buffers Deuterium factor A Hydrogen Radius Roentgen Rays

Most recents protocols related to «Hydrogen»

1
Preparation of Cell-free Lactobacillus Fractions
Not available on PMC !

Example 1

Cell-free fractions were prepared as previously described (25). Briefly, Lactobacillus acidophilus strain La-5 was grown overnight in modified DeMann, Rogosa and Sharpe medium. (mMRS; 10 g peptone from casein, 8 g meat extract, 4 g yeast extract, 8 g D(+)-glucose, 2 g dipotassium hydrogen phosphate, 2 g di-ammonium hydrogen citrate, 5 g sodium acetate, 0.2 g magnesium sulfate, 0.04 g manganese sulfate in 1 L distilled water) (MRS; BD Diagnostic Systems, Sparks, MD). The overnight culture was diluted 1:100 in fresh medium. When the culture grew to an optical density at 600 nm (OD600) of 1.6 (1.2×108 cells/ml), the cells were harvested by centrifugation at 6,000×g for 10 min at 4° C. The supernatant was sterilized by filtering through a 0.2-μm-pore-size filter (Millipore, Bioscience Division, Mississauga, ON, Canada) and will be referred to as cell-free spent medium (CFSM). Two litres of L. acidophilus La-5 CFSM was collected and freeze-dried (Unitop 600 SL, VirTis Co., Inc. Gardiner, NY., USA). The freeze-dried CFSM was reconstituted with 200 ml of 18-Ω water. The total protein content of the reconstituted CFSM was quantified using the BioRad DC protein assay kit II (Bio-Rad Laboratories Ltd., Mississauga, ON, Canada). Freeze-dried CFSM was stored at −20° C. prior to the assays.

US11857581B2. Antiviral methods and compositions comprising probiotic bacterial molecules (2024-01-02). MicroSintesis Inc. [CA]. Inventors: Mansel Griffiths [CA].
Full text: Click here
Patent 2024
ammonium citrate Biological Assay casein peptone Cells Centrifugation Diagnosis Freezing Glucose Hydrogen Lactobacillus acidophilus L Cells manganese sulfate Meat potassium phosphate, dibasic Proteins Sodium Acetate Sulfate, Magnesium Unitop Yeast, Dried
2
Fabrication of Micro-Porous Nickel-Copper Foams
Not available on PMC !

Example 7

A piece of rolled commercial nickel foam (2.5×4 cm2, 200 μm in thickness, MTI Corporation, CA, USA) was soaked in sulfuric acid (H2SO4, 1M) for 20 min to remove the native nickel oxide layer. Then, a thin layer of Cu film was electroplated at −1.8V (vs. Ag/AgCl) for 800 coulombs from an electrolyte made of copper sulfate (CuSO4, 2M) and boric acid (H3BO3, 1M) with copper foil serving as the counter electrode (MTI Corporation, CA, USA). Next, the Cu—Ni composite foams were annealed at a temperature of 1000° C. in a gas flow of hydrogen (H2, 5 sccm) and nitrogen (N2, 50 sccm) at 420 mTorr for 5 min. Finally, the annealed composite was electrochemically etched at +0.6 V (vs. Ag/AgCl) in the same electrolyte for 350 coulombs, resulting in large arrays of micropores uniformly distributed on the interconnected microstruts of the foam.

US11858816B2. Dendritic materials with hierarchical porosity (2024-01-02). BOARD OF REGENTS, THE UNIVERSITY OF TEXAS SYSTEM [US]. Inventors: Weigu Li [US], Donglei Fan [US].
Full text: Click here
Patent 2024
Anabolism boric acid Copper Electrolytes Hydrogen Nickel nickel monoxide Nitrogen Sulfate, Copper Sulfuric Acids
3
Synthesis of Compound Ia-10 and Derivative
Not available on PMC !

Example 5

[Figure (not displayed)]

A solution of Compound C (160 mg, 0.28 mmol) and NEt3 (798 μL, 2.8 mmol) in DCM (2 mL) was treated with phosgene solution (906 μL, 1.4 mmol, 0.5 M in toluene) at 0° C., and the resulting mixture was stirred at 0° C. for 0.5 hr under nitrogen. The reaction mixture was then added to MeOH (2 mL) at 0° C. and stirred for an additional 1 hr. The solvent was removed, and the residue was purified by silica gel chromatography. The THP-protected methylcarbonate was dissolved in MeOH (4 mL), treated with PPTS (catalytic) and stirred at 50° C. for 2 hr. The reaction mixture was concentrated, and the residue was dissolved in MTBE (20 mL) and washed with water and then brine to yield crude THP-deprotected methylcarbonate. The crude product was taken in dioxane (5 mL) along with 10% Pd/C (28 mg) and hydrogenated under a hydrogen atmosphere to yield crude Compound Ia-10 (83 mg) as an oil. MS: m/z 471 [M+Na]+

The following compound was synthesized using similar procedures as above:

