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Coal
Coal
Coal is a solid, combustible, sedimentary rock formed from the decomposition of plant matter over millions of years.
It is a major source of energy worldwide, used primarily for electricity generation and steel production.
Coal can be classified by its rank, which refers to the degree of metamorphosis the plant material has undergone, ranging from lignite (the lowest rank) to anthracite (the highest rank).
Coal research aims to improve the efficiency, environmental impact, and safety of coal extraction, processing, and utilization.
PubCompare.ai's innovative AI-powered tools can enhance coal research reproducibility and accuracy by helping researchers locate the best protocols from the literature, pre-prints, and patents using advanced comparisons.
Get started with PubCompare.ai today to imporve your coal research.
It is a major source of energy worldwide, used primarily for electricity generation and steel production.
Coal can be classified by its rank, which refers to the degree of metamorphosis the plant material has undergone, ranging from lignite (the lowest rank) to anthracite (the highest rank).
Coal research aims to improve the efficiency, environmental impact, and safety of coal extraction, processing, and utilization.
PubCompare.ai's innovative AI-powered tools can enhance coal research reproducibility and accuracy by helping researchers locate the best protocols from the literature, pre-prints, and patents using advanced comparisons.
Get started with PubCompare.ai today to imporve your coal research.
Most cited protocols related to «Coal»
Age Groups
Airborne Particulate Matter
Air Pollution
Animals
Blood Pressure
Carcinogens
Charcoal
Coal
Coronary Arteriosclerosis
Crop, Avian
Feces
Gender
Glucose
Health Risk Assessment
High Blood Pressures
Households
Hypercholesterolemia
Index, Body Mass
physiology
Plasma
Population at Risk
[25 (link)] define the ‘internode certainty’ (ICA) metric that quantifies the degree of certainty for individual focal bipartitions (internal edges) by considering the frequency of all conflicting bipartitions. This is calculated for each internal edge, i, as:
where b is the number of unique conflicting bipartitions (including the bipartition of interest, i) and P(Xn) is the proportional frequency of bipartition n in the set of bipartitions being examined. ICA values near 0 indicate maximum conflict (i.e. conflicting bipartitions are of similar frequency), whereas values near 1 indicate strong certainty in the bipartition of interest. As originally implemented, this measure requires complete taxon overlap. Very few gene trees in the set of homologs contained all taxa, and many of these homolog trees contained gene duplications. However, the ICA measurement itself only requires the ability to calculate the frequency of conflicting and compatible bipartitions. We use the distribution of conflicting bipartitions as determined using the above procedure for calculating the ICA statistic on our species tree and homolog phylogenies. The nature of reduced taxon sampling reduces the accuracy of the ICA. To explore the behavior of the ICA when presented with gene trees with missing data we conducted simulations. We simulated 50 phylogenies under a pure birth process each with 50 taxa. For each tree, we rescaled the root to 10 and conducted 1000 coalescent tree simulations using COAL [53 (link)] to generate topological conflict with respect to each internal node in the original pure birth tree. We then randomly pruned each of the 1000 gene trees according to a set percentage of missing data. We conducted these simulations reducing the gene trees with 10 %, 20 %, and 30 % missing data. For the empirical datasets, we recorded the ICA statistic for each bipartition in the combined species tree. Alternative methods for calculating ICA with missing taxa, but without gene duplications, are described by Kobert et al. (http://dx.doi.org/10.1101/022053 ).
