Datasets:

Shahzebbb
/

wiki_processed

Dataset card Data Studio Files Files and versions Community

Split (1)

train · 22.3M rows

chunks stringlengths 1 1.02k
rennial philosophy Philosophers of culture Philosophers of ethics and morality Philosophers of literature Philosophers of mind Philosophers of technology Psychedelic drug advocates Writers from Los Angeles Writers from Taos, New Mexico 20thcentury English philosophers Lost Generation writers
Ada may refer to Places Africa Ada Foah or Ada, Ghana, a town Ada Ghana parliament constituency Ada, Osun, a town in Osun State, Nigeria Asia Adeh, Urmia, also known as Ada, a village in West Azerbaijan Province Ada, Karaman, a village in Karaman Province, Turkey Australia and New Zealand Ada River disambiguation, three rivers Europe Ada, Bosnia and Herzegovina, a village Ada, Croatia, a village Ada, Serbia, a town and municipality Ada Ciganlija or Ada, a river island artificially turned into a peninsula in Belgrade, Serbia North America United States Ada, Alabama, an unincorporated community Ada County, Idaho Ada, Kansas, an unincorporated community Ada Township, Michigan Ada, Minnesota, a city Ada Township, Dickey County, North Dakota Ada, Ohio, a village Ada, Oklahoma, a city Ada, Oregon, an unincorporated community Ada Township, Perkins County, South Dakota Ada, West Virginia, an unincorporated community Ada, Wisconsin, an unincorporated community Outer space 523 Ada, an ast
eroid Film and television Ada TV, a television channel in the Turkish Republic of Northern Cyprus Ada 1961 film, a 1961 film by Daniel Mann Ada 2019 film, a short biopic about Ada Lovelace Ada... A Way of Life, a 2008 Bollywood musical by Tanvir Ahmed Ada dog actor, a dog that played Colin on the sitcom Spaced Ada, one of the main characters in 1991 movie Armour of God II Operation Condor Biology Ada plant, a genus of orchids Adenosine deaminase, an enzyme involved in purine metabolism Ada protein, an enzyme induced by treatment of bacterial cells Computer science Ada programming language, programming language based on Pascal Ada computer virus Air travel Ada Air, a regional airline based in Tirana, Albania Ada International Airport or Saipan International Airport, Saipan Island, Northern Mariana Islands Aerolnea de Antioquia, a Colombian airline Airline Deregulation Act, a 1978 US bill removing governmental control from commercial aviation Schools Ada, the National College for Digital Sk
ills, a further education college in Tottenham Hale, London Ada High School Ohio, Ada, Ohio Ada High School Oklahoma, Ada, Oklahoma People Ada name, a feminine given name and a surname, including a list of people and fictional characters Ada Lovelace 18151852, computer scientist sometimes regarded as the first computer programmer Other uses List of tropical storms named Ada Ada food, a traditional Kerala delicacy Ada, the cryptocurrency of the Cardano blockchain platform Ada Bridge, Belgrade, Serbia , a cargo vessel built for the London and South Western Railway Ada ship, a wooden ketch, wrecked near Newcastle, New South Wales, Australia Ada or Ardor A Family Chronicle, novel by Vladimir Nabokov Dangme language, spoken in Ghana ISO 6392 and 6393 code "ada" Ada Health GmbH, a symptom checker app See also ADA disambiguation Ada regulon, an Escherichia coli adaptive response protein Adah disambiguation Adha disambiguation Ada'a, a woreda in the Oromia Region of Ethiopia Ade disambiguation U
SS Little Ada 1864, a steamer captured by the Union Navy during the American Civil War
Aberdeen is a city in Scotland, United Kingdom. Aberdeen may also refer to Places Africa Aberdeen, Sierra Leone Aberdeen, Eastern Cape, South Africa Asia Hong Kong Aberdeen, Hong Kong, an area and town on southwest Hong Kong Island Aberdeen Channel, a channel between Ap Lei Chau Aberdeen Island and Nam Long Shan on the Hong Kong Island in Hong Kong Aberdeen Country Park, a country park in Hong Kong Island Aberdeen floating village, at Aberdeen Harbour, containing approximately 600 junks, which house an estimated 6,000 people Aberdeen Harbour, a harbour between Aberdeen, Hong Kong and Ap Lei Chau Aberdeen Island Aberdeen Tunnel, a tunnel in Hong Kong Island Aberdeen Tunnel Underground Laboratory, an underground particle physics laboratory in Hong Kong Island Ap Lei Chau or Aberdeen Island, an island of Hong Kong Aberdeen constituency, a constituency of Southern District Council India Aberdeen Bazaar, a shopping centre in Port Blair, South Andaman Island Sri Lanka Aberdeen Falls, a water
fall in Sri Lanka Australia Aberdeen, New South Wales Aberdeen, South Australia, one of the early townships that merged in 1940 to create the town of Burra Aberdeen, Tasmania, a suburb of the City of Devonport Caribbean Aberdeen, Jamaica, a town in Saint Elizabeth, Jamaica Europe Aberdeen Parliament of Scotland constituency Aberdeen UK Parliament constituency 18321885 Aberdeen Burghs UK Parliament constituency 18011832 Aberdeen Central Scottish Parliament constituency Aberdeen Central UK Parliament constituency Aberdeen Donside Scottish Parliament constituency County of Aberdeen, a historic county of Scotland whose county town was Aberdeen Old Aberdeen, a part of the city of Aberdeen in Scotland North America Canada Aberdeen, community in the township of Champlain, Prescott and Russell County, Ontario Aberdeen, Abbotsford, a neighbourhood in the City of Abbotsford, British Columbia Aberdeen Centre, a shopping mall in Richmond, British Columbia Aberdeen, Grey County, Ontario Aberdeen,
Kamloops, an area in the City of Kamloops, British Columbia Aberdeen Lake Nunavut, a lake in Kivalliq Region, Nunavut, Canada Aberdeen, Nova Scotia, part of the Municipality of Inverness County, Nova Scotia Aberdeen Parish, New Brunswick Rural Municipality of Aberdeen No. 373, Saskatchewan Aberdeen, Saskatchewan Aberdeen Bay, a bay between southern Baffin Island and northeastern Hector Island in the Nunavut territory Aberdeen Township, Quebec, until 1960 part of SheenEsherAberdeenetMalakoff, now part of RapidesdesJoachims, Quebec Aberdeen River, a tributary of rivire aux Castors Noirs in Mauricie, Qubec New Aberdeen, Nova Scotia United States Aberdeen, Arkansas Aberdeen, Florida Aberdeen, Georgia Aberdeen, Idaho Aberdeen, Ohio County, Indiana Aberdeen, Porter County, Indiana Aberdeen, Kentucky Aberdeen, Maryland Aberdeen Proving Ground, a United States Army facility located near Aberdeen, Maryland Aberdeen, Massachusetts, a neighborhood of Brighton, Boston Aberdeen, Mississippi Aberdeen
Lake Mississippi, a lake in northeast Mississippi on the TennesseeTombigbee Waterway, close to Aberdeen, Mississippi Aberdeen Township, New Jersey Aberdeen, North Carolina Aberdeen Historic District Aberdeen, North Carolina Aberdeen, Ohio Aberdeen, South Dakota Aberdeen Historic District Aberdeen, South Dakota Aberdeen, Texas Aberdeen Disputanta, Virginia Aberdeen Gardens Hampton, Virginia Aberdeen, Washington Aberdeen Gardens, Washington Aberdeen, West Virginia Business Abrdn, formerly Standard Life Aberdeen Aberdeen Asset Management Education Aberdeen Business School Aberdeen College, formerly one of the largest further education colleges in Scotland, merged with Banff Buchan College to form North East Scotland College Aberdeen Grammar School, Aberdeen, Scotland Aberdeen Hall, a universitypreparatory school in Kelowna, British Columbia, Canada Aberdeen High School disambiguation King's College, Aberdeen University of Aberdeen, a public research university in the city of Aberdeen E
ntertainment Aberdeen 2000 film, a 2000 NorwegianBritish film directed by Hans Petter Moland, starring Stellan Skarsgrd and Lena Headey Aberdeen 2014 film, a 2014 Hong Kong film starring Louis Koo Aberdeen band, an American rock band Aberdeen song, a song by Cage The Elephant Aberdeen City band, Boston based indiealternative rock band Other transportation Aberdeen Airport disambiguation Aberdeen Lock and Dam, one of four lock and dam structures on the TennesseeTombigbee Waterway Rail Aberdeen, Carolina and Western Railway, a shortline railroad operating in North Carolina Aberdeen and Rockfish Railroad, a shortline railroad operating in North Carolina Aberdeen Corporation Tramways Aberdeen Line disambiguation Aberdeen station disambiguation Dundee and Perth and Aberdeen Junction Railway, a later name of the Dundee and Perth Railway Shipping Aberdeen Line, a British shipping company founded in 1825 , one of several ships by that name , a sloop of the British Royal Navy that served between
1936 and 1948 , a merchant ship operated during the latter stages of World War II, later commissioned as the USS Altair Sports Aberdeen Dad Vail Regatta, the largest regular intercollegiate rowing event in the United States, named after its sponsor, Aberdeen Asset Management Aberdeen F.C. disambiguation Aberdeen GSFP RFC, an amateur rugby union club based in Aberdeen Aberdeen IronBirds, a minor league baseball team affiliated with the Baltimore Orioles Aberdeen L.F.C., a women's football team affiliated with Aberdeen F.C. See also Aberdeen Act Aberdeen Angus, a Scottish breed of small beef cattle Aberdeen Central disambiguation Aberdeen Gardens disambiguation Aberdeen Historic District disambiguation Aberdeen Hospital disambiguation Aberdeen Quarry, a granite quarry in Colorado Battle of Aberdeen disambiguation Diocese of Aberdeen and Orkney, one of the seven dioceses of the Scottish Episcopal Church Etymology of Aberdeen Marquess of Aberdeen and Temair, a title in the Peerage of the Un
ited Kingdom
Algae ; singular alga is an informal term for a large and diverse group of photosynthetic eukaryotic organisms. It is a polyphyletic grouping that includes species from multiple distinct clades. Included organisms range from unicellular microalgae, such as Chlorella, Prototheca and the diatoms, to multicellular forms, such as the giant kelp, a large brown alga which may grow up to in length. Most are aquatic and autotrophic they generate food internally and lack many of the distinct cell and tissue types, such as stomata, xylem and phloem that are found in land plants. The largest and most complex marine algae are called seaweeds, while the most complex freshwater forms are the Charophyta, a division of green algae which includes, for example, Spirogyra and stoneworts. No definition of algae is generally accepted. One definition is that algae "have chlorophyll as their primary photosynthetic pigment and lack a sterile covering of cells around their reproductive cells". Likewise, the colorless Prototheca un