[Figure (not displayed)]

US11866402B2. Treprostinil derivatives and compositions and uses thereof (2024-01-09). CORSAIR PHARMA, INC. [US]. Inventors: Xiaoming Zhang [US], Meenakshi S. Venkatraman [US], Cyrus K. Becker [US].
Full text: Click here
Patent 2024
Acetic Acid Anabolism Atmosphere brine Catalysis Chromatography dioxane fluoromethyl 2,2-difluoro-1-(trifluoromethyl)vinyl ether Gel Chromatography Hydrogen methyl tert-butyl ether Nitrogen Phosgene Podofilox Silica Gel Silicon Dioxide Solvents Toluene
4
Electrodeposition of Ni-Cu Dendrites on Porous Ni Foam
Not available on PMC !

Example 2

A planar conducting substrate, such as Ni and Cu foils, or a 3-D Ni foam was immersed in 1M H2SO4 to remove the oxide layer and then transferred to Ni—Cu electrolyte (0.1 M nickel chloride, 0.5 M nickel sulfamate, 0.0025 M copper chloride and 0.323 M boric acid). After electrodeposition at a current of −350 mA for 150 coulombs, the sample was turned upside down, and the surface pointing to the reference electrode was also reversed. Then another deposition is continued. Totally four such depositions were carried out on each sample. Next, the obtained Ni—Cu dendrites on porous nickel foam were enforced by annealing in nitrogen (50 SCCM) and hydrogen (5 SCCM) gas atmosphere at the temperature of 1000° C. for 5 min.

US11858816B2. Dendritic materials with hierarchical porosity (2024-01-02). BOARD OF REGENTS, THE UNIVERSITY OF TEXAS SYSTEM [US]. Inventors: Weigu Li [US], Donglei Fan [US].
Full text: Click here
Patent 2024
Atmosphere boric acid Chlorides Copper Dendrites Electrolytes Electroplating Hydrogen Lanugo Nickel nickel chloride Nitrogen Oxides sulfamate
5
Synthesis of Varenicline Free Base
Not available on PMC !

Example 21

A solution of Varenicline free base (5.0 g) in methanol (50 mL) was stirred under hydrogen (6.0 kg/cm2) pressure in the presence of 10% Palladium on carbon (0.5 g, 50% wet) at 25-35° C. After 6 hours and the reaction mass was filtered through celite pad and rinsed with methanol (50 ml). The filtrate was distilled completely under reduced pressure and co-distilled with methyl tert-butyl ether to remove traces of methanol & water. methyl tert-butyl ether (50.0 mL, 10.0 volume) was added to the concentrated mass and stirred at 5-10° C. for 3 hours. The product was isolated by filtration and washed with chilled methyl tert-butyl ether by twice. The wet material was dried at 40° C. under vacuum. Yield: 4.2 g

US11872234B2. Vareniciline compound and process of manufacture thereof (2024-01-16). Par Pharmaceutical, Inc. [US]. Inventors: Joseph Prabahar Koilpillai [IN], Sayuj Nath [IN], Satish Patil [IN], Somasundaram Muthuramalingam [IN], Selvakumar Viruthagiri [IN], Mohankumar Lakshmanan [IN].
Full text: Click here
Patent 2024
Carbon Celite Filtration Hydrogen Methanol methyl tert-butyl ether Palladium Pressure Vacuum Varenicline

Top products related to «Hydrogen»