where b is the number of unique conflicting bipartitions (including the bipartition of interest, i) and P(Xn) is the proportional frequency of bipartition n in the set of bipartitions being examined. ICA values near 0 indicate maximum conflict (i.e. conflicting bipartitions are of similar frequency), whereas values near 1 indicate strong certainty in the bipartition of interest. As originally implemented, this measure requires complete taxon overlap. Very few gene trees in the set of homologs contained all taxa, and many of these homolog trees contained gene duplications. However, the ICA measurement itself only requires the ability to calculate the frequency of conflicting and compatible bipartitions. We use the distribution of conflicting bipartitions as determined using the above procedure for calculating the ICA statistic on our species tree and homolog phylogenies. The nature of reduced taxon sampling reduces the accuracy of the ICA. To explore the behavior of the ICA when presented with gene trees with missing data we conducted simulations. We simulated 50 phylogenies under a pure birth process each with 50 taxa. For each tree, we rescaled the root to 10 and conducted 1000 coalescent tree simulations using COAL [53 (link)] to generate topological conflict with respect to each internal node in the original pure birth tree. We then randomly pruned each of the 1000 gene trees according to a set percentage of missing data. We conducted these simulations reducing the gene trees with 10 %, 20 %, and 30 % missing data. For the empirical datasets, we recorded the ICA statistic for each bipartition in the combined species tree. Alternative methods for calculating ICA with missing taxa, but without gene duplications, are described by Kobert et al. (
Childbirth
Coal
Gene Duplication
Genes
Genetic Processes
Plant Roots
Trees
1-palmitoyl-2-oleoylphosphatidylethanolamine
Age Groups
Airborne Particulate Matter
Air Pollution
Blood Pressure
Blood Vessel
Body Size
Cardiovascular Diseases
Cerebrovascular Accident
Child
Cholesterol
Cholesterol, beta-Lipoprotein
Chronic Obstructive Airway Disease
Coal
Cocaine
Diarrhea
Diet
Diet, Mediterranean
Dietary Exposure
Dietary Fiber
Dietary Modification
Environmental Pollutants
factor A
Fats
Fishes
Flatulence
Fruit
Genetic Heterogeneity
Glucose
High Blood Pressures
Households
Human Body
Hypercholesterolemia
Hypersensitivity
Lung Cancer
Meat
Myocardial Ischemia
Nuts
Omega-3 Fatty Acids
Plant Embryos
Plant Roots
Plasma
Population Group
Respiration Disorders
Respiratory Tract Infections
Rivers
Seafood
Systolic Pressure
Vegetables
Whole Grains
Zinc
Gene tree distributions and probabilities can be estimated based on a multi-species coalescent model [54 (link)]. In order to better determine whether the distribution of conflicting trees follows a pattern that could be explained by incomplete lineage sorting, we simulated gene trees on the species trees of Hymenoptera and Caryophylalles. In order to conduct these analyses, it is necessary to transform the species tree from branch lengths proportional to substitutions per site to branch lengths in coalescent time units (proportional to the product of population size Ne and mutation rate). Because we have no estimates of population size or mutation rate, and these are likely to have varied over the course of evolution for both groups, we transformed the trees to be ultrametric using treePL [55 (link)] and varied the root heights to be 10, 20 and 30. As branch lengths in these coalescent simulations reflect effective population size and mutation rate, if mutation rate is kept constant, these heights represent a broad range of effective population sizes. Under these conditions, deep coalescent events range from significantly frequent (as with 10) to relatively rare (as with 30). For each tree height, we generated 10,000 gene trees using COAL [53 (link)] and conducted the same bipartition analyses described above for the empirical datasets.
Biological Evolution
Coal
Genes
Hymenoptera
Plant Roots
Trees
The experiment took place in three plots at Wytham Woods, Oxfordshire, UK between September 2013 and March 2014 (see [10 (link)] for details). Great tits (Parus major), blue tits (Cyanistes caeruleus), marsh tits (Poecile palustris), coal tits (Periparus ater) and Eurasian nuthatches (Sitta europaea) were fitted with unique but randomly numbered radio frequent identification (RFID)-transponders when caught during the breeding season or by winter mist-netting [4 (link)].
