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Chemical properties

Chemical Properites: Discover the fundamental characteristics and behaviors of chemical substances.
This includes physical properties like melting point, boiling point, solubility, and density, as well as chemical properties like reactivity, acidity, and oxidation state.
Understanding a chemical's properties is crucial for designing effective experiments, formulating products, and predicting interactions.
Explore the diverse world of chemical properties and how they influence the behavior and applications of materials at the molecular level.

Most cited protocols related to «Chemical properties»

1
Expanding Biomolecular Force Fields with QM Data
Class I additive force fields (see equation 1), which do not explicitly treat electronic polarization, have been designed for use in polar environments typically found in proteins and in solution. To achieve this, the use of experimental target data, supplemented by QM data, was strongly emphasized during optimization of the nonbonded parameters in the biomolecular CHARMM force fields, in order to ensure physical behavior in the bulk phase. However, reproducing experimental data requires molecular dynamics (MD) simulations, which have to be set up carefully and repeated multiple times in the course of the parametrization, making the usage of experimental target data non-trivial and time-consuming. In addition, for many functional groups that may occur in drug-like molecules experimental data may not be available. Due to this lack of data, and since one of the main goals of CGenFF is easy and fast extensibility, a slightly different philosophy was adapted, with more emphasis on QM results as target data for parameter optimization. This is possible due to the wide range of functionalities already available whose parameters were optimized based largely on experimental data, along with the establishment of empirical scaling factors that can be applied to QM data in order to make them relevant for the bulk phase.
The only cases where experimental data would be required are situations where novel atom types are present for which LJ parameters are not already available in CGenFF. These cases would require optimization of the LJ parameters, supplemented with Hartree-Fock (HF) model compound-water minimum interaction energies and distances (see step 2.a under “Generation of target data for parameter validation and optimization” and step 1 under “Parametrization procedure”), based on the reproduction of bulk phase properties, typically pure solvent molecular volumes and heats of vaporization or crystal lattice parameters and heats of sublimation. Descriptions of the optimization protocol have been published previously.7 ,9 ,25 (link) However, it should be noted that CGenFF has been designed to cover the majority of atom types in pharmaceutical compounds, such that optimization of LJ parameters is typically not required.
The remainder of this section includes 1) the procedure to add new model compounds and chemical groups to the force field, 2) the procedure for generating the QM target data, and 3) the procedure for application of the QM information to parametrize new molecules. To put these procedures in better context, example systems including pyrollidine, the addition of substituents to pyrollidine and the development of a linker between pyrollidine and benzene are presented.
Vanommeslaeghe K., Hatcher E., Acharya C., Kundu S., Zhong S., Shim J., Darian E., Guvench O., Lopes P., Vorobyov I, & MacKerell AD J.r. (2010). CHARMM General Force Field (CGenFF): A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of computational chemistry, 31(4), 671-690.
Publication 2010
Benzene Dietary Fiber Pharmaceutical Preparations Physical Examination Proteins Reproduction Solvents Vaporization
2
Molecular Analysis with MDAnalysis
MDAnalysis, which was initially inspired by MDTools for Python (http://www.ks.uiuc.edu/Development/MDTools/Python/, J.C. Phillips, unpublished) and MMTK 8 , is implemented as a Python package. It consists of a core library, which is exposed via the Universe class in the top-level name space and the analysis sub-module, which contains an expanding selection of functionality that make use of the core library (Figure 1). A number of performance critical or low-level input/output (I/O) routines are written in C (either directly or using Cython), and hence installation requires a working C-compiler. It has been tested successfully on Linux and Mac OS X platforms. Reading of CHARMM DCD trajectory files utilizes open source code from catdcd (part of VMD7 (link)) and PDB files are read with the Bio.PDB package13 (link). For some analysis functions a fast linear algebra library such as LAPACK, ATLAS or the native vecLib framework on Mac OS X is needed. MDAnalysis depends on the NumPy package (http://numpy.scipy.org). The development process follows standard software engineering “best practice”14 by using a publicly accessible version-controlled source code repository with a bug tracker and a mailing list dedicated to the project. Importantly, individual code blocks are tested through an extensive unit test suite, ensuring that enhancements and bug fixes do not break old code or re-introduce bugs.
