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Methane

Methane is a simple hydrocarbon gas that is the primary component of natural gas.
It is a potent greenhouse gas, contributing significantly to global climate change.
Methane is produced through the anaerobic decomposition of organic matter, such as in wetlands, rice paddies, and the digestive tracts of ruminant animals.
It is also released during fossil fuel extraction and processing.
Understanding methane sources, sinks, and atmospheric concentrations is crucial for mitigating its environmental impact.
Researchers can leverage PubCompare.ai's AI-driven platform to optimize their methane research, locating the best protocols from literature, preprints, and patents while enhancing reproducibility and accuracy.
This powerful tool can streamline the research process and unlock new insights into this important greenhouse gas.

Most cited protocols related to «Methane»

1
Enrichment and Isolation of Aerobic Methanotrophs
Cited 27 times
Enrichment cultures with both microbial mats and lake water (20 ml) collected from Movile Cave (April 2010) were set up in 120 ml serum vials. In order to reproduce the atmosphere found in the air bells, the headspace of the serum vials was flushed with O2-free nitrogen and amended with 7% O2 and 2.5% CO2. Methane (10%) was introduced to the serum vial headspace as carbon source for enrichment of aerobic MOB. Following 2 weeks of enrichment, 50 μl aliquots, including a dilution series of 1:10 and 1:100 of the culture were spread onto Dilute Basal Salts (DBS) agar plates [12 ]. The plates were incubated in airtight plastic boxes with 10% CH4 in the atmosphere and monitored for colony formation. Selected colonies were streaked onto fresh DBS agar plates and sub-culturing was performed until the cultures were deemed to be pure. Purity was determined by phase contrast microscopy (×1000) and lack of growth on R2A agar (Oxoid) plates were used to confirm the purity of the culture. Initial identification of the MOB isolate was determined by sequencing the 16S rRNA gene as described in [9 (link)].
Kumaresan D., Stephenson J., Doxey A.C., Bandukwala H., Brooks E., Hillebrand-Voiculescu A., Whiteley A.S, & Murrell J.C. (2018). Aerobic proteobacterial methylotrophs in Movile Cave: genomic and metagenomic analyses. Microbiome, 6, 1.
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Publication 2018
Agar Atmosphere Bacteria, Aerobic Carbon Genes Methane Microscopy, Phase-Contrast Nitrogen Ribosomal RNA Genes RNA, Ribosomal, 16S Salts Serum Technique, Dilution
2
Metagenomic Profiling with Ribosomal and McrA Markers
Cited 19 times
A 16S rRNA gpkg was created from the 2013/08 public release of the Greengenes database (33 (link)). GraftM create was run using these sequences and the taxonomy-decorated phylogenetic tree for the 97% nucleotide identity representative OTU set (ftp://greengenes.microbio.me/greengenes_release/gg_13_8_otus). Ribosomal protein gpkgs. Gpkgs were created for ribosomal proteins by starting with the set of HMMs included with PhyloSift (20 (link)). These HMMs were used to search with HMMER, using an E-value cutoff of 1e–40, against the set of finished and permanent draft proteomes from the IMG (34 (link)) that were >90% complete and <5% contaminated according to CheckM v1.0.5 (35 (link)). To prevent contaminated genomes introducing error into the taxonomic annotations, only those genomes where a single hit was found were utilized. To limit the effect of taxonomic bias toward lineages with a greater number of sequenced genomes, only a single protein from each species (one representative per species, using a type strain where possible and including all those without species level taxonomic classification) were used. Proteomes were searched using GraftM graft using default parameters, after which 15 ribosomal markers were determined to be single copy on the basis of their being detected as having a single hit in >5900 of the 6215 genomes. GraftM packages for the 15 protein-coding genes were generated with GraftM create using those sequences found in single copy, a previously generated HMM and the corresponding IMG taxonomy for each genome. Functional and taxonomic McrA gpkgs. Two gpkgs were constructed for the alpha subunit of the methyl coenzyme M reductase (mcra) gene. Amino acid sequences for the McrA protein family and paralogous MrtA sequences were sourced from IMG (February 2014) using the BLASTP tool provided online. Spurious hit sequences were removed by manual inspection. Genes for the Bathyarchaeotal (36 (link)) and Vertrataearchaeotal (37 (link)) orthologues were sourced from NCBI. The first taxonomy-annotated gpkg was created using the default GraftM create pipeline using the sequences and their associated genome taxonomy. The second was created by re-decorating the McrA tree with functional, rather than taxonomic information. This second tree was annotated according to their substrate utilization: acetoclastic (from acetate) comprised of the order Methanosarcinales; hydrogenotrophic (from hydrogen, carbon dioxide and/or formate), comprised of the Methanomicrobiales, Methanocellales, Methanococcales and Methanobacteriales; methylotrophic (from methylated compounds) comprised of the Methanomassiliicoccales, Methanofastidiales and Vertrataearchaeota. Lineages within the Bathyarchaeota were recently found to encode mcra, though their metabolism is not yet confirmed. These sequences were included in the gpkg, but left unannotated. The McrA tree was curated with these functional groupings using ARB (38 (link)), with the exception of the Methanosarcina which are thought to be capable of producing methane from all three substrate groups (39 (link)). The Methanosarcinaceae were annotated as a clade separate to the exclusively acetoclastic Methanosaetaceae.