1
Tools by AutoDock
Sourced in United States, Hungary
AutoDock Tools is a software suite designed to perform molecular docking simulations. It provides a graphical user interface (GUI) for preparing input files, running docking calculations, and analyzing the results. The core function of AutoDock Tools is to predict the preferred binding orientations and affinities between a small molecule and a target protein.
2
Tools 1 by AutoDock
Sourced in United States
AutoDock Tools 1.5.6 is a molecular docking software package. It allows users to perform automated docking of ligands (small molecules) to protein receptors. The software provides a graphical user interface for preparing input files, running docking calculations, and analyzing the results.
3
Protein preparation wizard by Schrödinger
Sourced in United States
The Protein Preparation Wizard is a laboratory tool designed to automate the process of preparing protein samples for analysis. It streamlines the various steps involved in protein preparation, including solubilization, purification, and buffer exchange, to ensure consistent and reliable results. The core function of the Protein Preparation Wizard is to simplify and standardize the protein preparation workflow, enabling researchers to focus on their core research objectives.
4
Vina 1 by AutoDock
Sourced in United States
AutoDock Vina 1.1.2 is a software application designed for molecular docking. It is capable of predicting the binding affinity and orientation of small molecules (ligands) to a given protein (receptor). The software uses a hybrid global-local search engine and a scoring function to evaluate the potential binding interactions between the ligand and the receptor.
5
Sodium hydroxide (naoh) by Merck Group
Sourced in Germany, United States, India, United Kingdom, Italy, China, Spain, France, Australia, Canada, Poland, Switzerland, Singapore, Belgium, Sao Tome and Principe, Ireland, Sweden, Brazil, Israel, Mexico, Macao, Chile, Japan, Hungary, Malaysia, Denmark, Portugal, Indonesia, Netherlands, Czechia, Finland, Austria, Romania, Pakistan, Cameroon, Egypt, Greece, Bulgaria, Norway, Colombia, New Zealand, Lithuania
Sodium hydroxide is a chemical compound with the formula NaOH. It is a white, odorless, crystalline solid that is highly soluble in water and is a strong base. It is commonly used in various laboratory applications as a reagent.
6
Nicotine hydrogen tartrate by Merck Group
Sourced in United States, Sao Tome and Principe, United Kingdom
Nicotine hydrogen tartrate is a chemical compound used in various laboratory applications. It serves as a precursor for the synthesis of nicotine and other related compounds. The compound is a white, crystalline powder with a characteristic odor. It is soluble in water and certain organic solvents. Nicotine hydrogen tartrate is primarily utilized in research and development settings to facilitate the study and production of nicotine-based products.
7
Sodium chloride (nacl) by Merck Group
Sourced in United States, Germany, United Kingdom, India, Italy, France, Spain, China, Canada, Sao Tome and Principe, Poland, Belgium, Australia, Switzerland, Macao, Denmark, Ireland, Brazil, Japan, Hungary, Sweden, Netherlands, Czechia, Portugal, Israel, Singapore, Norway, Cameroon, Malaysia, Greece, Austria, Chile, Indonesia
NaCl is a chemical compound commonly known as sodium chloride. It is a white, crystalline solid that is widely used in various industries, including pharmaceutical and laboratory settings. NaCl's core function is to serve as a basic, inorganic salt that can be used for a variety of applications in the lab environment.
8
Hydrochloric acid (hcl) by Merck Group
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Hydrochloric acid is a commonly used laboratory reagent. It is a clear, colorless, and highly corrosive liquid with a pungent odor. Hydrochloric acid is an aqueous solution of hydrogen chloride gas.
9
Nicotine hydrogen tartrate salt by Merck Group
Sourced in United States, Canada, Sao Tome and Principe
Nicotine hydrogen tartrate salt is a chemical compound that is used as a laboratory reagent. It is a crystalline solid and is soluble in water and various organic solvents. The compound is commonly used in scientific research and development applications.
10
Methanol by Merck Group
Sourced in Germany, United States, Italy, India, United Kingdom, China, France, Poland, Spain, Switzerland, Australia, Canada, Sao Tome and Principe, Brazil, Ireland, Japan, Belgium, Portugal, Singapore, Macao, Malaysia, Czechia, Mexico, Indonesia, Chile, Denmark, Sweden, Bulgaria, Netherlands, Finland, Hungary, Austria, Israel, Norway, Egypt, Argentina, Greece, Kenya, Thailand, Pakistan
Methanol is a clear, colorless, and flammable liquid that is widely used in various industrial and laboratory applications. It serves as a solvent, fuel, and chemical intermediate. Methanol has a simple chemical formula of CH3OH and a boiling point of 64.7°C. It is a versatile compound that is widely used in the production of other chemicals, as well as in the fuel industry.

More about "Hydrogen"

Hydrogen is a versatile and widely-used element that plays a crucial role in various industries, energy applications, and scientific research.
This lightweight and highly reactive gas has a diverse range of applications, making it an essential component in many chemical processes and technological advancements.
One of the primary uses of hydrogen is as a clean and sustainable fuel source.
Hydrogen-powered vehicles and energy systems are gaining traction as alternatives to traditional fossil fuels, offering a more environmentally-friendly option.
Researchers and scientists are constantly exploring new ways to harness the power of hydrogen, leveraging tools like AutoDock Tools, AutoDock Tools 1.5.6, and AutoDock Vina 1.1.2 to optimize their research efforts.
In the chemical industry, hydrogen is a key ingredient in the production of a wide range of materials, including sodium hydroxide, nicotine hydrogen tartrate, and sodium chloride (NaCl).
The Protein Preparation Wizard is a valuable tool used in the analysis and preparation of protein structures, which can be crucial in understanding the interactions and behavior of these chemical compounds.
Beyond its industrial applications, hydrogen also plays a vital role in scientific research and development.
Researchers often use hydrogen as a model system to study fundamental physical and chemical processes, such as reaction kinetics, molecular interactions, and energy transfer.
The use of advanced tools like PubCompare.ai can enhance the reproducibility and accuracy of these studies, enabling scientists to identify the most effective protocols from literature, pre-prints, and patents.
By streamlining the research process and optimizing the use of hydrogen in their work, researchers and scientists can unlock new levels of efficiency and innovation, driving progress in areas like energy, materials science, and biotechnology.
Whether you're working with hydrogen-based technologies, exploring chemical processes, or conducting fundamental research, leveraging the right tools and resources can be a game-changer in your hydrogen-related projects.