RFID antenna-equipped sunflower seed feeding stations continuously recorded the times of individuals' feeding visits at six locations for 40 days. We then replaced each feeder with two ‘selective’ feeders (placed approx. 100 m apart). Selective feeders had clear flaps locked over the feeding hole, which could be unlocked based on a bird's identity (see the electronic supplementary material, videos). At each site, one selective feeder was programmed to allow access only to birds with even-numbered RFID-tags and the other only allowed odd-numbered birds (but both recorded all visits, irrespective of tag-type). Birds of the same tag-type could access the same feeding stations as one another (termed ‘matched’ dyads), while those of the opposite tag-type could only access different feeding stations from one another (termed ‘mismatched’ dyads). We ran this manipulation for 90 days, during which time birds with odd and even tags remained spatially overlapping but became socially segregated [10 (link)], as they quickly learnt which feeders they could access [11 (link)].
We constructed one social network from the data before the experimental manipulation (40 days) and one during (90 days). We used machine-learning algorithms to identify distinct flocks visiting each feeder [12 (link),13 (link)] and constructed social networks given individuals' co-occurrences in flocks using the simple ratio index [14 (link)] defined as , where SAB is the social association between birds A and B; x is the number of observations of A and B co-occurring together; RA and RB are the number of times A was recorded without B or B without A and RAB is the number of times both birds were simultaneously observed apart.
RFID antenna-equipped sunflower seed feeding stations continuously recorded the times of individuals' feeding visits at six locations for 40 days. We then replaced each feeder with two ‘selective’ feeders (placed approx. 100 m apart). Selective feeders had clear flaps locked over the feeding hole, which could be unlocked based on a bird's identity (see the electronic supplementary material, videos). At each site, one selective feeder was programmed to allow access only to birds with even-numbered RFID-tags and the other only allowed odd-numbered birds (but both recorded all visits, irrespective of tag-type). Birds of the same tag-type could access the same feeding stations as one another (termed ‘matched’ dyads), while those of the opposite tag-type could only access different feeding stations from one another (termed ‘mismatched’ dyads). We ran this manipulation for 90 days, during which time birds with odd and even tags remained spatially overlapping but became socially segregated [10 (link)], as they quickly learnt which feeders they could access [11 (link)].
We constructed one social network from the data before the experimental manipulation (40 days) and one during (90 days). We used machine-learning algorithms to identify distinct flocks visiting each feeder [12 (link),13 (link)] and constructed social networks given individuals' co-occurrences in flocks using the simple ratio index [14 (link)] defined as , where SAB is the social association between birds A and B; x is the number of observations of A and B co-occurring together; RA and RB are the number of times A was recorded without B or B without A and RAB is the number of times both birds were simultaneously observed apart.
Aves
Coal
Helianthus annuus
Marshes
Surgical Flaps
Most recents protocols related to «Coal»
Example 2
A mixture obtained by mixing 100 parts by mass of granular coal pitch having a softening point of 280° C. as an organic material with 0.9 part by mass of tris(2,4-pentanedionato)iron(III) (metal species: Fe) was fed into a melt extruder, where it was melted and mixed at a melting temperature of 320° C., and spun at a discharge rate of 16 g/min to obtain a pitch fiber. The pitch fiber was subjected to an infusibilization treatment by heating for 54 minutes, to 354° C. from ambient temperature in the air at a rate of 1 to 30° C./minute, to obtain an infusibilized pitch fiber as an activated carbon precursor. The iron (Fe) content in the activated carbon precursor was 0.11% by mass.
The activated carbon precursor was activated by conducting a heat treatment at an atmospheric temperature of 950° C. for 40 minutes, while continuously introducing a gas having a CO2 concentration of 100% by volume into an activation furnace, to obtain an activated carbon of Example 2. In the activated carbon, the pore volume A of pores with a size of 1.0 nm or less was 0.396 cc/g, the pore volume B of pores with a size of 3.0 nm or more and 3.5 nm or less was 0.016 cc/g, the iron content was 0.251% by mass, and the average fiber diameter was 13.6 μm.
Granular coal pitch having a softening point of 280° C. as an organic material was fed into a melt extruder, where it was melted and mixed at a melting temperature of 320° C., and spun at a discharge rate of 20 g/min, to obtain a pitch fiber. The pitch fiber was subjected to an infusibilization treatment by heating for 54 minutes, to 354° C. from ambient temperature in the air at a rate of 1 to 30° C./minute, to obtain an infusibilized pitch fiber as an activated carbon precursor. The iron content in the activated carbon precursor was 0% by mass.