Boyd J.A., Woodcroft B.J, & Tyson G.W. (2018). GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Research, 46(10), e59.
Publication 2018
Acetate Amino Acid Sequence Carbon dioxide formate Gene Products, Protein Genes Genome Glycoprotein Hormones, alpha Subunit Grafts Hydrogen Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Metabolism Methane Methanobacteriales Methanococcales Methanomicrobiales Methanosaetaceae Methanosarcina Methanosarcinaceae Methanosarcinales methyl coenzyme M reductase Nucleotides Oxidoreductase Proteins Protein Subunits Proteome Ribosomal Proteins Ribosomes RNA, Ribosomal, 16S Staphylococcal Protein A Strains Trees
3
Quantification of Phytohormones in Grains
Cited 11 times
The levels of ZR, IAA, and ABA were determined by Zoonbio Biotechnology Co., Ltd, and the methods were modified from those described by Pan et al. (2002) . Approximately 0.5 g dehulled grains were ground in a pre-cooled mortar that contained 5 mL extraction buffer composed of isopropanol/hydrochloric acid. The extract was shaken at 4°C for 30 min. Then, 10 mL dichloromethane was added, and the sample was shaken at 4°C for 30 min and centrifuged at 13,000 rpm for 5 min at the same temperature. We then extracted the lower, organic phase. The organic phase was dried under N2 and dissolved in 150 μL methanol (0.1% methane acid) and filtered with a 0.22-μm filter membrane. The purified product was then subjected to high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis. HPLC analysis was performed using a ZORBAX SB-C18 (Agilent Technologies) column (2.1 mm × 150 mm; 3.5 mm). The mobile phase A solvents consisted of methanol/0.1% methanoic acid, and the mobile phase B solvents consisted of ultrapure water/0.1% methanoic acid. The injection volume was 2 μL. MS conditions were as follows: the spray voltage was 4500 V; the pressure of the air curtain, nebulizer, and aux gas were 15, 65, and 70 psi, respectively; and the atomizing temperature was 400°C.
You C., Zhu H., Xu B., Huang W., Wang S., Ding Y., Liu Z., Li G., Chen L., Ding C, & Tang S. (2016). Effect of Removing Superior Spikelets on Grain Filling of Inferior Spikelets in Rice. Frontiers in Plant Science, 7, 1161.
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Publication 2016
Acids Air Pressure Buffers Cereals formic acid High-Performance Liquid Chromatographies Hydrochloric acid Isopropyl Alcohol Methane Methanol Methylene Chloride Nebulizers Solvents Tandem Mass Spectrometry Tissue, Membrane
4
Methane Yield Selection in Sheep
Cited 10 times
Animals in the dataset were from 7 New Zealand Sheep Improvement Limited (SIL) flocks including 1 AgResearch flock (SIL flock number 2638), 3 Central Progeny Test (CPT) flocks (SIL flock numbers 4640, 4757, 9153), 2 industry flocks (SIL flock numbers 2629, 4474), and the “methane yield selection” flock (SIL flock number 3633). In total, 3601 lambs were measured at least once using PAC or RC of which 2,255 lambs (born between 2012 and 2015; at 7 ± 3 [±StDev; range 4 to 13] mo of age) had 4,733 separate 1-h measurement records using PAC and 2,110 lambs (born in 2007 and between 2009 and 2015; at 9 ± 2 [range 6 to 12] mo of age) had 8,655 daily records using RC. Of these, 844 animals had both PAC and RC measurements as lambs. In addition, 1,251 (2,387 records) animals were measured in PAC as yearling/ewes (called ewe in this paper from this point onwards; 42 ± 19 [range 16 to 88] mo of age) of which 874 animals also had RC measurements as lambs and 698 had PAC measurements as lambs. Flock, birth year, sex, and measurement year and season are detailed in Supplementary Table S1.