MDAnalysis is fully object-oriented and treats atoms, residues, segments and trajectories as objects. These objects are represented in Python as classes with appropriate functions (“methods”) and variables (“attributes”) defined on these objects. In the following we describe the overall architecture of the package and some of the most important classes to enable users to make best use of the library. A complete simulation system is represented by the Universe class (Figure 1). It is initialized from a topology file, which defines the atoms in the system, and a trajectory or coordinate file, which lists the position of each atom for a number of time frames or a single snapshot.
Universe contains the attribute atoms (an instance of an AtomGroup), which can be thought of as a list of all atoms that are represented as Atom instances. The Atom is the fundamental object in MDAnalysis. It contains data such as chemical type, partial charge, or its Cartesian coordinates. For convenience, atoms are grouped in residues (represented by a Residue class, which is a special AtomGroup); a list of residues can be referred to as a Segment (a ResidueGroup that inherits from AtomGroup). A segment can correspond to a chain in a PDB file or a whole multi-chain protein; similarly, a residue typically corresponds to a single amino acid in a peptide chain, water molecule, or lipid. Residues and segments are simply containers for Atom objects; an Atom records the Residue, Segment and Universe it belongs to. In this way it is possible to directly switch between structural hierarchies, depending on which level is the most convenient for a task at hand. Attributes of Atom instances can be read (and set) individually and thus provide fine grained control for specific analysis tasks. Python indexing and “slicing” of an AtomGroup returns an Atom or a list of Atoms; index operations on a Segment return Residue objects.
In many cases, the user is interested in a property of a group of atoms. Any AtomGroup has a number of methods predefined that provide properties of all atoms in the group (such as coordinates or masses) as NumPy arrays or aggregate properties of the whole group such as the center of mass, the total mass, or the principal axes (see Figure 2 and the documentation of the package, accessible from within Python via the help (Classname) command). Every AtomGroup (which includes Residue, ResidueGroup, Segment and SegmentGroup classes by inheritance) also has the attributes atoms, residues, and segments. They contain lists of those Atom, Residue, and Segment instances to which the atoms in the group belong; for instance, residues contains all residues of which the atoms are members so that one can quickly find all residues for which a certain atom-based selection criterion is true. Because such a consistent application programming interface (API) applies at all levels of the structural hierarchy, concise code can be written that processes segments, residues and arbitrary collections of atoms in the same manner. Additionally, managed attributes are automatically generated that simplify access to certain selections. For instance, any AtomGroup contains an attribute for each atom name that it contains (such as “CA” or “N”). Such an “instant selector” attribute provides a group of all atoms with the same name, e.g. all CA-atoms. Similarly, a Segment also contains instant selectors for residue names and residue numbers (the latter are prefixed with the letter “r” to make them valid Python identifiers). The Universe contains instant selectors for segment identifiers (prefixed with “s” if they start with a number).
A new AtomGroup can be generated from an instant selector or a selection via the selectAtoms() method. MDAnalysis contains a full selection language comparable to the one offered by CHARMM6 (link) or VMD7 (link). It includes selections by atom properties, connectivity, and geometry (such as distances). Selections can be grouped by Boolean operators to create arbitrarily complex expressions.
A simulation trajectory file consists of a sequence of coordinate frames. MDAnalysis provides a view or a “cursor” on the trajectory. A trajectory has a Timestep object associated with it (Figure 2). The Timestep contains the current frame number, the unitcell dimensions (if recorded in the trajectory file), and the coordinates of all atoms. Atom and AtomGroup coordinates always refer to the coordinates in the current time step; they update automatically. A trajectory Reader instance is responsible for reading the trajectory file and populating the Timestep whenever a new frame is read from the file. Reader classes can be used as Python iterators, they have a next() method to advance the trajectory cursor, and they typically also allow indexing to jump to a specific frame. A Writer class is used to write a Timestep to a file on disk. The design of the library is easily extensible; for instance, new trajectory readers and writers can be added through a common API by inheriting from the coordinates.base.Reader class and adding appropriate low-level code for the actual I/O.
The default units in MDAnalysis are the ångström (10−10 m) for length and the picosecond (10−12 s) for time. Data from trajectories are automatically converted to these units, and data written to trajectories is converted to the native format (although this behavior can be changed using internal flags). The unit cell representation differs between trajectory formats. MDAnalysis always makes the unit cell available in the dimensions attribute as a tuple (a,b,c,α,β,γ) for the lengths and angles of the parallelepiped forming the simulation box.