der Chlorophyta are all devoid of any chlorophyll. Although cyanobacteria are often referred to as "bluegreen algae", most authorities exclude all prokaryotes from the definition of algae. Algae constitute a polyphyletic group since they do not include a common ancestor, and although their plastids seem to have a single origin, from cyanobacteria, they were acquired in different ways. Green algae are examples of algae that have primary chloroplasts derived from endosymbiotic cyanobacteria. Diatoms and brown algae are examples of algae with secondary chloroplasts derived from an endosymbiotic red alga. Algae exhibit a wide range of reproductive strategies, from simple asexual cell division to complex forms of sexual reproduction. Algae lack the various structures that characterize land plants, such as the phyllids leaflike structures of bryophytes, rhizoids of nonvascular plants, and the roots, leaves, and other organs found in tracheophytes vascular plants. Most are phototrophic, although some are mixotroph
ic, deriving energy both from photosynthesis and uptake of organic carbon either by osmotrophy, myzotrophy, or phagotrophy. Some unicellular species of green algae, many golden algae, euglenids, dinoflagellates, and other algae have become heterotrophs also called colorless or apochlorotic algae, sometimes parasitic, relying entirely on external energy sources and have limited or no photosynthetic apparatus. Some other heterotrophic organisms, such as the apicomplexans, are also derived from cells whose ancestors possessed plastids, but are not traditionally considered as algae. Algae have photosynthetic machinery ultimately derived from cyanobacteria that produce oxygen as a byproduct of photosynthesis, unlike other photosynthetic bacteria such as purple and green sulfur bacteria. Fossilized filamentous algae from the Vindhya basin have been dated back to 1.6 to 1.7 billion years ago. Because of the wide range of types of algae, they have increasing different industrial and traditional applications in human
society. Traditional seaweed farming practices have existed for thousands of years and have strong traditions in East Asia food cultures. More modern algaculture applications extend the food traditions for other applications include cattle feed, using algae for bioremediation or pollution control, transforming sunlight into algae fuels or other chemicals used in industrial processes, and in medical and scientific applications. A 2020 review, found that these applications of algae could play an important role in carbon sequestration in order to mitigate climate change while providing valuable valueadd products for global economies. Etymology and study The singular is the Latin word for 'seaweed' and retains that meaning in English. The etymology is obscure. Although some speculate that it is related to Latin , 'be cold', no reason is known to associate seaweed with temperature. A more likely source is , 'binding, entwining'. The Ancient Greek word for 'seaweed' was , which could mean either the seaweed pr
obably red algae or a red dye derived from it. The Latinization, , meant primarily the cosmetic rouge. The etymology is uncertain, but a strong candidate has long been some word related to the Biblical , 'paint' if not that word itself, a cosmetic eyeshadow used by the ancient Egyptians and other inhabitants of the eastern Mediterranean. It could be any color black, red, green, or blue. Accordingly, the modern study of marine and freshwater algae is called either phycology or algology, depending on whether the Greek or Latin root is used. The name fucus appears in a number of taxa. Classifications The committee on the International Code of Botanical Nomenclature has recommended certain suffixes for use in the classification of algae. These are phyta for division, phyceae for class, phycideae for subclass, ales for order, inales for suborder, aceae for family, oidease for subfamily, a Greekbased name for genus, and a Latinbased name for species. Algal characteristics basic to primary classification The pr
imary classification of algae is based on certain morphological features. The chief among these are a pigment constitution of the cell, b chemical nature of stored food materials, c kind, number, point of insertion and relative length of the flagella on the motile cell, d chemical composition of cell wall and e presence or absence of a definitely organized nucleus in the cell or any other significant details of cell structure. History of classification of algae Although Carolus Linnaeus 1754 included algae along with lichens in his 25th class Cryptogamia, he did not elaborate further on the classification of algae. Jean Pierre tienne Vaucher 1803 was perhaps the first to propose a system of classification of algae, and he recognized three groups, Conferves, Ulves, and Tremelles. While Johann Heinrich Friedrich Link 1820 classified algae on the basis of the colour of the pigment and structure, William Henry Harvey 1836 proposed a system of classification on the basis of the habitat and the pigment. J. G. Ag
ardh 18491898 divided algae into six orders Diatomaceae, Nostochineae, Confervoideae, Ulvaceae, Floriadeae and Fucoideae. Around 1880, algae along with fungi were grouped under Thallophyta, a division created by Eichler 1836. Encouraged by this, Adolf Engler and Karl A. E. Prantl 1912 proposed a revised scheme of classification of algae and included fungi in algae as they were of opinion that fungi have been derived from algae. The scheme proposed by Engler and Prantl is summarised as follows Schizophyta Phytosarcodina Flagellata Dinoflagellata Bacillariophyta Conjugatae Chlorophyceae Charophyta Phaeophyceae Rhodophyceae Eumycetes Fungi The algae contain chloroplasts that are similar in structure to cyanobacteria. Chloroplasts contain circular DNA like that in cyanobacteria and are interpreted as representing reduced endosymbiotic cyanobacteria. However, the exact origin of the chloroplasts is different among separate lineages of algae, reflecting their acquisition during different endosymbiotic
events. The table below describes the composition of the three major groups of algae. Their lineage relationships are shown in the figure in the upper right. Many of these groups contain some members that are no longer photosynthetic. Some retain plastids, but not chloroplasts, while others have lost plastids entirely. Phylogeny based on plastid not nucleocytoplasmic genealogy Linnaeus, in Species Plantarum 1753, the starting point for modern botanical nomenclature, recognized 14 genera of algae, of which only four are currently considered among algae. In Systema Naturae, Linnaeus described the genera Volvox and Corallina, and a species of Acetabularia as Madrepora, among the animals. In 1768, Samuel Gottlieb Gmelin 17441774 published the Historia Fucorum, the first work dedicated to marine algae and the first book on marine biology to use the then new binomial nomenclature of Linnaeus. It included elaborate illustrations of seaweed and marine algae on folded leaves. W. H. Harvey 18111866 and Lamouroux 18
13 were the first to divide macroscopic algae into four divisions based on their pigmentation. This is the first use of a biochemical criterion in plant systematics. Harvey's four divisions are red algae Rhodospermae, brown algae Melanospermae, green algae Chlorospermae, and Diatomaceae. At this time, microscopic algae were discovered and reported by a different group of workers e.g., O. F. Mller and Ehrenberg studying the Infusoria microscopic organisms. Unlike macroalgae, which were clearly viewed as plants, microalgae were frequently considered animals because they are often motile. Even the nonmotile coccoid microalgae were sometimes merely seen as stages of the lifecycle of plants, macroalgae, or animals. Although used as a taxonomic category in some preDarwinian classifications, e.g., Linnaeus 1753, de Jussieu 1789, Horaninow 1843, Agassiz 1859, Wilson Cassin 1864, in further classifications, the "algae" are seen as an artificial, polyphyletic group. Throughout the 20th century, most classifications
treated the following groups as divisions or classes of algae cyanophytes, rhodophytes, chrysophytes, xanthophytes, bacillariophytes, phaeophytes, pyrrhophytes cryptophytes and dinophytes, euglenophytes, and chlorophytes. Later, many new groups were discovered e.g., Bolidophyceae, and others were splintered from older groups charophytes and glaucophytes from chlorophytes, many heterokontophytes e.g., synurophytes from chrysophytes, or eustigmatophytes from xanthophytes, haptophytes from chrysophytes, and chlorarachniophytes from xanthophytes. With the abandonment of plantanimal dichotomous classification, most groups of algae sometimes all were included in Protista, later also abandoned in favour of Eukaryota. However, as a legacy of the older plant life scheme, some groups that were also treated as protozoans in the past still have duplicated classifications see ambiregnal protists. Some parasitic algae e.g., the green algae Prototheca and Helicosporidium, parasites of metazoans, or Cephaleuros, parasites