“Methane yield selection” flock animals (Pinares-Patiño et al., 2013 ) were progeny of maternal dual-purpose sires generated by the New Zealand industry CPT program (McLean et al., 2006 ), comprising Coopworth, Romney, Perendale, Texel, and composite breeds, where the latter breed consisted primarily of combinations of the former breeds with additional infusions of Finn and East Friesian. All rams were mated to composite ewes. Ewe progeny born in 2007 and between 2009 and 2011 (and also ram lambs born in 2009) was measured for CH4 emissions per unit of feed DMI (i.e., CH4 yield) (Pinares-Patiño et al., 2013 ), with the most extreme low and high 10% retained for further breeding. The lines were closed in 2012 and all sires used from 2012 onwards were born in the high and low CH4-yield selection flocks, which are currently maintained at 100 ewes per line. Progeny from the CH4-yield selection flock included in the dataset was born between 2010 and 2015.
Jonker A., Hickey S.M., Rowe S.J., Janssen P.H., Shackell G.H., Elmes S., Bain W.E., Wing J., Greer G.J., Bryson B., MacLean S., Dodds K.G., Pinares-Patiño C.S., Young E.A., Knowler K., Pickering N.K, & McEwan J.C. (2018). Genetic parameters of methane emissions determined using portable accumulation chambers in lambs and ewes grazing pasture and genetic correlations with emissions determined in respiration chambers. Journal of Animal Science, 96(8), 3031-3042.
Publication 2018
Animals Birth Childbirth Domestic Sheep Methane Mothers
5
Rumen Microbiome Analysis in Cattle
Cited 10 times
Rumen samples were obtained from the animals when they were alive (n = 50) and after slaughter (n = 68). Rumen samples were taken from live animals within 2 hours of leaving the respiration chambers. Approximately 50 mL rumen contents were taken by inserting a stomach tube (16 × 2700 mm Equivet Stomach Tube, JørgenKruuse A/S, Langeskov, Denmark) nasally and aspirating manually. Between 3 to 17 days after leaving the respiration chamber the animals were slaughtered in a commercial abattoir where two rumen fluid samples (approximately 50 mL) were taken immediately after the rumen was opened to be drained. The slaughter process results in well mixed samples of rumen contents. DNA was extracted from the rumen samples and subjected to qPCR for the 16S rRNA genes as described in [14 (link)] to determine the abundance of archaea and bacteria and their ratio.
Eight extreme animals (4 high and 4 low) for methane emissions, balanced for breed type and diet, were used in a metagenomic study, in which deep sequencing was applied. Illumina TruSeq libraries were prepared from genomic DNA and sequenced on an Illumina HiSeq 2500 instrument by Edinburgh Genomics. Paired-end reads (2 × 100 bp) were generated, resulting in between 8.6 and 14.5 GB per sample (between 43.4 and 72.7 million paired reads). The genomic reads were aligned to the KEGG genes database. Parameters were adjusted such that all hits were reported that were equal in quality to the best hit for each genomic read. The read and best hits have to be more than 90% identical and have to be belonging to a single KEGG orthologue group to be kept in the data. If the best hits are spread over more than one KEGG orthologue group, the read were disregarded. Read counts for KEGG orthologues were summed and normalised to the total number of hits.
Roehe R., Dewhurst R.J., Duthie C.A., Rooke J.A., McKain N., Ross D.W., Hyslop J.J., Waterhouse A., Freeman T.C., Watson M, & Wallace R.J. (2016). Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genetics, 12(2), e1005846.
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Publication 2016
Animals Archaea Bacteria Cell Respiration Genes Genome Metagenome Methane Ribosomal RNA Genes RNA, Ribosomal, 16S Rumen Stomach Therapy, Diet

Most recents protocols related to «Methane»

1
Quantifying Greenhouse Gas Emissions from Pig Operations
Gas emission was estimated according
to eq 1, where E is the emission rate (g h–1), M is the molar mass (g mol–1), Cout is the concentration (atm) measured in the
air outlet from the sections, Cin is the
concentration (atm) measured in the air inlet for the sections, Q is the ventilation rate (m3 h–1), R is the gas constant (m3·atm·K–1·mol–1), and T is the temperature (K).