Michaud-Agrawal N., Denning E.J., Woolf T.B, & Beckstein O. (2011). MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. Journal of computational chemistry, 32(10), 2319-2327.
Publication 2011
3
Predictive Models for ADME Properties
The Bioavailability Radar in the first section of the One-panel-per-molecule output complements the two-dimensional image from the JChem webserver and the canonical SMILES calculated by OpenBabel. We use the JpGraph PHP library (version 3.5.0b1, 2016, http://jpgraph.net) to produce the radar plot, which bears six axes for six important properties for oral bioavailability. Each property is defined by a descriptor of SwissADME and a range of optimal values is depicted as a pink area. The latter is inspired from commonly accepted bioavailability and drug-likeness guidelines23 (link)24 (link). For saturation, the ratio of sp3 (link) hybridized carbons over the total carbon count of the molecule (Fraction Csp3) should be at least 0.25. For size, the molecular weight (MW calculated by OpenBabel) should be between 150 and 500 g/mol. For polarity, the TPSA25 (link) should be between 20 and 130Å2 (link). For solubility, log S (calculated with the ESOL model36 ) should not exceed 6. For lipophilicity, XLOGP329 (link) should be in the range from −0.7 to +6.0. For flexibility, the molecule should not have more than 9 rotatable bonds. To be estimated as drug-like, the red line of the compound under study must be fully included in the pink area. Any deviation represents a suboptimal physicochemical property for oral bioavailability.
All descriptors and molecular parameters of the Physicochemical Properties section are computed through the OpenBabel API (version 2.3.0, 2012, http://openbabel.org)9 (link). Noteworthy, the topological polar surface area (TPSA) is strictly based on the fragmental system provided by Ertl et al.25 (link) including polar sulfur and phosphorus atoms.
Multiple freely available computational methods to predict n-octanol/water partition coefficient (log Po/w) values are made available in the Lipophilicity section. iLOGP (for implicit log P) is an in-house physics-based methods relying on Gibbs free energy of solvation calculated by GB/SA in water and n-octanol. Generalized-born (GB) parameters are computed through the GBMV2 method68 (link) and solvent-accessible surface area (SA) is the analytical approximation generated by CHARMM (version c36b1, 2011, https://www.charmm.org)69 (link). The iLOGP implemented in SwissADME corresponds to Model9 of the seminal publication16 (link), which was trained on 11,993 molecules (r = 0.72, MAE = 0.89, and RMSE = 1.14 against experimental log P). 5-fold crossvalidation ensured robustness (q2CV = 0.52, MAECV = 0.89, and RMSECV = 1.14) and external test benchmarks showed the excellent predictive power and extended applicability domain compared to well-established methods. XLOGP3 values are obtained through the command-line Linux program (version 3.2.2, courtesy of CCBG, Shanghai Institute of Organic Chemistry) including the knowledge-based corrections29 (link). WLOGP is our own implementation of the atomistic method developed by Wildman and Crippen30 . MLOGP values are computed through an in-house implementation of Moriguchi’s topological method31 32 . SILICOS-IT is the log Po/w estimation returned by executing the FILTER-IT program (version 1.0.2, 2013, http://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/filter-it/1.0.2/filter-it.html). Finally, SwissADME gives a consensus log Po/w value, which is the arithmetic mean of the five predictive values mentioned above.
Similarly to lipophilicity, the Water Solubility section includes multiple predictive methods for the user to choose between the most accurate model for a given chemical series and an averaged consensus value. The ESOL model36 is a QSPR model establishing the linear relationship between log S and five molecular parameters, i.e. MW, the number of rotatable bonds, the fraction of aromatic heavy atoms and Daylight’s CLOGP. Because the lipophilicity descriptor is not freely available, the implementation of ESOL in SwissADME replaces CLOGP by XLOGP3 as parameter in the linear equation to predict log S. XLOGP3 is known to perform well on external datasets and to return similar predictions as CLOGP28 (link). The other three parameters were computed with OpenBabel. Likewise, Ali et al.37 (link) linked log S with log Po/w and TPSA. The model implemented in SwissADME corresponds to the model 3 of the original publication, with XLOGP3 as lipophilicity descriptor. The third solubility method available in SwissADME is the log S estimated by the FILTER-IT program (version 1.0.2, 2013, http://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/filter-it/1.0.2/filter-it.html). This prediction is based on a system of 16 fragmental contributions modulated by the squared root of MW. All three models are predicting log S values, which are also translated within SwissADME into solubility in mol/l and mg/ml. Finally a qualitative estimation of the solubility class is given according to the following log S scale: insoluble <−10 The Pharmacokinetics section proposes one linear method for skin permeation, which relies on the simple QSPR model by Potts and Guy39 linking the decimal logarithm of the skin permeability coefficient (log Kp in cm/s) with MW and log Po/w. The model implemented in SwissADME uses XLOGP3 as lipophilicity descriptor. Besides, most of the models in this section are machine-learning binary classifiers for important ADME behaviours. Passive gastro-intestinal (HIA) absorption and blood-brain barrier (BBB) permeation are predicted with the BOILED-Egg model, which defines favourable and unfavourable zones in the log Po/wversus PSA physicochemical space for passive diffusion through both physiological barriers17 (link). The classification showed 10-fold cross-validation accuracy of 92% and 88% for BBB and HIA, respectively (refer to Graphical Output).