of plants were originally classified as fungi, sporozoans, or protistans of incertae sedis, while others e.g., the green algae Phyllosiphon and Rhodochytrium, parasites of plants, or the red algae Pterocladiophila and Gelidiocolax mammillatus, parasites of other red algae, or the dinoflagellates Oodinium, parasites of fish had their relationship with algae conjectured early. In other cases, some groups were originally characterized as parasitic algae e.g., Chlorochytrium, but later were seen as endophytic algae. Some filamentous bacteria e.g., Beggiatoa were originally seen as algae. Furthermore, groups like the apicomplexans are also parasites derived from ancestors that possessed plastids, but are not included in any group traditionally seen as algae. Relationship to land plants The first land plants probably evolved from shallow freshwater charophyte algae much like Chara almost 500 million years ago. These probably had an isomorphic alternation of generations and were probably filamentous. Fossils of is
olated land plant spores suggest land plants may have been around as long as 475 million years ago. Morphology A range of algal morphologies is exhibited, and convergence of features in unrelated groups is common. The only groups to exhibit threedimensional multicellular thalli are the reds and browns, and some chlorophytes. Apical growth is constrained to subsets of these groups the florideophyte reds, various browns, and the charophytes. The form of charophytes is quite different from those of reds and browns, because they have distinct nodes, separated by internode 'stems'; whorls of branches reminiscent of the horsetails occur at the nodes. Conceptacles are another polyphyletic trait; they appear in the coralline algae and the Hildenbrandiales, as well as the browns. Most of the simpler algae are unicellular flagellates or amoeboids, but colonial and nonmotile forms have developed independently among several of the groups. Some of the more common organizational levels, more than one of which may occur
in the lifecycle of a species, are Colonial small, regular groups of motile cells Capsoid individual nonmotile cells embedded in mucilage Coccoid individual nonmotile cells with cell walls Palmelloid nonmotile cells embedded in mucilage Filamentous a string of nonmotile cells connected together, sometimes branching Parenchymatous cells forming a thallus with partial differentiation of tissues In three lines, even higher levels of organization have been reached, with full tissue differentiation. These are the brown algae,some of which may reach 50 m in length kelpsthe red algae, and the green algae. The most complex forms are found among the charophyte algae see Charales and Charophyta, in a lineage that eventually led to the higher land plants. The innovation that defines these nonalgal plants is the presence of female reproductive organs with protective cell layers that protect the zygote and developing embryo. Hence, the land plants are referred to as the Embryophytes. Turfs The term algal turf is c
ommonly used but poorly defined. Algal turfs are thick, carpetlike beds of seaweed that retain sediment and compete with foundation species like corals and kelps, and they are usually less than 15 cm tall. Such a turf may consist of one or more species, and will generally cover an area in the order of a square metre or more. Some common characteristics are listed Algae that form aggregations that have been described as turfs include diatoms, cyanobacteria, chlorophytes, phaeophytes and rhodophytes. Turfs are often composed of numerous species at a wide range of spatial scales, but monospecific turfs are frequently reported. Turfs can be morphologically highly variable over geographic scales and even within species on local scales and can be difficult to identify in terms of the constituent species. Turfs have been defined as short algae, but this has been used to describe height ranges from less than 0.5 cm to more than 10 cm. In some regions, the descriptions approached heights which might be described as
canopies 20 to 30 cm. Physiology Many algae, particularly members of the Characeae species, have served as model experimental organisms to understand the mechanisms of the water permeability of membranes, osmoregulation, turgor regulation, salt tolerance, cytoplasmic streaming, and the generation of action potentials. Phytohormones are found not only in higher plants, but in algae, too. Symbiotic algae Some species of algae form symbiotic relationships with other organisms. In these symbioses, the algae supply photosynthates organic substances to the host organism providing protection to the algal cells. The host organism derives some or all of its energy requirements from the algae. Examples are Lichens Lichens are defined by the International Association for Lichenology to be "an association of a fungus and a photosynthetic symbiont resulting in a stable vegetative body having a specific structure". The fungi, or mycobionts, are mainly from the Ascomycota with a few from the Basidiomycota. In nature t
hey do not occur separate from lichens. It is unknown when they began to associate. One mycobiont associates with the same phycobiont species, rarely two, from the green algae, except that alternatively, the mycobiont may associate with a species of cyanobacteria hence "photobiont" is the more accurate term. A photobiont may be associated with many different mycobionts or may live independently; accordingly, lichens are named and classified as fungal species. The association is termed a morphogenesis because the lichen has a form and capabilities not possessed by the symbiont species alone they can be experimentally isolated. The photobiont possibly triggers otherwise latent genes in the mycobiont. Trentepohlia is an example of a common green alga genus worldwide that can grow on its own or be lichenised. Lichen thus share some of the habitat and often similar appearance with specialized species of algae aerophytes growing on exposed surfaces such as tree trunks and rocks and sometimes discoloring them. Cor
al reefs Coral reefs are accumulated from the calcareous exoskeletons of marine invertebrates of the order Scleractinia stony corals. These animals metabolize sugar and oxygen to obtain energy for their cellbuilding processes, including secretion of the exoskeleton, with water and carbon dioxide as byproducts. Dinoflagellates algal protists are often endosymbionts in the cells of the coralforming marine invertebrates, where they accelerate hostcell metabolism by generating sugar and oxygen immediately available through photosynthesis using incident light and the carbon dioxide produced by the host. Reefbuilding stony corals hermatypic corals require endosymbiotic algae from the genus Symbiodinium to be in a healthy condition. The loss of Symbiodinium from the host is known as coral bleaching, a condition which leads to the deterioration of a reef. Sea sponges Endosymbiontic green algae live close to the surface of some sponges, for example, breadcrumb sponges Halichondria panicea. The alga is thus protect
ed from predators; the sponge is provided with oxygen and sugars which can account for 50 to 80 of sponge growth in some species. Lifecycle Rhodophyta, Chlorophyta, and Heterokontophyta, the three main algal divisions, have lifecycles which show considerable variation and complexity. In general, an asexual phase exists where the seaweed's cells are diploid, a sexual phase where the cells are haploid, followed by fusion of the male and female gametes. Asexual reproduction permits efficient population increases, but less variation is possible. Commonly, in sexual reproduction of unicellular and colonial algae, two specialized, sexually compatible, haploid gametes make physical contact and fuse to form a zygote. To ensure a successful mating, the development and release of gametes is highly synchronized and regulated; pheromones may play a key role in these processes. Sexual reproduction allows for more variation and provides the benefit of efficient recombinational repair of DNA damages during meiosis, a key s
tage of the sexual cycle. However, sexual reproduction is more costly than asexual reproduction. Meiosis has been shown to occur in many different species of algae. Numbers The Algal Collection of the US National Herbarium located in the National Museum of Natural History consists of approximately 320,500 dried specimens, which, although not exhaustive no exhaustive collection exists, gives an idea of the order of magnitude of the number of algal species that number remains unknown. Estimates vary widely. For example, according to one standard textbook, in the British Isles the UK Biodiversity Steering Group Report estimated there to be 20,000 algal species in the UK. Another checklist reports only about 5,000 species. Regarding the difference of about 15,000 species, the text concludes "It will require many detailed field surveys before it is possible to provide a reliable estimate of the total number of species ..." Regional and group estimates have been made, as well 5,0005,500 species of red algae wo
rldwide "some 1,300 in Australian Seas" 400 seaweed species for the western coastline of South Africa, and 212 species from the coast of KwaZuluNatal. Some of these are duplicates, as the range extends across both coasts, and the total recorded is probably about 500 species. Most of these are listed in List of seaweeds of South Africa. These exclude phytoplankton and crustose corallines. 669 marine species from California US 642 in the checklist of Britain and Ireland and so on, but lacking any scientific basis or reliable sources, these numbers have no more credibility than the British ones mentioned above. Most estimates also omit microscopic algae, such as phytoplankton. The most recent estimate suggests 72,500 algal species worldwide. Distribution The distribution of algal species has been fairly well studied since the founding of phytogeography in the mid19th century. Algae spread mainly by the dispersal of spores analogously to the dispersal of Plantae by seeds and spores. This dispersal can be ac
