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Chemical content

Chemical content refers to the composition and properties of chemical substances, including their molecular structure, physical characteristics, and reactivity.
This term encompasses the study of organic and inorganic compounds, as well as the analysis and identification of chemical components in various materials and samples.
Chemical content is a fundamental aspect of chemistry, biology, and other scientific disciplines, and is essential for understanding the behavior and interactions of matter at the molecular level.
Reserach in this area can lead to advancements in areas such as drug development, material science, and environmental protection.

Most cited protocols related to «Chemical content»

Fecal and Urinary Excretion Profiles

Generation rate, composition, and physical and chemical nature of both feces and urine were recorded as of Table 1. Each recorded datum was the mean of the data from the reported study. Some published papers reported two or more independent studies so these papers contributed more than one value to the data set. The mean and median of each variable were both calculated as measures of central tendency and data were checked for normality by calculating a coefficient of skewness (Young, 1962 ):

Table 1 Measured variables for feces and urine

	Feces unit	Urine unit
Variable	of measure	of measure
Generation	g/cap/day	L/cap/day
Frequency of defecation	motions/24 hr	urinations/24 hr
Water content	% total mass	% total mass
Organic composition	% total mass	% dry mass
Components of solids	% total mass	% total mass
Inorganic composition	% dry mass	% dry mass
Daily excretion of elements	g/cap/day	g/cap/day, mg/L
Chemical nature
pH	pH	pH
COD and BOD	mg/g wet mass	mg/L
Physical form
Bristol stool form	Linear scale (1–7)
Diarrhea prevalence	% of population

σ = Standard deviationn = Valid number of casesBox and whisker plots were created using Statistica 11 software (Statsoft Inc., Tulsa, OK, USA, 2011). Outliers of each data set were defined using a standard default outlier coefficient value (Burns et al., 2005 (link)).

No outliers were removed from the data set but were identified in the graphical output. Full statistical calculations were only conducted on variables that had at least seven values but a median value is given for data when there were less than seven values.
A summary of studies used in the statistical analysis are outlined in Table 2, including the location and number of studies. A large proportion (80%) of the data set was from studies conducted in Europe and North America. A distinction was therefore made between low and high income countries by the measure of development; using the Human Development Index (HDI), a composite index measuring average achievement in three basic dimensions of human development; life expectancy, education, and income (UNDP, 2011 ).
Table 2 The geographical location and human development index ranking of studies used in statistical analysis