Six other classification models are part of the Pharmacokinetics section to predict the propensity of the molecule under investigation to be substrate or inhibitor of important pharmacokinetics-related proteins, for which large diverse and balanced datasets were retrieved and meticulously cleansed. For P-glycoprotein1 (P-gp), the training set consists of 521 substrates and 512 non-substrates extracted from the Metrabase database70 (link) (http://www-metrabase.ch.cam.ac.uk, accessed January 2016), whereas the test set was obtained from ref. 71 (link). To ensure truly external validation, molecules overlapping with the training set were removed from the test set, which finally includes 215 substrates and 200 non-substrates. For CYP major isoforms, all datasets were those of Veith et al.50 (link) and downloaded from the PubChem database72 (link) (http://pubchem.ncbi.nlm.nih.gov, accessed February 2016). In case of unbalanced dataset (all except CYP1A2 and CYP2C19), sufficient chemical diversity was guaranteed by clustering with the Ward method and a reciprocal nearest neighbour (RNN) algorithm73 , the more populated class to lessen. The number of molecules (described by circular fingerprints) of the large class is reduced by defining clusters with the JKlustor program (version 14.9.29, 2014, http://www.chemaxon.com). Only the centre of each cluster (i.e. the molecule that has the smallest sum of dissimilarities to the other molecules in the cluster) is included in the training or test set to balance. As a result, the training sets involved respectively 4301 CYP1A2 inhibitors and 4844 CYP1A2 non-inhibitors; 4284 CYP2C19 inhibitors and 4988 CYP2C19 non-inhibitors; 2940 CYP2C9 inhibitors and 3000 CYP2C9 non-inhibitors; 1814 CYP2D6 inhibitors and 1850 CYP2D6 non-inhibitors; and 3758 CYP3A4 inhibitors and 3760 CYP3A4 non-inhibitors. The test sets involved respectively 1412 CYP1A2 inhibitors and 1588 CYP1A2 non-inhibitors; 1386 CYP2C19 inhibitors and 1614 CYP2C19 non-inhibitors; 1020 CYP2C9 inhibitors and 1055 CYP2C9 non-inhibitors; 528 CYP2D6 inhibitors and 540 CYP2D6 non-inhibitors; and 1289 CYP3A4 inhibitors and 1290 CYP3A4 non-inhibitors.
SwissADME’s backend calculations were ran to generate 50 molecular and physicochemical descriptors per molecule (described in the Supplementary Table S1). For a given model, a descriptor was rejected if non-zero values for all molecules in the training set are less than 20% or if the coefficient of variation is less than 3%. In case of correlation higher than 0.9 between remaining descriptors, a selection is made based on F-score. The selected descriptors for each model are shown in Supplementary Tables S2–S7. These tables also include the minimum and maximum values for each descriptor among all molecules used in the training. This enables beholding the broadness of physicochemical space involved and the applicability domain of the SVM models. The predictive capability of each model can be further appraised on Supplementary Table S8, where external accuracy was split in sensitivity and specificity to ensure that positive and negative molecules are predicted with the same level of robustness. The final training and test sets with selected descriptors were normalized and the respective model ready to be built. First, the libSVM support vector machine python library (version 3.20, 2015, https://www.csie.ntu.edu.tw/~cjlin/libsvm/)74 was used for multi-step grid-based optimization of the best coefficients for the above-selected descriptors as well as for the soft-margin permissivity (C) and the hyper-parameter (ϒ) of the RBF Gaussian kernel function. The 10-fold crossvalidated accuracy (ACCCV) for each model was so maximized and AUCCV was calculated. In a second step, the so-built models were used on the external test sets (normalized according to the training set) in order to evaluate predictive power in terms of external accuracy (ACCext) and AUCext. All final SVM models were stored in separate files, which are read through the libSVM API upon SwissADME job submission.