complished by air, water, or other organisms. Due to this, spores can be found in a variety of environments fresh and marine waters, air, soil, and in or on other organisms. Whether a spore is to grow into an organism depends on the combination of the species and the environmental conditions where the spore lands. The spores of freshwater algae are dispersed mainly by running water and wind, as well as by living carriers. However, not all bodies of water can carry all species of algae, as the chemical composition of certain water bodies limits the algae that can survive within them. Marine spores are often spread by ocean currents. Ocean water presents many vastly different habitats based on temperature and nutrient availability, resulting in phytogeographic zones, regions, and provinces. To some degree, the distribution of algae is subject to floristic discontinuities caused by geographical features, such as Antarctica, long distances of ocean or general land masses. It is, therefore, possible to identify
species occurring by locality, such as "Pacific algae" or "North Sea algae". When they occur out of their localities, hypothesizing a transport mechanism is usually possible, such as the hulls of ships. For example, Ulva reticulata and U. fasciata travelled from the mainland to Hawaii in this manner. Mapping is possible for select species only "there are many valid examples of confined distribution patterns." For example, Clathromorphum is an arctic genus and is not mapped far south of there. However, scientists regard the overall data as insufficient due to the "difficulties of undertaking such studies." Ecology Algae are prominent in bodies of water, common in terrestrial environments, and are found in unusual environments, such as on snow and ice. Seaweeds grow mostly in shallow marine waters, under deep; however, some such as Navicula pennata have been recorded to a depth of . A type of algae, Ancylonema nordenskioeldii, was found in Greenland in areas known as the 'Dark Zone', which caused an increas
e in the rate of melting ice sheet. Same algae was found in the Italian Alps, after pink ice appeared on parts of the Presena glacier. The various sorts of algae play significant roles in aquatic ecology. Microscopic forms that live suspended in the water column phytoplankton provide the food base for most marine food chains. In very high densities algal blooms, these algae may discolor the water and outcompete, poison, or asphyxiate other life forms. Algae can be used as indicator organisms to monitor pollution in various aquatic systems. In many cases, algal metabolism is sensitive to various pollutants. Due to this, the species composition of algal populations may shift in the presence of chemical pollutants. To detect these changes, algae can be sampled from the environment and maintained in laboratories with relative ease. On the basis of their habitat, algae can be categorized as aquatic planktonic, benthic, marine, freshwater, lentic, lotic, terrestrial, aerial subaerial, lithophytic, halophytic or
euryhaline, psammon, thermophilic, cryophilic, epibiont epiphytic, epizoic, endosymbiont endophytic, endozoic, parasitic, calcifilic or lichenic phycobiont. Cultural associations In classical Chinese, the word is used both for "algae" and in the modest tradition of the imperial scholars for "literary talent". The third island in Kunming Lake beside the Summer Palace in Beijing is known as the Zaojian Tang Dao, which thus simultaneously means "Island of the AlgaeViewing Hall" and "Island of the Hall for Reflecting on Literary Talent". Cultivation Seaweed farming Bioreactors Uses Agar Agar, a gelatinous substance derived from red algae, has a number of commercial uses. It is a good medium on which to grow bacteria and fungi, as most microorganisms cannot digest agar. Alginates Alginic acid, or alginate, is extracted from brown algae. Its uses range from gelling agents in food, to medical dressings. Alginic acid also has been used in the field of biotechnology as a biocompatible medium for cell encapsula
tion and cell immobilization. Molecular cuisine is also a user of the substance for its gelling properties, by which it becomes a delivery vehicle for flavours. Between 100,000 and 170,000 wet tons of Macrocystis are harvested annually in New Mexico for alginate extraction and abalone feed. Energy source To be competitive and independent from fluctuating support from local policy on the long run, biofuels should equal or beat the cost level of fossil fuels. Here, algaebased fuels hold great promise, directly related to the potential to produce more biomass per unit area in a year than any other form of biomass. The breakeven point for algaebased biofuels is estimated to occur by 2025. Fertilizer For centuries, seaweed has been used as a fertilizer; George Owen of Henllys writing in the 16th century referring to drift weed in South Wales Today, algae are used by humans in many ways; for example, as fertilizers, soil conditioners, and livestock feed. Aquatic and microscopic species are cultured in clear t
anks or ponds and are either harvested or used to treat effluents pumped through the ponds. Algaculture on a large scale is an important type of aquaculture in some places. Maerl is commonly used as a soil conditioner. Nutrition Naturally growing seaweeds are an important source of food, especially in Asia, leading some to label them as superfoods. They provide many vitamins including A, B1, B2, B6, niacin, and C, and are rich in iodine, potassium, iron, magnesium, and calcium. In addition, commercially cultivated microalgae, including both algae and cyanobacteria, are marketed as nutritional supplements, such as spirulina, Chlorella and the vitaminC supplement from Dunaliella, high in betacarotene. Algae are national foods of many nations China consumes more than 70 species, including fat choy, a cyanobacterium considered a vegetable; Japan, over 20 species such as nori and aonori; Ireland, dulse; Chile, cochayuyo. Laver is used to make laver bread in Wales, where it is known as ; in Korea, . It is also u
sed along the west coast of North America from California to British Columbia, in Hawaii and by the Mori of New Zealand. Sea lettuce and badderlocks are salad ingredients in Scotland, Ireland, Greenland, and Iceland. Algae is being considered a potential solution for world hunger problem. Two popular forms of algae are used in cuisine Chlorella This form of alga is found in freshwater and contains photosynthetic pigments in its chloroplast. It is high in iron, zinc, magnesium, vitamin B2 and Omega3 Fatty acids. Furthermore, it contains all nine of the essential amino acids the body does not produce on its own Spirulina Known otherwise as a cyanobacterium a prokaryote, incorrectly referred to as a "bluegreen alga", contains 10 more protein than Chlorella as well as more thiamine and copper. The oils from some algae have high levels of unsaturated fatty acids. For example, Parietochloris incisa is very high in arachidonic acid, where it reaches up to 47 of the triglyceride pool. Some varieties of algae fav
ored by vegetarianism and veganism contain the longchain, essential omega3 fatty acids, docosahexaenoic acid DHA and eicosapentaenoic acid EPA. Fish oil contains the omega3 fatty acids, but the original source is algae microalgae in particular, which are eaten by marine life such as copepods and are passed up the food chain. Algae have emerged in recent years as a popular source of omega3 fatty acids for vegetarians who cannot get longchain EPA and DHA from other vegetarian sources such as flaxseed oil, which only contains the shortchain alphalinolenic acid ALA. Pollution control Sewage can be treated with algae, reducing the use of large amounts of toxic chemicals that would otherwise be needed. Algae can be used to capture fertilizers in runoff from farms. When subsequently harvested, the enriched algae can be used as fertilizer. Aquaria and ponds can be filtered using algae, which absorb nutrients from the water in a device called an algae scrubber, also known as an algae turf scrubber. Agricultural R
esearch Service scientists found that 6090 of nitrogen runoff and 70100 of phosphorus runoff can be captured from manure effluents using a horizontal algae scrubber, also called an algal turf scrubber ATS. Scientists developed the ATS, which consists of shallow, 100foot raceways of nylon netting where algae colonies can form, and studied its efficacy for three years. They found that algae can readily be used to reduce the nutrient runoff from agricultural fields and increase the quality of water flowing into rivers, streams, and oceans. Researchers collected and dried the nutrientrich algae from the ATS and studied its potential as an organic fertilizer. They found that cucumber and corn seedlings grew just as well using ATS organic fertilizer as they did with commercial fertilizers. Algae scrubbers, using bubbling upflow or vertical waterfall versions, are now also being used to filter aquaria and ponds. Polymers Various polymers can be created from algae, which can be especially useful in the creation of b
ioplastics. These include hybrid plastics, cellulosebased plastics, polylactic acid, and biopolyethylene. Several companies have begun to produce algae polymers commercially, including for use in flipflops and in surf boards. Bioremediation The alga Stichococcus bacillaris has been seen to colonize silicone resins used at archaeological sites; biodegrading the synthetic substance. Pigments The natural pigments carotenoids and chlorophylls produced by algae can be used as alternatives to chemical dyes and coloring agents. The presence of some individual algal pigments, together with specific pigment concentration ratios, are taxonspecific analysis of their concentrations with various analytical methods, particularly highperformance liquid chromatography, can therefore offer deep insight into the taxonomic composition and relative abundance of natural algae populations in sea water samples. Stabilizing substances Carrageenan, from the red alga Chondrus crispus, is used as a stabilizer in milk products. Ad