Country	n	HDI*	References
Africa	2	3/4^a	Cranston and Burkitt (1975 (link)), Burkitt et al. (1980 (link))
Australia	2	1	Birkett et al. (1996 (link)), Hovey et al. (2003 (link))
Burma	1	4	Myo-Kin et al. (1994 (link))
Canada	3	1	Burkitt et al. (1980 (link)), Vuksan et al. (1999 (link))
China	3	2	Jie et al. (2000 (link)), Chen et al. (2008 (link)), Bai and Wang (2010 )
Denmark	2	1	Maclennan and Jensen (1977 (link)), Jensen et al. (1982 (link))
Developing countries	2	3/4^a	Feachem et al. (1978 )
Europe and North America	1	1/2^b	Feachem et al. (1978 )
European	1	1^b	Mykkänen et al. (1998 (link))
Finland	4	1	Reddy et al. (1975 (link)), Reddy et al. (1978 (link)), Jensen et al. (1982 (link)), Mykkänen et al. (1998 (link))
Germany	1	1	Erhardt et al. (1997 (link))
Guatemala	1	3	Calloway and Kretsch (1978 (link))
Holland	4	1	Stasse-Wolthuis et al. (1980 (link)), Van Faassen et al. (1993 (link)), Gaillard (2002 ), Wierdsma et al. (2011 (link))
India	1	3	Shetty and Kurpad (1986 (link))
Iran	1	2	Adibi et al. (2007 (link))
Japan	7	1	Glober et al. (1977 ), Polprasert and Valencia (1981 ), Tarida et al. (1984 (link)), Saitoh et al. (1999 (link)), Danjo et al. (2008 (link)), Shinohara et al. (2010 (link)), Hotta and Funamizu (2009 (link))
Kenya	1	4	Cranston and Burkitt (1975 (link))
New Zealand	1	1	Pomare et al. (1981 )
North America	1	1^b	Vuksan et al. (2008 (link))
Peru	1	2	Crofts (1975 (link))
Singapore	1	1	Chen et al. (2000 (link))
South Africa	2	3	Burkitt et al. (1972 ), Walker (1975 (link))
Spain	1	1	Roig et al. (1993 (link))
Sweden	4	1	Reddy et al. (1978 (link)), Vinneras (2002 ), Vinnerås et al. (2006 )
Thailand	2	2	Danivat et al. (1988 ), Schouw et al. (2002 (link))
Tonga	1	2	Pomare et al. (1981 )
UK	26	1	Olmsted et al. (1934 ), Connell et al. (1965 (link)), Southgate and Durnin (1970 (link)), Burkitt et al. (1972 ), Goy et al. (1976 (link)), Wyman et al. (1978 (link)), Prynne and Southgate (1979 (link)), Stephen and Cummings (1980 (link)), Eastwood et al. (1984 (link)), Eastwood et al. (1986 (link)), Davies et al. (1986 (link)), Cummings et al. (1987 (link)), Sandler and Drossman (1987 (link)), Cummings et al. (1992 (link)), Murphy et al. (1993 (link)), Cummings et al. (1996 (link)), Lewis and Heaton (1997 (link)), Chen et al. (1998 (link)), Reddy et al. (1998 (link)), Rivero-Marcotegui et al. (1998 (link)), Aichbichler et al. (1998 (link)), Almeida et al. (1999 ), Magee et al. (2000 (link)), Chaplin et al. (2000 (link)), Woodmansey et al. (2004 (link)), Silvester et al. (2011 (link))
USA	18	1	Canfield et al. (1963 ), Watts et al. (1963 (link)), Diem and Lentner (1970 ), Goldsmith and Burkitt (1975 (link)), Cummings et al. (1978 ), Glober et al. (1977 ), Goldberg et al. (1977 (link)), Beyer and Flynn (1978 (link)), Reddy et al. (1978 (link)), Calloway and Kretsch (1978 (link)), Kien et al. (1981 (link)), Polprasert and Valencia (1981 ), Tucker et al. (1981 (link)), Schubert et al. (1984 (link)), Parker and Gallagher (1988 ), Zuckerman, et al. (1995 (link)), Aichbichler et al. (1998 (link)), McRorie et al. (2000)

*Human Development Index Classifications (UNDP, 2011 ): 1. Very high, 2. High, 3. Medium, 4. Low.^aClassification not available, presumed to be ranking 3 or 4.^bClassification not available, presumed to be ranking 1 or 2.

Preliminary data analysis indicated that fiber intake was a major cause of variation in fecal generation and composition. There were a sufficient number of studies that had examined the effects of fiber intake on fecal output to enable further analysis to be undertaken on these data. The total dietary fiber intake was related to the generation of feces in linear and nonlinear regression analyses.

Rose C., Parker A., Jefferson B, & Cartmell E. (2015). The Characterization of Feces and Urine: A Review of the Literature to Inform Advanced Treatment Technology. Critical Reviews in Environmental Science and Technology, 45(17), 1827-1879.

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Publication 2015

Quantitative Lipid Profiling by Nanospray Mass Spectrometry

A TSQ Quantum Ultra Plus triple-quadrupole mass spectrometer (Thermo Fisher Scientific, San Jose, CA) equipped with an automated nanospray apparatus (i.e., Nanomate HD, Advion Bioscience Ltd., Ithaca, NY) and Xcalibur system software were utilized in the study13 (link). Ionization voltages of -1.1, -0.95, and +1.2 kV and gas pressures of 0.3, 0.15, and 0.3 psi were employed on the nanomate apparatus for the analyses of anionic lipids, PE, and PC, respectively. The nanomate was controlled by Chipsoft 7.2.0 software. Each lipid extract solution prepared above was diluted to less than 50 pmol of total lipids/μl with CHCl₃/MeOH/isopropanol (1:2:4 by volume) prior to infusion to the mass spectrometer with the nanomate. This concentration was estimated based on the protein content and the total lipid content that is normalized to protein content from prior studies or previous experience. This procedure uses low concentrations of lipid to prevent lipid aggregation during analysis and to minimize any effects of residual inorganic components carried over during lipid extraction on ion suppression and/or chemical noise.
The first and third quadrupoles were used as independent mass analyzers with a mass resolution setting of 0.7 Thomson while the second quadrupole served as a collision cell for tandem mass spectrometry. Typically, a 1 to 2-min period of signal averaging in the profile mode was employed for each full MS scan. For tandem mass spectrometry, a collision gas pressure was set at 1.0 mTorr but the collision energy varied with the classes of lipids as indicated or described previously3 (link), 7 (link). For each tandem MS mass spectrum, a 2 to 5-min period of signal averaging in the profile mode was employed. All the full MS scans and tandem MS scans were automatically acquired by a customized sequence subroutine operated under Xcalibur software. Data from biological samples were normalized to the protein content and all data are presented as the mean ± SD of multiple samples from different animals.