Daina A., Michielin O, & Zoete V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717.
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Publication 2017
4
Automated CHARMM Protein-Ligand Docking
Since docking assays are carried out in the CHARMM22/27 all-hydrogen force field (26 (link)), target proteins and ligands that have been uploaded as CHARMM-formatted files can be used as is. Conversely, proteins and ligands that have been submitted in PDB or Mol2 format, respectively, have to be converted prior to the docking itself. This procedure, which generally requires a good knowledge of this software and of the physico-chemical properties of proteins and small molecules, is normally performed by experimented users. However, this step has been automatized for the SwissDock web service and is performed transparently for the user.
Grosdidier A., Zoete V, & Michielin O. (2011). SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Research, 39(Web Server issue), W270-W277.
Publication 2011
Biological Assay chemical properties Hydrogen Ligands Proteins
5
Quantitative Cardiac MRI Evaluation
Data for quantitative evaluation were acquired on a Siemens Magnetom Espree (Siemens AG Medical Solutions, Erlangen, Germany) 1.5-T scanner using a phased-array coil, in accordance with the local institutional review board. Twenty-five cardiac datasets were acquired (from 21 subjects), of which 15 were short-axis slice orientation and 10 were long-axis orientation. Imaging was performed with an ECG-triggered GRE sequence, using an echo-train with monopolar readout. Typical parameters included FOV = 36 cm × 27 cm; bandwidth = 977 Hz/pixel; pulse repetition time = 11.2 ms; flip angle = 20° to 25°; matrix size = 256 × 126, TE spacing between 1.9 ms and 3.07 ms (3 (link)). Usually four echoes were collected (often selected to provide nearly optimal noise properties (13 (link))), but only three are used in this work, to conform more closely to the common conditions used in water/fat separation (4 (link),10 (link)). One additional dataset, not included in the quantitative results, was acquired on a Siemens Avanto 1.5-T scanner with TEs {3.6, 5.8, 7.9}ms.
The proposed algorithm was run on each of the acquired two-dimensional slices, for 50 iterations in all cases, at which point the changes in the estimated field map were negligible. Multicoil data were processed jointly, as described in Hernando et al. (14 (link)). In order to evaluate the reliability of the proposed method, water/fat separation was performed on 25 cardiac datasets acquired with various slice orientations. Three echoes (N = 3) were used for each dataset. For comparison, the same datasets were also processed using a previously proposed method, based on VARPRO and the iterated conditional modes (ICM) algorithm, where the voxels are updated one at a time (14 (link)). Both methods included
T2∗ decay in the signal model. By visual inspection of the resulting decompositions, we counted the number of images containing errors (e.g., localized water/fat swaps). These swaps are defined as estimation errors where the main signal component in a voxel is assigned to the wrong chemical species (e.g., identifying as mostly fat a voxel that contains mostly water). Additionally, some of the datasets were processed using our own implementation of the voxel-independent IDEAL algorithm (without region growing or any other advanced features that may have been added to the current commercial implementation) (4 (link)). Note that the data used in the comparison have a different set of TEs from those suggested in Reeder et al. (4 (link)) and Pineda et al. (13 (link)). The TEs employed in this work are not signal-to-noise-ratio optimal (which would require a TE spacing of nearly 1.6 ms at 1.5 T) due to the monopolar readout with gradient flyback.
Hernando D., Kellman P., Haldar J.P, & Liang Z.P. (2010). Robust Water/Fat Separation in the Presence of Large Field Inhomogeneities Using a Graph Cut Algorithm. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 63(1), 79-90.
Publication 2010
ECHO protocol Epistropheus Ethics Committees, Research Heart Mental Orientation Pulse Rate

Most recents protocols related to «Chemical properties»

1
Soil Chemical Property Analysis Methodology
Not available on PMC !