ditional images See also AlgaeBase AlgaePARC Eutrophication Iron fertilization Marimo algae Microbiofuels Microphyte Photobioreactor Phycotechnology Plant Toxoid anatoxin References Bibliography General . Regional Britain and Ireland Australia New Zealand Europe Arctic Greenland Faroe Islands . Canary Islands Morocco South Africa North America External links a database of all algal names including images, nomenclature, taxonomy, distribution, bibliography, uses, extracts EnAlgae Endosymbiotic events Polyphyletic groups
Analysis of variance ANOVA is a collection of statistical models and their associated estimation procedures such as the "variation" among and between groups used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the ttest beyond two means. History While the analysis of variance reached fruition in the 20th century, antecedents extend centuries into the past according to Stigler. These include hypothesis testing, the partitioning of sums of squares, experimental techniques and the additive model. Laplace was performing hypothesis testing in the 1770s. Around 1800, Laplace and Gauss developed the leastsquares method for combining observations, which improved u
pon methods then used in astronomy and geodesy. It also initiated much study of the contributions to sums of squares. Laplace knew how to estimate a variance from a residual rather than a total sum of squares. By 1827, Laplace was using least squares methods to address ANOVA problems regarding measurements of atmospheric tides. Before 1800, astronomers had isolated observational errors resulting from reaction times the "personal equation" and had developed methods of reducing the errors. The experimental methods used in the study of the personal equation were later accepted by the emerging field of psychology which developed strong full factorial experimental methods to which randomization and blinding were soon added. An eloquent nonmathematical explanation of the additive effects model was available in 1885. Ronald Fisher introduced the term variance and proposed its formal analysis in a 1918 article The Correlation Between Relatives on the Supposition of Mendelian Inheritance. His first application o
f the analysis of variance was published in 1921. Analysis of variance became widely known after being included in Fisher's 1925 book Statistical Methods for Research Workers. Randomization models were developed by several researchers. The first was published in Polish by Jerzy Neyman in 1923. Example The analysis of variance can be used to describe otherwise complex relations among variables. A dog show provides an example. A dog show is not a random sampling of the breed it is typically limited to dogs that are adult, purebred, and exemplary. A histogram of dog weights from a show might plausibly be rather complex, like the yelloworange distribution shown in the illustrations. Suppose we wanted to predict the weight of a dog based on a certain set of characteristics of each dog. One way to do that is to explain the distribution of weights by dividing the dog population into groups based on those characteristics. A successful grouping will split dogs such that a each group has a low variance of dog we
ights meaning the group is relatively homogeneous and b the mean of each group is distinct if two groups have the same mean, then it isn't reasonable to conclude that the groups are, in fact, separate in any meaningful way. In the illustrations to the right, groups are identified as X1, X2, etc. In the first illustration, the dogs are divided according to the product interaction of two binary groupings young vs old, and shorthaired vs longhaired e.g., group 1 is young, shorthaired dogs, group 2 is young, longhaired dogs, etc.. Since the distributions of dog weight within each of the groups shown in blue has a relatively large variance, and since the means are very similar across groups, grouping dogs by these characteristics does not produce an effective way to explain the variation in dog weights knowing which group a dog is in doesn't allow us to predict its weight much better than simply knowing the dog is in a dog show. Thus, this grouping fails to explain the variation in the overall distribution yellowo
range. An attempt to explain the weight distribution by grouping dogs as pet vs working breed and less athletic vs more athletic would probably be somewhat more successful fair fit. The heaviest show dogs are likely to be big, strong, working breeds, while breeds kept as pets tend to be smaller and thus lighter. As shown by the second illustration, the distributions have variances that are considerably smaller than in the first case, and the means are more distinguishable. However, the significant overlap of distributions, for example, means that we cannot distinguish X1 and X2 reliably. Grouping dogs according to a coin flip might produce distributions that look similar. An attempt to explain weight by breed is likely to produce a very good fit. All Chihuahuas are light and all St Bernards are heavy. The difference in weights between Setters and Pointers does not justify separate breeds. The analysis of variance provides the formal tools to justify these intuitive judgments. A common use of the metho
d is the analysis of experimental data or the development of models. The method has some advantages over correlation not all of the data must be numeric and one result of the method is a judgment in the confidence in an explanatory relationship. Classes of models There are three classes of models used in the analysis of variance, and these are outlined here. Fixedeffects models The fixedeffects model class I of analysis of variance applies to situations in which the experimenter applies one or more treatments to the subjects of the experiment to see whether the response variable values change. This allows the experimenter to estimate the ranges of response variable values that the treatment would generate in the population as a whole. Randomeffects models Randomeffects model class II is used when the treatments are not fixed. This occurs when the various factor levels are sampled from a larger population. Because the levels themselves are random variables, some assumptions and the method of contrasting
the treatments a multivariable generalization of simple differences differ from the fixedeffects model. Mixedeffects models A mixedeffects model class III contains experimental factors of both fixed and randomeffects types, with appropriately different interpretations and analysis for the two types. Example Teaching experiments could be performed by a college or university department to find a good introductory textbook, with each text considered a treatment. The fixedeffects model would compare a list of candidate texts. The randomeffects model would determine whether important differences exist among a list of randomly selected texts. The mixedeffects model would compare the fixed incumbent texts to randomly selected alternatives. Defining fixed and random effects has proven elusive, with competing definitions arguably leading toward a linguistic quagmire. Assumptions The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the res
ponse to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data. Textbook analysis using a normal distribution The analysis of variance can be presented in terms of a linear model, which makes the following assumptions about the probability distribution of the responses Independence of observations this is an assumption of the model that simplifies the statistical analysis. Normality the distributions of the residuals are normal. Equality or "homogeneity" of variances, called homoscedasticity the variance of data in groups should be the same. The separate assumptions of the textbook model imply that the errors are independently, identically, and normally distributed for fixed effects models, that is, that the errors are independent and Randomizationbased analysis In a randomized controlled experiment, the treatments are ra
ndomly assigned to experimental units, following the experimental protocol. This randomization is objective and declared before the experiment is carried out. The objective randomassignment is used to test the significance of the null hypothesis, following the ideas of C. S. Peirce and Ronald Fisher. This designbased analysis was discussed and developed by Francis J. Anscombe at Rothamsted Experimental Station and by Oscar Kempthorne at Iowa State University. Kempthorne and his students make an assumption of unit treatment additivity, which is discussed in the books of Kempthorne and David R. Cox. Unittreatment additivity In its simplest form, the assumption of unittreatment additivity states that the observed response from experimental unit when receiving treatment can be written as the sum of the unit's response and the treatmenteffect , that is The assumption of unittreatment additivity implies that, for every treatment , the th treatment has exactly the same effect on every experiment unit. The
assumption of unit treatment additivity usually cannot be directly falsified, according to Cox and Kempthorne. However, many consequences of treatmentunit additivity can be falsified. For a randomized experiment, the assumption of unittreatment additivity implies that the variance is constant for all treatments. Therefore, by contraposition, a necessary condition for unittreatment additivity is that the variance is constant. The use of unit treatment additivity and randomization is similar to the designbased inference that is standard in finitepopulation survey sampling. Derived linear model Kempthorne uses the randomizationdistribution and the assumption of unit treatment additivity to produce a derived linear model, very similar to the textbook model discussed previously. The test statistics of this derived linear model are closely approximated by the test statistics of an appropriate normal linear model, according to approximation theorems and simulation studies. However, there are differences. For exa