Yang K., Cheng H., Gross R.W, & Han X. (2009). Automated Lipid Identification and Quantification by Multi-dimensional Mass Spectrometry-Based Shotgun Lipidomics. Analytical chemistry, 81(11), 4356-4368.

Publication 2009

Animals Biopharmaceuticals Cells Chloroform Isopropyl Alcohol Lipids Pressure Proteins Radionuclide Imaging Tandem Mass Spectrometry

Quantitative Neurometabolic Profiling of Cerebellum and Pons

Two 3T whole-body Siemens Tim Trio (Siemens Medical Solutions, Erlangen, Germany) scanners were used in this study; one located at the Center for Magnetic Resonance Research (CMRR) in Minnesota and the other one at the Institut du Cerveau et de la Moelle (ICM) in Paris. Age- and BMI-matched healthy subjects (Table 1) were enrolled after giving informed consent according to procedures approved by the Institutional Review Board at CMRR and by the local ethics committee at ICM. The standard body RF coil was used for radiofrequency transmission and the 32-channel phased-array Siemens head coil was used for signal reception. Soft pads were used to hold each subject’s head in place to minimize head movement in the MR system. T₁-weighted MPRAGE images (repetition time (T_R) = 2530ms, echo time (T_E) = 3.65 ms, flip angle = 7°, slice thickness = 1 mm, 224 slices, field-of-view = 256×176 mm², matrix size = 256×256) were acquired to position the volume-of-interest (VOI) for MRS measurements. B₀ shimming was achieved using an adiabatic version of FAST(EST)MAP (16 (link)), which is available as a work-in-progress (WIP) package on the Siemens system.
Proton spectra were acquired using a modified semi-LASER sequence (T_E = 28 ms, T_R = 5 s, 64 averages) (14 (link)) from two VOIs: cerebellar vermis (10×25×25 mm³) and pons (16×16×16 mm³). Voxel placement was based on anatomical landmarks. The fourth ventricle, cervical spinal cord and the brainstem were used to separate the cerebellum. The surfaces, lobes, lobules and fissures of the cerebellum were then used as landmarks in positioning the voxel in the vermis. For pons VOI placement, the midbrain, fourth ventricle and the medulla were used as landmarks.
The semi-LASER sequence (14 (link)) used in this study is a more compact version of the originally published semi-LASER sequence (17 (link)). Briefly, the sequence consisted of a 2 ms asymmetric slice-selective 90° pulse (18 (link)) followed by two pairs of slice selective adiabatic full passage (AFP) pulses (4 ms duration, HS4 modulation, R25) (19 ), which were interleaved, rather than applied sequentially, to improve suppression of unwanted coherences with shorter spoiler gradient pulses. Water suppression was achieved with VAPOR, which was interleaved with outer volume suppression (OVS) to suppress unwanted coherences (18 (link)). A substantially lower chemical shift displacement error is obtained with the semi-LASER sequence (3.6% /ppm for the slice-selective 90° pulse and 2% /ppm for the AFP pulses) compared to the standard PRESS sequence provided on the Siemens platform (12–13% /ppm).
B₁ levels required for localization pulses and for water suppression were adjusted for each voxel. Specifically, the RF power magnitude for the 90° asymmetric pulse was calibrated by monitoring the signal intensity whilst increasing the RF power and choosing the RF power setting that produced the maximum signal. The power for the AFP pulses was automatically set relative to the 90° pulse. A similar procedure was carried out for the water suppression calibration.
On the scanner, signals from individual coil elements were combined after correcting for phase shifts between elements and weighting them based on the coil sensitivities (20 (link)) to generate a free induction decay (FID). Each FID was then individually saved for shot-to-shot frequency and phase correction before averaging. Two non-suppressed water spectra were acquired: one for eddy current correction (the RF pulses of the VAPOR scheme were turned off) and one for use as reference for metabolite quantification (VAPOR and OVS schemes turned off in order to eliminate magnetization transfer effects). To evaluate the cerebrospinal fluid (CSF) contribution to each VOI, fully relaxed unsuppressed water signals were acquired at different T_E’s ranging from 28–4000 ms (T_R = 15 s) with the entire VAPOR and OVS scheme turned off (21 ).