Odor was assessed as
the odorant concentration and estimated as the sum of odor activity
values (SOAV) for hydrogen sulfide and the eight VOCs according to eq 2 in which SOAV is calculated
as the concentration measured by PTR-MS divided by the odor threshold
value (OTV, units of ppbv) for each of the nine odorants.
Odor
emission was estimated according to eq 3, where Eodor is
the emission (SOAV s–1), SOAV is the sum of odor
activity values expressed per m3 (SOAV m–3), and Q is the ventilation rate (m3 h–1).
Enteric
methane emission was calculated on a daily basis according
to eq 4,10 where ECH4 enteric (g
pig–1 d–1) is the enteric methane
emission, GE is the gross energy consumption (MJ d–1 pig–1), Ym is the
fraction of gross energy intake being converted to methane (%), n is the number of pigs in the section, and 0.005565 is
the energy content of methane (MJ g–1).
Ym was set to 0.24% based on an average
of four studies.19 (link)−22 (link) Slurry methane emission was estimated by subtracting enteric methane
emission from eq 4 from
the measured total methane emission.
Enteric carbon dioxide
emission, ECO2 enteric (g pig–1 d–1), was calculated
using the empirical relationship in eq 5,23 (link) where BW is the pig
body weight (kg). The constants in eq 5 were derived from fitting to multiple datasets.23 (link)
The average daily body
weight of pigs was calculated by linear
interpolation between in and outgoing weights of the pigs. Linear
growth is a realistic assumption for pigs that are between 100 and
200 days old (as in this study).24 (link)
Dalby F.R., Hansen M.J., Guldberg L.B., Hafner S.D, & Feilberg A. (2023). Simple Management Changes Drastically Reduce Pig House Methane Emission in Combined Experimental and Modeling Study. Environmental Science & Technology, 57(9), 3990-4002.
Publication 2023
Body Weight Carbon Hydrogen Sulfide Methane Molar Odorants Odors Pigs Respiratory Rate
2
Anaerobic Biodegradation Model for Methane Emission
The anaerobic
biodegradation model (ABM)1 (link) was used to
model methane emission from the slurry
present in each of the pig house sections and for extrapolation of
emissions from the slurry in outside storage. The ABM predicts organic
matter transformation to methane and carbon dioxide by simulating
(i) initial disintegration, hydrolysis, and fermentation of degradable
volatile solids (VSd) to VFA through a single first-order reaction
and (ii) methanogenesis using Monod kinetics for describing VFA conversion
by active methanogens, resulting in the production of methane and
carbon dioxide. The ABM explicitly simulates development of a methanogen
community and by default includes five methanogen populations,1 (link) which are active in different temperature ranges.
These default settings were initially chosen based on fitting to methane
productivity at varying temperatures as reported by Elsgaard et al.25 (link) Here, the numbers of methanogen groups were
reduced to three (m0, m1, and m2) to decrease computation time and
complexity during parameter estimation. In light of recent studies
on methanogen activity at low temperatures,3 (link),26 (link) VFA
substrate conversion rates (qmax,opt)
at low temperatures were reduced taking into consideration measured
methane potential curves3 (link) and implementing
of a new methanogen group (m0) (Supporting Information, Table S2 and Figure S1).
Enrichment of
VSd in the residual slurry remaining after slurry removal (from pig
houses as well as from outside storages) was implemented in a similar
fashion as for methanogens, which has previously been described.1 (link) Washing of the pig sections between batches of
pigs was simulated by the initial removal of slurry, leaving only
the residual mass, which was then diluted with water (70 kg pig–1), and finally removal of diluted slurry to the slurry
level before washing (but after the initial removal of slurry). This
simulation would have the net effect of reducing the amount of VSd
and methanogens present in the pit before the next batch of pigs enters
the section. Substrate (VFA) inhibition was implemented in the ABM
using a modified version of the model published by Zhang et al.,27 (link) see the Supporting Information, Text S1.
Dalby F.R., Hansen M.J., Guldberg L.B., Hafner S.D, & Feilberg A. (2023). Simple Management Changes Drastically Reduce Pig House Methane Emission in Combined Experimental and Modeling Study. Environmental Science & Technology, 57(9), 3990-4002.