mple, the randomizationbased analysis results in a small but strictly negative correlation between the observations. In the randomizationbased analysis, there is no assumption of a normal distribution and certainly no assumption of independence. On the contrary, the observations are dependent! The randomizationbased analysis has the disadvantage that its exposition involves tedious algebra and extensive time. Since the randomizationbased analysis is complicated and is closely approximated by the approach using a normal linear model, most teachers emphasize the normal linear model approach. Few statisticians object to modelbased analysis of balanced randomized experiments. Statistical models for observational data However, when applied to data from nonrandomized experiments or observational studies, modelbased analysis lacks the warrant of randomization. For observational data, the derivation of confidence intervals must use subjective models, as emphasized by Ronald Fisher and his followers. In practice, t
he estimates of treatmenteffects from observational studies generally are often inconsistent. In practice, "statistical models" and observational data are useful for suggesting hypotheses that should be treated very cautiously by the public. Summary of assumptions The normalmodel based ANOVA analysis assumes the independence, normality and homogeneity of variances of the residuals. The randomizationbased analysis assumes only the homogeneity of the variances of the residuals as a consequence of unittreatment additivity and uses the randomization procedure of the experiment. Both these analyses require homoscedasticity, as an assumption for the normalmodel analysis and as a consequence of randomization and additivity for the randomizationbased analysis. However, studies of processes that change variances rather than means called dispersion effects have been successfully conducted using ANOVA. There are no necessary assumptions for ANOVA in its full generality, but the Ftest used for ANOVA hypothesis test
ing has assumptions and practical limitations which are of continuing interest. Problems which do not satisfy the assumptions of ANOVA can often be transformed to satisfy the assumptions. The property of unittreatment additivity is not invariant under a "change of scale", so statisticians often use transformations to achieve unittreatment additivity. If the response variable is expected to follow a parametric family of probability distributions, then the statistician may specify in the protocol for the experiment or observational study that the responses be transformed to stabilize the variance. Also, a statistician may specify that logarithmic transforms be applied to the responses, which are believed to follow a multiplicative model. According to Cauchy's functional equation theorem, the logarithm is the only continuous transformation that transforms real multiplication to addition. Characteristics ANOVA is used in the analysis of comparative experiments, those in which only the difference in outcomes i
s of interest. The statistical significance of the experiment is determined by a ratio of two variances. This ratio is independent of several possible alterations to the experimental observations Adding a constant to all observations does not alter significance. Multiplying all observations by a constant does not alter significance. So ANOVA statistical significance result is independent of constant bias and scaling errors as well as the units used in expressing observations. In the era of mechanical calculation it was common to subtract a constant from all observations when equivalent to dropping leading digits to simplify data entry. This is an example of data coding. Logic The calculations of ANOVA can be characterized as computing a number of means and variances, dividing two variances and comparing the ratio to a handbook value to determine statistical significance. Calculating a treatment effect is then trivial "the effect of any treatment is estimated by taking the difference between the mean o
f the observations which receive the treatment and the general mean". Partitioning of the sum of squares ANOVA uses traditional standardized terminology. The definitional equation of sample variance is , where the divisor is called the degrees of freedom DF, the summation is called the sum of squares SS, the result is called the mean square MS and the squared terms are deviations from the sample mean. ANOVA estimates 3 sample variances a total variance based on all the observation deviations from the grand mean, an error variance based on all the observation deviations from their appropriate treatment means, and a treatment variance. The treatment variance is based on the deviations of treatment means from the grand mean, the result being multiplied by the number of observations in each treatment to account for the difference between the variance of observations and the variance of means. The fundamental technique is a partitioning of the total sum of squares SS into components related to the effects u
sed in the model. For example, the model for a simplified ANOVA with one type of treatment at different levels. The number of degrees of freedom DF can be partitioned in a similar way one of these components that for error specifies a chisquared distribution which describes the associated sum of squares, while the same is true for "treatments" if there is no treatment effect. See also Lackoffit sum of squares. The Ftest The Ftest is used for comparing the factors of the total deviation. For example, in oneway, or singlefactor ANOVA, statistical significance is tested for by comparing the F test statistic where MS is mean square, is the number of treatments and is the total number of cases to the Fdistribution with , degrees of freedom. Using the Fdistribution is a natural candidate because the test statistic is the ratio of two scaled sums of squares each of which follows a scaled chisquared distribution. The expected value of F is where is the treatment sample size which is 1 for no treatment ef
fect. As values of F increase above 1, the evidence is increasingly inconsistent with the null hypothesis. Two apparent experimental methods of increasing F are increasing the sample size and reducing the error variance by tight experimental controls. There are two methods of concluding the ANOVA hypothesis test, both of which produce the same result The textbook method is to compare the observed value of F with the critical value of F determined from tables. The critical value of F is a function of the degrees of freedom of the numerator and the denominator and the significance level . If F FCritical, the null hypothesis is rejected. The computer method calculates the probability pvalue of a value of F greater than or equal to the observed value. The null hypothesis is rejected if this probability is less than or equal to the significance level . The ANOVA Ftest is known to be nearly optimal in the sense of minimizing false negative errors for a fixed rate of false positive errors i.e. maximizing powe
r for a fixed significance level. For example, to test the hypothesis that various medical treatments have exactly the same effect, the Ftest's pvalues closely approximate the permutation test's pvalues The approximation is particularly close when the design is balanced. Such permutation tests characterize tests with maximum power against all alternative hypotheses, as observed by Rosenbaum. The ANOVA Ftest of the nullhypothesis that all treatments have exactly the same effect is recommended as a practical test, because of its robustness against many alternative distributions. Extended logic ANOVA consists of separable parts; partitioning sources of variance and hypothesis testing can be used individually. ANOVA is used to support other statistical tools. Regression is first used to fit more complex models to data, then ANOVA is used to compare models with the objective of selecting simpler models that adequately describe the data. "Such models could be fit without any reference to ANOVA, but ANOVA tools
could then be used to make some sense of the fitted models, and to test hypotheses about batches of coefficients." "We think of the analysis of variance as a way of understanding and structuring multilevel modelsnot as an alternative to regression but as a tool for summarizing complex highdimensional inferences ..." For a single factor The simplest experiment suitable for ANOVA analysis is the completely randomized experiment with a single factor. More complex experiments with a single factor involve constraints on randomization and include completely randomized blocks and Latin squares and variants GraecoLatin squares, etc.. The more complex experiments share many of the complexities of multiple factors. A relatively complete discussion of the analysis models, data summaries, ANOVA table of the completely randomized experiment is available. There are some alternatives to conventional oneway analysis of variance, e.g. Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and
Winsorized variances, BrownForsythe test, AlexanderGovern test, James second order test and KruskalWallis test, available in onewaytests R It is useful to represent each data point in the following form, called a statistical model where i 1, 2, 3, , R j 1, 2, 3, , C overall average mean j differential effect response associated with the j level of X; this assumes that overall the values of j add to zero that is, ij noise or error associated with the particular ij data value That is, we envision an additive model that says every data point can be represented by summing three quantities the true mean, averaged over all factor levels being investigated, plus an incremental component associated with the particular column factor level, plus a final component associated with everything else affecting that specific data value. For multiple factors ANOVA generalizes to the study of the effects of multiple factors. When the experiment includes observations at all combinations of levels of each fact