All spectral processing was performed in Matlab by the same person prior to LCModel fitting. Eddy current correction was carried out first to correct for distorted line shapes and zero-order phase. Individual shots affected by subject motion (based on water suppression efficiency) were removed. Single-shot frequency correction was performed using a cross-correlation algorithm and phase correction was performed using a least-square fit algorithm. All steps were completely automated except for the removal of FIDs affected by motion. Finally the summed spectrum was referenced based on NAA resonance at 2.01 ppm.
Spectra were then analyzed with LCModel (22 (link)) with the water scaling option (version 6.3-0G). The model basis set was generated based on density matrix formalism as described before (23 (link)). The basis set also included macromolecule spectra, which were acquired using inversion-recovery technique in 4 healthy subjects (total averages = 928, T_R = 2.5 s, inversion time, T_I = 0.75 s, VOI = 15.6 mL, 5 ms duration HS5 inversion pulse, occipital cortex). Due to the shorter T₁ relaxation time of the methylene protons of tCr at 3.93 ppm relative to other metabolite protons (24 (link)), this resonance was present in the metabolite-nulled macromolecule spectra and was removed using a Hankel singular value decomposition (HSVD) algorithm in Matlab. A 12.5 Hz Gaussian line broadening was also applied to the macromolecule spectra after incorporating a reference peak at 0 ppm (see supplementary material). No baseline correction, zero-filling or apodization functions were applied to the in vivo data prior to the analysis. LCModel fitting (supplementary material) was performed over the spectral range from 0.5–4.2 ppm.
Metabolite concentrations were determined after correcting for tissue water content and CSF contributions in the selected VOI using the water-scaling option in LCModel. The transverse relaxation times (T₂) of tissue water and % CSF contribution to the VOI were obtained by fitting the integrals of the unsuppressed water spectra acquired in each VOI at different T_E values with a bi-exponential fit (21 ), with the T₂ of CSF fixed at 740 ms based on measurement of T₂ of water in a small voxel located in ventricles with the same semi-LASER sequence (4 healthy subjects, T_R = 15 s, VOI = 0.125–0.360 mL, twelve T_E values ranging from 28–4000 ms), and three free parameters: T₂ of tissue water, amplitude of tissue water, and amplitude of CSF water.
In order to obtain accurate metabolite concentrations, corrections must be made for T₂ relaxation of both water and metabolites. In the case of semi-LASER, T₂ relaxation is slowed due to the Carr-Purcell (CP) conditions, and T₂ values under CP conditions must be used for quantification. For water, these values can be estimated by correcting the free precession T₂ value measured for the tissue water signal at different echo-times by a fixed factor to account for CP effects. A previous study compared water T₂ values measured with LASER and CP-LASER sequences at 4T and 7T (25 (link)). Extrapolating from that study, we assumed that the T₂ of water under CP conditions is 1.5× longer than the measured free precession T₂ at 3T. Signal loss due to T₂ relaxation of metabolites was neglected since the apparent T₂ is sequence-dependent. This assumption is justified by the fact that metabolites have longer T₂ such that correction factors would be small at T_E = 28 ms. Nonetheless this choice will result in somewhat underestimated metabolite concentrations relative to the true concentrations in tissue. A water content of 82% and 72% was used for vermis and pons, respectively (26 ,27 ).
Metabolites that were quantified with Cramér-Rao lower bounds (CRLB) ≤ 50% from at least half of the spectra from a particular brain region were included in the neurochemical profile. In addition, if the correlation between two metabolites was very high (i.e. correlation coefficient r more negative than −0.7) in the majority of the spectra from a region, then only their sum was reported, e.g. tCr (creatine + phosphocreatine) and tCho (glycerophosphorylcholine + phosphorylcholine). If there was indication for pairwise correlation with r from −0.5 to −0.7, then the concentration sum of the pair was reported in addition to the individual metabolites’ concentrations, e.g. NAA, NAAG and total NAA (tNAA, NAA + NAAG), as recommended by the LCModel manual, Jan 2013 (22 (link)). Moreover, spectra with the associated water reference linewidth greater than 10 Hz were excluded due to trends observed in overestimating aspartate and ascorbate and underestimating glutamate in these spectra. Water linewidths > 10 Hz only occurred for spectra acquired from the pons region.