Publication 2023
Carbon dioxide Cold Temperature Fermentation Hydrolysis Kinetics Light Methane Methanobacteria Pigs Psychological Inhibition
3
Optimizing Anaerobic Digestion Model Parameters
As described
previously,1 (link) there is significant uncertainty
(and probably high variability between locations) in the values of
ABM parameters related to hydrolysis and microbial kinetics. New estimates
were developed here using only measurements from section C. For parameter
estimation, slurry production (inferred from slurry heights during
batches) and slurry temperatures were fixed to measured values from
section C. The composition of the produced slurry was calculated based
on the fresh feces and urine composition, assuming that urine and
feces were excreted in a ratio of 3:1 w/w %.28 (link) Degradable VS was calculated as 70% of VS,29 (link),30 (link) and a factor of 1.54 gCOD gVSd–1 was used for
conversion to COD.1 (link) The ratio between qmax,opt for the three methanogen populations
(m0:m1:m2) was fixed at 1:2.4:3.73 during optimization. Optimization
was performed with a quasi-Newton method31 (link) using the optim() function in base R (stats package, v4.2.1) (R
core team, 2022) and specifying method argument as “L-BFGS-B”
with parameter boundaries. Optimization minimized the absolute difference
between measured and ABM-calculated daily methane emission rates (g
d–1) and concentrations of VFA (gCOD kg–1slurry). The methane emission rate and VFA concentration
were equally weighted by centering and scaling to a mean of 0 and
standard deviation of 1 in measurements. Period 4 was excluded from
the optimization and validation due to uncertainty in the washing
procedure and a 1 month delay in delivery of piglets for the fourth
batch period.
The optimized parameter values were used for validation
of the model. Sections WF, SF, and ST during periods 1–3 were
used as validation datasets. Slurry temperature was not measured in
sections SF and ST, and instead the slurry temperature from section
WF was used as the model input. Temperature data from section WF rather
than from section C was used because the retained slurry mass in section
WF better represented slurry masses in sections ST and SF. Hence,
slurry temperatures were expected to respond similarly to heat transfer
from the air and surroundings. The slurry production rate in ST was
set to the rate in the control section since slurry production measurements
were systematically underestimated in the ST section.
Dalby F.R., Hansen M.J., Guldberg L.B., Hafner S.D, & Feilberg A. (2023). Simple Management Changes Drastically Reduce Pig House Methane Emission in Combined Experimental and Modeling Study. Environmental Science & Technology, 57(9), 3990-4002.
Publication 2023
A-factor (Streptomyces) Feces Hydrolysis Kinetics Methane Methanobacteria Obstetric Delivery Population Group Urine
4
Monitoring Gaseous Emissions from Pig Facilities
Heated and insulated sample tubes
of PTFE (outer diameter: 8 mm, inner diameter: 6 mm, Mikrolab A/S,
Aarhus Denmark) for the venting outlet in each section and the common
fresh air supply were flushed continuously (ca. 5 L min–1) by a pump with a PTFE membrane (Capex L2, Charles Austen Pumps Ltd.,
Byfleet, UK) placed in an insulated room next to the sections. The
concentrations of methane, carbon dioxide, and ammonia was measured
by cavity-ring down spectroscopy (CRDS) using G2201-i, G4301, and
G2103 analyzer models (Picarro Inc., Santa Clara, CA, USA). The VOCs
and hydrogen sulfide were measured by proton-transfer reaction mass
spectrometry (HS-PTR-MS, Ionicon Analytik, Innsbruck, Austria) during
periods 1 and 4. The CRDS analyzers were connected to the outlet from
the Teflon pump using a 10-way PEEK valve (VICI, Houston, TX, USA)
and PTR-MS with a five-way PEEK valve (Bio-Chem Valve Incorporated,
Boonton, NJ). Measurements were performed in a continuous cycle with
two measurements per hour for each outlet for methane, carbon dioxide,
and ammonia and one measurement per hour for VOCs and hydrogen sulfide.
The VOCs measured were methanethiol, trimethylamine, acetic acid,
propanoic acid, butanoic acid, pentanoic acid, 4-methylphenol, and
skatole. These VOCs together with hydrogen sulfide were chosen as
they are found in high concentrations in air from pig sections and/or
have low odor threshold values.15 (link)−17 (link) The PTR-MS was operated with
standard drift tube conditions: a voltage of 600 V, a pressure between
2.1 and 2.2 mbar, and a temperature of 75 °C. The inlet temperature
was 75 °C. The rate constants used were based on previously reported
values,15 (link),18 (link) and the hydrogen sulfide concentration was
corrected for humidity dependence.15 (link)Temperature, relative humidity, airflow rate in each section, and
the temperature outside were recorded every minute by a log system
(VengSystem A/S, Roslev, Denmark). Calibrated measuring fans were
used to estimate the airflow rate (Reventa, Horstmar, Germany). In-house
air temperature was measured 1.7 m above the floor over the pen partitioning
and ca. 1/3 from the back end of the section using a calibrated temperature
sensor of the ventilation control. Slurry temperature (PT100, Campell
Scientific, Logan, UT, USA) was measured in sections C and WF in the
bottom of the slurry pits.