The activated carbon precursor was activated by conducting a heat treatment at an atmospheric temperature of 875° C. for 40 minutes, while continuously introducing a gas having an H2O concentration of 100% by volume into an activation furnace, to obtain an activated carbon of Comparative Example 2. In the activated carbon, the pore volume A of pores with a size of 1.0 nm or less was 0.401 cc/g, the pore volume B of pores with a size of 3.0 nm or more and 3.5 nm or less was 0.000 cc/g, the iron content was 0% by mass, and the average fiber diameter was 16.7 μm.
Carbon Fiber
Charcoal, Activated
Coal
Fibrosis
Iron
Metals
Patient Discharge
Tromethamine
Example 6
A mixture obtained by mixing 100 parts by mass of granular coal pitch having a softening point of 280° C. as an organic material with 0.3 part by mass of tris(acetylacetonato)yttrium was fed into a melt extruder, where it was melted and mixed at a melting temperature of 320° C., and spun at a discharge rate of 20 g/min to obtain a pitch fiber. The pitch fiber was subjected to an infusibilization treatment by heating for 54 minutes, to 354° C. from ambient temperature in the air at a rate of 1 to 30° C./minute, to obtain an infusibilized pitch fiber as an activated carbon precursor. The yttrium content in the activated carbon precursor was 0.06% by mass.
The activated carbon precursor was activated by conducting a heat treatment at an atmospheric temperature of 950° C. for 60 minutes, while continuously introducing a gas having a CO2 concentration of 100% by volume into an activation furnace, to obtain an activated carbon of Comparative Example 6. In the activated carbon, the pore volume A of pores with a size of 1.0 nm or less was 0.429 cc/g, the pore volume B of pores with a size of 3.0 nm or more and 3.5 nm or less was 0.000 cc/g, the yttrium content was 0.15% by mass, and the fiber diameter was 18.2 μm.
Carbon Fiber
Charcoal, Activated
Coal
Fibrosis
Patient Discharge
Tromethamine
Yttrium
Example 11
Alternative feedstocks to caking coals were explored as source materials for carbon foam. In one series of experiments, a foaming pitch derived from non-caking coal prepared as described above was used as a feedstock.
90 g of foaming pitch with a particle size range of 30-50 mesh was weighed and transferred to a 250 mL beaker and 15 g of a flux agent composed of high fructose corn syrup and recycled coal volatiles as described previously was added. The contents were mixed for a period of time until the mixture was homogeneous. The foaming mixture was loaded into a crucible and converted into carbon foam using microwave radiation at 20% power for 5 min. The foam was covered with a ceramic lid and calcined in one step in a non-oxidizing environment as described previously.
A thin layer of a graphene-type compound was found on the lid of the crucible after this experiment, showing that the method can provide an additional carbon species from vapors expelled during the heat treatment and calcination processes disclosed herein. Examples of graphene-type layers formed on carbon foams can be seen in
11-dehydrocorticosterone
Carbon
Coal
Figs
Graphene
High Fructose Corn Syrup
Microwaves
Vision
Example 12
In some experiments, larger samples having compositions similar to those described previously (i.e., containing coal powder, high fructose corn syrup, and graphite) but with a top surface area of approximately 1 square foot were prepared. Coal flux mixtures were prepared using a commercial mixer. A square sample container 1 foot on each side was constructed and a large-chamber microwave with rotating coil was obtained for these experiments. Several samples of this size were manufactured successfully using the heating protocols described previously. The container used for large-scale foam production as well as an example large piece of foam are seen in
It will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Coal
Figs
Foot
Graphite
High Fructose Corn Syrup
Microwaves
Powder
Vision
Example 6
Samples containing high fructose corn syrup as an additional agent but lacking a conductive carbon compound were prepared as described above. Samples containing high fructose corn syrup as well as 1% by weight or 5% by weight graphite were also prepared. Samples containing 1% by weight graphite reached the required temperatures more easily and thus formed green foam more quickly, and a similar increase was also seen for samples containing 5% by weight graphite, regardless of whether high volatile or low volatile bituminous coal was used. Results were similar for different coal particle sizes (20-35 mesh, 35-60 mesh, 60-100 mesh, and >100 mesh) as well as for different microwave power levels, with the largest impact of increasing graphite concentration on foam formation time at low power levels. A 1000 W microwave was used for most experiments.