Deelchand D.K., Adanyeguh I.M., Emir U.E., Nguyen T.M., Valabregue R., Henry P.G., Mochel F, & Öz G. (2014). Two-site reproducibility of cerebellar and brainstem neurochemical profiles with short-echo, single voxel MRS at 3 T. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 73(5), 1718-1725.

Publication 2014

Reconstruction of Metabolic Network for E. coli

The reconstruction process has also been previously outlined (Feist et al, 2006 (link); Reed et al, 2006a (link)). Here, we provide certain details specific to this work. Starting from the metabolic network for iJR904 (Reed et al, 2003 (link)), additional reactions were added to the network based on E. coli-specific biochemical characterization studies (see Supplementary information for a complete list of references) and other reactions were removed (see Results). This process was aided by comparing the content of iJR904 with the EcoCyc database (see below). The E. coli genome annotation (Riley et al, 2006 (link)) was used as a citation source for biochemical characterization studies and a framework upon which translated metabolic proteins, and subsequently reactions, were assigned to form gene to protein to reaction (GPR) assignments. The SimPheny™ (Genomatica Inc., San Diego, CA) software platform was used to build the reconstruction. For each reaction entered into the reconstruction, the involved metabolites were characterized according to their chemical formula and charge determined using their pK_a value for a pH of 7.2. Metabolite charge was determined using its pK_a value(s). When the metabolite pK_a was not available, charge was determined using the pK_a of ionizable groups present in a metabolite (http://www.chemaxon.com/product/pka.html). All of the reactions entered into the network were designated as enzymatically catalyzed reactions or spontaneous reactions, were both elementally and charged balanced and are either reversible or irreversible. Reversibility was determined first from primary literature for each particular enzyme/reaction, if available (see Supplementary information for references). Additionally, general heuristic rules, like those applied by Kümmel et al (2006b) , were used to enter reversibility using knowledge about the physiological direction of a reaction in a pathway (sometimes including regulatory knowledge) and/or basic thermodynamic information (such as reactions hydrolyzing high-energy phosphate bonds are almost always irreversible). Furthermore, a thermodynamic analysis of reversibility was utilized to assign the directionality of some reactions (see above).

Feist A.M., Henry C.S., Reed J.L., Krummenacker M., Joyce A.R., Karp P.D., Broadbelt L.J., Hatzimanikatis V, & Palsson B.Ø. (2007). A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Molecular Systems Biology, 3, 121.

Publication 2007

Enzymes Escherichia coli Gene Products, Protein Genome Metabolic Networks Phosphates physiology Proteins Reconstructive Surgical Procedures

Analysis of Macromolecular Interactions using SEDPHAT

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Publication 2014

Most recents protocols related to «Chemical content»

Chemical Content Measurement Protocol

Not available on PMC !

Measurement of chemical content was carried out to determine the chemical content due to the influence of treatment materials and storage time. Measurements were carried out using the Lactostar (c) 2008 Funke Gerber, chemical content which includes; fat, solid non-fat, protein, and lactose.

Saleh A.S., & Wibisono Y. (2024). The Influence of Extremely Low Frequency (ELF) Waves for Pasteurization of Cow Milk. Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), 13(2), 361-361.

Publication 2024

Chemical Content Quantification Protocol

To determine the chemical content of the formulations, samples of 1 cm in diameter were cut and dissolved in 1 mL of ethanol, later transferred to a volumetric flask of 25 mL, and the solution was read by UV–Vis spectrophotometry (Cary 100 Varian, Santa Clara, CA, USA) at a wavelength of 243 nm [17 (link)].