Dalby F.R., Hansen M.J., Guldberg L.B., Hafner S.D, & Feilberg A. (2023). Simple Management Changes Drastically Reduce Pig House Methane Emission in Combined Experimental and Modeling Study. Environmental Science & Technology, 57(9), 3990-4002.
Publication 2023
Acetic Acid Ammonia Butyric Acid Capex Carbon dioxide Dental Caries Humidity Hydrogen Sulfide Methane methylmercaptan Odors para-cresol polyetheretherketone Polytetrafluoroethylene Pressure propionic acid Protons Spectrum Analysis Teflon Tissue, Membrane trimethylamine valeric acid Van der Woude syndrome
5
Modeling Manure Removal Strategies' Impact on Methane Emissions
The relative effect of the different manure removal
strategies was modeled in scenarios with significantly higher and
lower base methane emission. Multiple barn and outside storage simulations
were run by applying the optimized ABM parameter set and varying the
hydrolysis rate (αopt) and substrate conversion rate
of methanogens (qmax,opt) to force various
levels of base methane emission levels. The two parameter values spanned
from 20 to 500% of the optimized parameter values, covering reported
methane emission levels in pig houses (Supporting Information, Table S3). Input variables and parameters for
barn simulations were similar to those described for model validation
(but with changes in αopt and qmax,opt). When calculating averaged methane emission on a yearly
basis, predicted emissions between the batch periods (7 days) were
included. The slurry mass effluents and slurry effluent concentrations
of degradable VS and methanogens from the barn simulations were used
as the input for outside storage simulations. The slurry temperature
was altered in monthly intervals according to Danish weather conditions
(Supporting Information, Table S4). In
the storage simulations, the slurry was completely removed once a
year in March and 10% of the slurry was removed for field application
in September. The enrichment factor (resid_enrich) was set to zero
due to assumed vigorous agitation of the slurry tank before field
application. The simulated storage was scaled to fit the slurry from
30 pigs by setting the surface area to 20 m2, thereby achieving
an average slurry height of ca. 2 m over a 1 year simulation. The
pH in the storage was set to 7 for all treatments (slightly higher
than in-barn slurry pH) as pH tends to increase slightly during storage.32 (link) Predicted methane emission was normalized to
slurry volume to correct for differences in slurry production between
the sections.
Dalby F.R., Hansen M.J., Guldberg L.B., Hafner S.D, & Feilberg A. (2023). Simple Management Changes Drastically Reduce Pig House Methane Emission in Combined Experimental and Modeling Study. Environmental Science & Technology, 57(9), 3990-4002.
Publication 2023
Methane Methanobacteria N,N,N',N'-tetramethyl-4,4'-methylenedianiline Pigs

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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.

More about "Methane"

Methane, a simple hydrocarbon gas, is the primary component of natural gas and a potent greenhouse gas contributing significantly to global climate change.
It is produced through the anaerobic decomposition of organic matter, such as in wetlands, rice paddies, and the digestive tracts of ruminant animals, as well as during fossil fuel extraction and processing.
Understanding methane sources, sinks, and atmospheric concentrations is crucial for mitigating its environmental impact.
Researchers can leverage powerful AI-driven platforms like PubCompare.ai to optimize their methane research.
These tools can help locate the best protocols from literature, preprints, and patents, while enhancing reproducibility and accuracy.
By streamlining the research process, researchers can unlock new insights into this important greenhouse gas.
Methane's chemical structure and properties are closely related to other hydrocarbons, such as GC-2014 and GC-2010, as well as other gases like hydrochloric acid and GC-8A.
Additionally, methane production can be influenced by factors like methanol, bovine serum albumin (BSA), fetal bovine serum (FBS), Tris(hydroxymethyl)aminomethane (TRIS), and dimethyl sulfoxide (DMSO).
Sodium chloride (NaCl) is also a common reagent used in methane-related research.
Understanding these related compounds and factors can provide valuable context for methane studies.