or, it is termed factorial. Factorial experiments are more efficient than a series of single factor experiments and the efficiency grows as the number of factors increases. Consequently, factorial designs are heavily used. The use of ANOVA to study the effects of multiple factors has a complication. In a 3way ANOVA with factors x, y and z, the ANOVA model includes terms for the main effects x, y, z and terms for interactions xy, xz, yz, xyz. All terms require hypothesis tests. The proliferation of interaction terms increases the risk that some hypothesis test will produce a false positive by chance. Fortunately, experience says that high order interactions are rare. The ability to detect interactions is a major advantage of multiple factor ANOVA. Testing one factor at a time hides interactions, but produces apparently inconsistent experimental results. Caution is advised when encountering interactions; Test interaction terms first and expand the analysis beyond ANOVA if interactions are found. Te
xts vary in their recommendations regarding the continuation of the ANOVA procedure after encountering an interaction. Interactions complicate the interpretation of experimental data. Neither the calculations of significance nor the estimated treatment effects can be taken at face value. "A significant interaction will often mask the significance of main effects." Graphical methods are recommended to enhance understanding. Regression is often useful. A lengthy discussion of interactions is available in Cox 1958. Some interactions can be removed by transformations while others cannot. A variety of techniques are used with multiple factor ANOVA to reduce expense. One technique used in factorial designs is to minimize replication possibly no replication with support of analytical trickery and to combine groups when effects are found to be statistically or practically insignificant. An experiment with many insignificant factors may collapse into one with a few factors supported by many replications. Ass
ociated analysis Some analysis is required in support of the design of the experiment while other analysis is performed after changes in the factors are formally found to produce statistically significant changes in the responses. Because experimentation is iterative, the results of one experiment alter plans for following experiments. Preparatory analysis The number of experimental units In the design of an experiment, the number of experimental units is planned to satisfy the goals of the experiment. Experimentation is often sequential. Early experiments are often designed to provide meanunbiased estimates of treatment effects and of experimental error. Later experiments are often designed to test a hypothesis that a treatment effect has an important magnitude; in this case, the number of experimental units is chosen so that the experiment is within budget and has adequate power, among other goals. Reporting sample size analysis is generally required in psychology. "Provide information on sample size
and the process that led to sample size decisions." The analysis, which is written in the experimental protocol before the experiment is conducted, is examined in grant applications and administrative review boards. Besides the power analysis, there are less formal methods for selecting the number of experimental units. These include graphical methods based on limiting the probability of false negative errors, graphical methods based on an expected variation increase above the residuals and methods based on achieving a desired confidence interval. Power analysis Power analysis is often applied in the context of ANOVA in order to assess the probability of successfully rejecting the null hypothesis if we assume a certain ANOVA design, effect size in the population, sample size and significance level. Power analysis can assist in study design by determining what sample size would be required in order to have a reasonable chance of rejecting the null hypothesis when the alternative hypothesis is true. Effect
size Several standardized measures of effect have been proposed for ANOVA to summarize the strength of the association between a predictors and the dependent variable or the overall standardized difference of the complete model. Standardized effectsize estimates facilitate comparison of findings across studies and disciplines. However, while standardized effect sizes are commonly used in much of the professional literature, a nonstandardized measure of effect size that has immediately "meaningful" units may be preferable for reporting purposes. Model confirmation Sometimes tests are conducted to determine whether the assumptions of ANOVA appear to be violated. Residuals are examined or analyzed to confirm homoscedasticity and gross normality. Residuals should have the appearance of zero mean normal distribution noise when plotted as a function of anything including time and modeled data values. Trends hint at interactions among factors or among observations. Followup tests A statistically significant ef
fect in ANOVA is often followed by additional tests. This can be done in order to assess which groups are different from which other groups or to test various other focused hypotheses. Followup tests are often distinguished in terms of whether they are "planned" a priori or "post hoc." Planned tests are determined before looking at the data, and post hoc tests are conceived only after looking at the data though the term "post hoc" is inconsistently used. The followup tests may be "simple" pairwise comparisons of individual group means or may be "compound" comparisons e.g., comparing the mean pooling across groups A, B and C to the mean of group D. Comparisons can also look at tests of trend, such as linear and quadratic relationships, when the independent variable involves ordered levels. Often the followup tests incorporate a method of adjusting for the multiple comparisons problem. Study designs There are several types of ANOVA. Many statisticians base ANOVA on the design of the experiment, especially on
the protocol that specifies the random assignment of treatments to subjects; the protocol's description of the assignment mechanism should include a specification of the structure of the treatments and of any blocking. It is also common to apply ANOVA to observational data using an appropriate statistical model. Some popular designs use the following types of ANOVA Oneway ANOVA is used to test for differences among two or more independent groups means, e.g. different levels of urea application in a crop, or different levels of antibiotic action on several different bacterial species, or different levels of effect of some medicine on groups of patients. However, should these groups not be independent, and there is an order in the groups such as mild, moderate and severe disease, or in the dose of a drug such as 5 mgmL, 10 mgmL, 20 mgmL given to the same group of patients, then a linear trend estimation should be used. Typically, however, the oneway ANOVA is used to test for differences among at least three gr
oups, since the twogroup case can be covered by a ttest. When there are only two means to compare, the ttest and the ANOVA Ftest are equivalent; the relation between ANOVA and t is given by . Factorial ANOVA is used when there is more than one factor. Repeated measures ANOVA is used when the same subjects are used for each factor e.g., in a longitudinal study. Multivariate analysis of variance MANOVA is used when there is more than one response variable. Cautions Balanced experiments those with an equal sample size for each treatment are relatively easy to interpret; unbalanced experiments offer more complexity. For singlefactor oneway ANOVA, the adjustment for unbalanced data is easy, but the unbalanced analysis lacks both robustness and power. For more complex designs the lack of balance leads to further complications. "The orthogonality property of main effects and interactions present in balanced data does not carry over to the unbalanced case. This means that the usual analysis of variance techniques
do not apply. Consequently, the analysis of unbalanced factorials is much more difficult than that for balanced designs." In the general case, "The analysis of variance can also be applied to unbalanced data, but then the sums of squares, mean squares, and Fratios will depend on the order in which the sources of variation are considered." ANOVA is in part a test of statistical significance. The American Psychological Association and many other organisations holds the view that simply reporting statistical significance is insufficient and that reporting confidence bounds is preferred. Generalizations ANOVA is considered to be a special case of linear regression which in turn is a special case of the general linear model. All consider the observations to be the sum of a model fit and a residual error to be minimized. The KruskalWallis test and the Friedman test are nonparametric tests, which do not rely on an assumption of normality. Connection to linear regression Below we make clear the connection betw
een multiway ANOVA and linear regression. Linearly reorder the data so that th observation is associated with a response and factors where denotes the different factors and is the total number of factors. In oneway ANOVA and in twoway ANOVA . Furthermore, we assume the th factor has levels, namely . Now, we can onehot encode the factors into the dimensional vector . The onehot encoding function is defined such that the th entry of is The vector is the concatenation of all of the above vectors for all . Thus, . In order to obtain a fully general way interaction ANOVA we must also concatenate every additional interaction term in the vector and then add an intercept term. Let that vector be . With this notation in place, we now have the exact connection with linear regression. We simply regress response against the vector . However, there is a concern about identifiability. In order to overcome such issues we assume that the sum of the parameters within each set of interactions is equal to zero.