Guadarrama-Escobar O.R., Valdés-Alvarez C.A., Constantino-Gonzalez K.S., Serrano-Castañeda P., Peña-Juárez M.C., Morales-Florido M.I., Salgado-Machuca M., Rodríguez-Pérez B., Rodriguez-Cruz I.M., Vargas-Estrada D., Mercado-Márquez C., Vázquez-Durán A., Méndez-Albores A., Anguíano-Almazán E, & Escobar-Chavez J.J. (2024). Design and Characterization of Ocular Inserts Loaded with Dexamethasone for the Treatment of Inflammatory Ophthalmic Disease. Pharmaceutics, 16(2), 294.

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Publication 2024

Seasonal Soil Substrate Analysis

Not available on PMC !

The seasonal water dynamics in the substrate of all treatments was controlled using a data logger and sensors for the measurement of the volumetric water content (VWC) in the soil (Spectrum Technologies, Inc., Aurora, CO, USA). In each treatment, there were four sensors installed at a depth of 5-20 cm. Sampling for the substrate chemical trait determination was carried out in mid-September 2021 (six months after the P fertilization). In each box (treatment), one composite sample was collected, formed with nine subsamples collected in a diagonal (X) arrangement. The samples of each treatment were subjected to soluble component extraction by mixing with deionized water at a 1:2 ratio. The material was mixed for 1 h using a rotating mixer. Then, the suspension was filtered through filter paper, and the clear filtrate was analyzed to determine the pH reaction, total nitrogen (N total), nitrate (NO 3 -), ammonium (NH 4 + ), phosphorus (PO 4 3-), potassium (K + ), calcium (Ca 2+ ), magnesium (Mg 2+ ), chloride (Cl -) and sodium (Na + ) ions and salt, as well as electrical conductivity (E.C.), in the substrate of each treatment separately. The chemical analysis methodology applied is described in more detail in Page et al. [74] .

Vukmirović A., Škvorc Ž., Bogdan S., Krstonošić D., Bogdan I.K., Karažija T., Bačurin M., Brener M., & Sever K. (2024). Effects of Drought, Phosphorus Fertilization and Provenance on the Growth of Common Beech and Sessile Oak. Forests, 15(1), 219-219.

Publication 2024

Cyanobacterial Chemical Composition Analysis

To obtain the chemical composition of the cyanobacterium, the amounts of total nitrogen (N), nitrite (NO₂⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), sulfate (SO₄²⁻), phosphate (PO₄³⁻), carbonate (CO₃²⁻), magnesium (Mg²⁺), calcium (Ca²⁺), sodium (Na⁺), and potassium (K⁺) ions were determined at the Institute of Arian Fan Azma, Iran, following the methods summarized in Table 2. Each test was carried out in three replications, and the averages of the obtained amounts were provided.

Table 2

Chemical content of cyanobacteria

	Analytical Method	Nostoc spongiaeforme ISB65
Total N (mg.L^− 1)	Macro kjeldahl	35.00
NO₂⁻ (mg.L^− 1)	Colorimetric	0.60
NO₃⁻ (mg.L^− 1)	Ultraviolet Spectrophotometric	9.40
NH₄⁺ (mg.L^− 1)	Nesslerization	5.00
Phosphate (mg.L^− 1)	Vanadomolybdophosphoric acid colorimetric	9.00
CO₃²⁻ (mg.L^− 1)	Titrimetric	0.00
HCO₃⁻ (mg.L^− 1)	Titrimetric	30.00
Na⁺ (mg.L^− 1)	Flame Emission Photometric	1.50
Mg²⁺ (mg.L^− 1)	EDTA Titrimetric	5.00
Ca²⁺ (mg.L^− 1)	EDTA Titrimetric	7.00
K⁺ (mg.L^− 1)	Flame Emission Photometric	0.11
EC (µS cm^− 1)	Platinum Electrode	15.00
pH	Electrometric	6.02

Shariatmadari Z., Zarezadeh S., Riahi H., Ghotbi-Ravandi A.A., Seyed Hashtroudi M, & Shahroudi E. (2024). Cyanobacterial elicitor enhances the biomass of Mentha piperita L. and improves the production of high-value rosmarinic acid under in vitro culture of apical meristem. BMC Plant Biology, 24, 190.