Soil chemical properties were analyzed before planting and after the termination of the greenhouse study. For the determination of total nitrogen, the Kjeldahl procedure (Bremner and Mulvancy. 1982 ) was utilized. Available phosphorus was measured by a spectrophotometer (Olsen and Sommers, 1982) . A flame photometer according to Knudsen et al. (1982) was used to measure the available potassium. Soil chemical properties before transplanting were as follow: 7.1, 0.47 mS/cm, 0.17, 40.0, and 200 for pH, EC, N%, P (mg/kg), and K (mg/kg), respectively.
Abubaker S., Qrunfleh I.M., Shatnawi M., Ammari T.G., Hasan H.S., & Al-Tawaha A.R. (2024). The Effect of Compost Tea on Some Growth and Yield Parameters and Soil Chemical Properties of Greenhouse Tomato (<i>Solanum lycopersicum</i> L.). Ecological Engineering & Environmental Technology, 25(6), 362-370.
Publication 2024
2
Compost Chemical Properties Analysis
Not available on PMC !
The chemical properties of compost, namely organic C content, C/N ratio, and compost pH, were observed at the end of the study. Organic C content and C/N ratio were determined through laboratory tests. The compost chemical properties test was conducted by compositing three replicates of each treatment into one laboratory test. Observation of nutrient content was conducted at the end of the study through testing of the elements contained in the compost (NPK) by compositing three replicates of each treatment into one laboratory test.
Sukmawati F.N., Irna A., Pramudya Y., Sukarji S., Pamungkas S.S.T., Rahmat A., Rozaki Z., & Hanum F.F. (2024). Quality analysis of coffee waste compost with the addition of cassava tapai local microorganism (LMO) bioactivator. Applied Research in Science and Technology, 3(2), 72-85.
Publication 2024
3
Analyzing Chemical Properties of Lemon Juice Powder
Not available on PMC !
Chemical properties like carbohydrate, ascorbic acid, total total phenols, total flavonoids and antioxidant activity were analyzed in the spray dried lemon juice powder. Sadasivam and Manickam (2023).
Rohini C., Kanchana S., Geetha P., Mini M.L., & P. A.A.C. (2024). Optimize the process for encapsulated spray dried Lemon juice powder to enhance the Vitamin C. World Journal of Advanced Research and Reviews, 21(1), 2064-2072.
Publication 2024
4
Soil Chemical Characterization Methodology
Not available on PMC !
The chemical properties of this soil were characterized according to the methodology recommended by Empresa Brasileira de Pesquisa Agropecuária 10 and described below.
Hydrogen potential (pH): pH measurement using a combined electrode immersed in a solid aqueous suspension: liquid (H 2 O) ratio 1:2.5. An Orion potentiometer model 710A (Orion Research Inc., Boston, MA, USA) was used for this purpose.
The extraction of exchangeable calcium (Ca 2+ ) and magnesium (Mg 2+ ) was determined using a KCl (1 mol/L) solution, while sodium (Na + ) and potassium (K + ) were analyzed using a solution known as Mehlich-1, and then Ca 2+ and Mg 2+ ions were measured by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), model ULTIMA 2 (Horiba Inc, Japan) and Na + and K + were measured by Flame Photometer model DM-6 (Digimed). The exchangeable aluminum content (Al 3+ ) was obtained by extraction with KCl solution (1 mol/L) and determined by the volumetric method with diluted NaOH solution.
Nacinovic M.G.G., Mahler C.F., Marques M.C., Macedo J.R.d., Melo A.d.S., Souza G.d.S.e., & Borgesa R.C. (2024). Comparison Between Vetiver and Brachiaria Grass in Erosion Control in Tropical Climate. Sustainable Chemical Engineering.
Publication 2024
5
Soil Chemical Property Analysis
The chemical properties of pH, CEC, Ca2+, and Mg2+ were determined. Soil pH was determined using a pH electrode (Seven Excellence S479-uMix, Mettler-Toledo, Switzerland) in a 1:2.5 soil:water suspension. Ca2+ and Mg2+ contents were measured by using inductively coupled plasma‒optical emission spectrometry69 (link). CEC was determined by using a microplate reader (Synergy 2, BioTek, USA) following extraction using [Co(NH3)6]Cl3 (ref. 70 (link)).
Li J., Pei J., Fang C., Li B, & Nie M. (2024). Drought may exacerbate dryland soil inorganic carbon loss under warming climate conditions. Nature Communications, 15, 617.
Full text: Click here
Publication 2024

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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.

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