Bituminous Coal
Carbon
Coal
Electric Conductivity
Graphite
High Fructose Corn Syrup
Microwaves
Vision
Top products related to «Coal»
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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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More about "Coal"
Explore the Diverse World of Coal: From Energy Generation to Steel Production Coal is a remarkable natural resource that has played a pivotal role in powering industries and societies worldwide.
This solid, combustible, sedimentary rock is formed from the decomposition of plant matter over millions of years, making it a major source of energy globally.
Coal can be classified by its rank, ranging from the lowest grade lignite to the highest grade anthracite, each with unique properties and applications.
Research into coal has aimed to enhance the efficiency, environmental impact, and safety of its extraction, processing, and utilization.
Advanced analytical techniques, such as those enabled by the ESCALAB 250Xi and D8 Advance instruments, can provide valuable insights into the composition and characteristics of coal samples.
Meanwhile, chemical treatments with Sodium hydroxide, Formic acid, and Ethanol can help refine and extract valuable components from coal.
Innovations in coal research, like those facilitated by PubCompare.ai's AI-powered tools, can further advance the field.
By helping researchers locate the best protocols from literature, pre-prints, and patents, PubCompare.ai can enhance the reproducibility and accuracy of coal studies.
This, in turn, can lead to improved coal-based energy generation, steel production, and other industrial applications.
Whether you're exploring the geological origins of coal, investigating its chemical properties, or developing new extraction and processing methods, PubCompare.ai's intuitive platform can be a valuable asset in your coal research journey.
Get started with PubCompare.ai today and unlock the full potential of this versatile and influential natural resource.
OtherTerms: Coal, Energy, Steel, Lignite, Anthracite, ESCALAB 250Xi, Sodium hydroxide, Formic acid, Ethanol, D8 Advance, PubCompare.ai, Reproducibility, Accuracy, Extraction, Processing, Utilization
This solid, combustible, sedimentary rock is formed from the decomposition of plant matter over millions of years, making it a major source of energy globally.
Coal can be classified by its rank, ranging from the lowest grade lignite to the highest grade anthracite, each with unique properties and applications.
Research into coal has aimed to enhance the efficiency, environmental impact, and safety of its extraction, processing, and utilization.
Advanced analytical techniques, such as those enabled by the ESCALAB 250Xi and D8 Advance instruments, can provide valuable insights into the composition and characteristics of coal samples.
Meanwhile, chemical treatments with Sodium hydroxide, Formic acid, and Ethanol can help refine and extract valuable components from coal.
Innovations in coal research, like those facilitated by PubCompare.ai's AI-powered tools, can further advance the field.
By helping researchers locate the best protocols from literature, pre-prints, and patents, PubCompare.ai can enhance the reproducibility and accuracy of coal studies.
This, in turn, can lead to improved coal-based energy generation, steel production, and other industrial applications.
Whether you're exploring the geological origins of coal, investigating its chemical properties, or developing new extraction and processing methods, PubCompare.ai's intuitive platform can be a valuable asset in your coal research journey.
Get started with PubCompare.ai today and unlock the full potential of this versatile and influential natural resource.
OtherTerms: Coal, Energy, Steel, Lignite, Anthracite, ESCALAB 250Xi, Sodium hydroxide, Formic acid, Ethanol, D8 Advance, PubCompare.ai, Reproducibility, Accuracy, Extraction, Processing, Utilization