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Publication 2024

Leaf Elemental Composition Analysis

We obtained cross sections of leaves from the choice experiments using a razor blade, which was cleaned with ethanol before and after each use. Samples were fixed on an aluminum SEM mount covered with conductive carbon adhesive tape. The elemental composition of the samples was analyzed using a scanning electron microscope (SEM) (JSM-IT700HR) with an energy-dispersive X-ray spectrometer (EDX) (JED-2300 Analysis Station Plus, JEOL, Tokyo, Japan) at a low vacuum (30 Pa), 15 kV accelerating voltage, and 500× magnification. We measured three points within the epidermis of both the adaxial and abaxial surfaces and four points within the mesophyll. The SEM–EDX analysis was conducted on the same day as the toughness measurements.

Gao S, & Tuda M. (2024). Silica and Selenium Nanoparticles Attract or Repel Scale Insects by Altering Physicochemical Leaf Traits. Plants, 13(7), 952.

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Publication 2024

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D8 advance by Bruker

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The D8 Advance is a versatile X-ray diffractometer (XRD) designed for phase identification, quantitative analysis, and structural characterization of a wide range of materials. It features advanced optics and a high-performance detector to provide accurate and reliable results.

What is Chemical content and why is it important?

Chemical content refers to the composition and properties of chemical substances, including their molecular structure, physical characteristics, and reactivity. This term encompasses the study of organic and inorganic compounds, as well as the analysis and identification of chemical components in various materials and samples. Chemical content is a fundamental aspect of chemistry, biology, and other scientific disciplines, and is essential for understanding the behavior and interactions of matter at the molecular level. Research in this area can lead to advancements in areas such as drug development, material science, and environmental protection.

What are some common challenges in working with Chemical content?

Some common challenges in working with Chemical content include ensuring accuracy and reproducibility of experimental methods, identifying the most effective protocols from the literature, and comparing the performance of different chemicals or materials. Researchers often need to sift through a vast amount of information to find the most reliable and relevant protocols, which can be time-consuming and prone to errors.

How can PubCompare.ai help with Chemical content research?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help researchers identify the most effective protocols related to Chemical content for their specific research goals. PubCompare.ai's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for reproducibility and accuracy. This can save time and improve the quality of Chemical content research.

What are some different types or variations of Chemical content?

Chemical content can encompass a wide range of substances, including organic compounds (such as drugs, dyes, and fuels), inorganic compounds (such as minerals, salts, and gases), and a variety of nanomaterials and composites. The specific type or variation of Chemical content being studied will depend on the research question and the intended application, such as in the fields of material science, environmental chemistry, or pharmaceutical development.

How can PubCompare.ai help researchers compare different Chemical content protocols?

PubCompare.ai's AI-driven analysis can help researchers compare the efectiveness of different Chemical content protocols found in the literature, pre-prints, and patents. The platform can highlight key differences in factors such as yield, purity, and reproducibility, allowing researchers to identify the most reliable and effective protocols for their specific needs. This can save time and improve the quality of Chemical content research by ensuring that researchers are using the most optimal protocols for their experiments.

More about "Chemical content"

Chemical composition and properties are fundamental aspects of chemistry, biology, and other scientific disciplines.
This encompasses the study of organic and inorganic compounds, as well as the analysis and identification of chemical components in various materials and samples.
Key areas within chemical content include molecular structure, physical characteristics, and reactivity.
Researching chemical content can lead to advancements in diverse fields such as drug development, material science, and environmental protection.
Common chemical substances relevant to this topic include sodium hydroxide, triglycerides, thiobarbituric acid reactive substances (TBARS), methanol, gallic acid, hydrochloric acid, bovine serum albumin, and various chemical assay kits.
Techniques for analyzing chemical content may involve spectroscopic methods, chromatography, colorimetric assays, and other analytical approaches.
Accurate and reproducible chemical research is enabled by tools that help identify the most reliable experimental protocols from literature, preprints, and patents.
By understanding the fundamentals of chemical composition and properties, researchers can unlock new insights and innovations at the molecular level, driving progress across the sciences.