From here, one can use Fstatistics or other methods to determine the relevance of the individual factors. Example We can consider the 2way interaction example where we assume that the first factor has 2 levels and the second factor has 3 levels. Define if and if , i.e. is the onehot encoding of the first factor and is the onehot encoding of the second factor. With that, where the last term is an intercept term. For a more concrete example suppose that Then, See also ANOVA on ranks ANOVAsimultaneous component analysis Analysis of covariance ANCOVA Analysis of molecular variance AMOVA Analysis of rhythmic variance ANORVA Explained variation Linear trend estimation Mixeddesign analysis of variance Multivariate analysis of covariance MANCOVA Permutational analysis of variance Variance decomposition Expected mean squares Footnotes Notes References Prepublication chapters are available online. Cohen, Jacob 1988. Statistical power analysis for the behavior sciences 2nd ed.. Routledge Cox,
David R. 1958. Planning of experiments. Reprinted as Freedman, David A.2005. Statistical Models Theory and Practice, Cambridge University Press. Lehmann, E.L. 1959 Testing Statistical Hypotheses. John Wiley Sons. Moore, David S. McCabe, George P. 2003. Introduction to the Practice of Statistics 4e. W H Freeman Co. Rosenbaum, Paul R. 2002. Observational Studies 2nd ed.. New York SpringerVerlag. Further reading Cox, David R. Reid, Nancy M. 2000. The theory of design of experiments. Chapman HallCRC. Freedman, David A.; Pisani, Robert; Purves, Roger 2007 Statistics, 4th edition. W.W. Norton Company Tabachnick, Barbara G. Fidell, Linda S. 2007. Using Multivariate Statistics 5th ed.. Boston Pearson International Edition. External links SOCR ANOVA Activity Examples of all ANOVA and ANCOVA models with up to three treatment factors, including randomized block, split plot, repeated measures, and Latin squares, and their analysis in R University of Southampton
NISTSEMATECH eHandbook of Statistical Methods, section 7.4.3 "Are the means equal?" Analysis of variance Introduction Design of experiments Statistical tests Parametric statistics
In organic chemistry, an alkane, or paraffin a historical trivial name that also has other meanings, is an acyclic saturated hydrocarbon. In other words, an alkane consists of hydrogen and carbon atoms arranged in a tree structure in which all the carboncarbon bonds are single. Alkanes have the general chemical formula . The alkanes range in complexity from the simplest case of methane , where n 1 sometimes called the parent molecule, to arbitrarily large and complex molecules, like pentacontane or 6ethyl2methyl51methylethyl octane, an isomer of tetradecane . The International Union of Pure and Applied Chemistry IUPAC defines alkanes as "acyclic branched or unbranched hydrocarbons having the general formula , and therefore consisting entirely of hydrogen atoms and saturated carbon atoms". However, some sources use the term to denote any saturated hydrocarbon, including those that are either monocyclic i.e. the cycloalkanes or polycyclic, despite their having a distinct general formula i.e. cycloalkanes are
. In an alkane, each carbon atom is sp3hybridized with 4 sigma bonds either CC or CH, and each hydrogen atom is joined to one of the carbon atoms in a CH bond. The longest series of linked carbon atoms in a molecule is known as its carbon skeleton or carbon backbone. The number of carbon atoms may be considered as the size of the alkane. One group of the higher alkanes are waxes, solids at standard ambient temperature and pressure SATP, for which the number of carbon atoms in the carbon backbone is greater than about 17. With their repeated units, the alkanes constitute a homologous series of organic compounds in which the members differ in molecular mass by multiples of 14.03 u the total mass of each such methylenebridge unit, which comprises a single carbon atom of mass 12.01 u and two hydrogen atoms of mass 1.01 u each. Methane is produced by methanogenic bacteria and some longchain alkanes function as pheromones in certain animal species or as protective waxes in plants and fungi. Nevertheless, most
alkanes do not have much biological activity. They can be viewed as molecular trees upon which can be hung the more activereactive functional groups of biological molecules. The alkanes have two main commercial sources petroleum crude oil and natural gas. An alkyl group is an alkanebased molecular fragment that bears one open valence for bonding. They are generally abbreviated with the symbol for any organyl group, R, although Alk is sometimes used to specifically symbolize an alkyl group as opposed to an alkenyl group or aryl group. Structure and classification Ordinarily the CC single bond distance is . Saturated hydrocarbons can be linear, branched, or cyclic. The third group is sometimes called cycloalkanes. Very complicated structures are possible by combining linear, branch, cyclic alkanes. Isomerism Alkanes with more than three carbon atoms can be arranged in various ways, forming structural isomers. The simplest isomer of an alkane is the one in which the carbon atoms are arranged in a single c
hain with no branches. This isomer is sometimes called the nisomer n for "normal", although it is not necessarily the most common. However, the chain of carbon atoms may also be branched at one or more points. The number of possible isomers increases rapidly with the number of carbon atoms. For example, for acyclic alkanes C1 methane only C2 ethane only C3 propane only C4 2 isomers butane and isobutane C5 3 isomers pentane, isopentane, and neopentane C6 5 isomers hexane, 2methylpentane, 3methylpentane, 2,2dimethylbutane, and 2,3dimethylbutane C7 9 isomers heptane, methylhexane 2 isomers, dimethylpentane 4 isomers, 3ethylpentane, 2,2,3trimethylbutane C8 18 isomers octane, 2methylheptane, 3methylheptane, 2,3dimethylhexane, 3,4dimethylhexane, 2,3,4trimethylpentane, 3,3dimethylhexane, 2,2trimethylpentane, 2,4dimethylhexane, 2,2,4trimethylpentane, 2,3,3Trimethylpentane, 3,3,4trimethylpentane, 3,4,4trimethylpentane, 2,4,4trimethylpentane, 5 isomers C9 35 isomers C10 75 isomers C12 355 isomers C32 27,711
,253,769 isomers C60 22,158,734,535,770,411,074,184 isomers, many of which are not stable. Branched alkanes can be chiral. For example, 3methylhexane and its higher homologues are chiral due to their stereogenic center at carbon atom number 3. The above list only includes differences of connectivity, not stereochemistry. In addition to the alkane isomers, the chain of carbon atoms may form one or more rings. Such compounds are called cycloalkanes, and are also excluded from the above list because changing the number of rings changes the molecular formula. For example, cyclobutane and methylcyclopropane are isomers of each other C4H8, but are not isomers of butane C4H10. Nomenclature The IUPAC nomenclature systematic way of naming compounds for alkanes is based on identifying hydrocarbon chains. Unbranched, saturated hydrocarbon chains are named systematically with a Greek numerical prefix denoting the number of carbons and the suffix "ane". In 1866, August Wilhelm von Hofmann suggested systematizing nome
nclature by using the whole sequence of vowels a, e, i, o and u to create suffixes ane, ene, ine or yne, one, une, for the hydrocarbons CnH2n2, CnH2n, CnH2n2, CnH2n4, CnH2n6. In modern nomenclature, the first three specifically name hydrocarbons with single, double and triple bonds; while "one" now represents a ketone. Linear alkanes Straightchain alkanes are sometimes indicated by the prefix "n" or "n"for "normal" where a nonlinear isomer exists. Although this is not strictly necessary and is not part of the IUPAC naming system, the usage is still common in cases where one wishes to emphasize or distinguish between the straightchain and branchedchain isomers, e.g., "nbutane" rather than simply "butane" to differentiate it from isobutane. Alternative names for this group used in the petroleum industry are linear paraffins or nparaffins. The first six members of the series in terms of number of carbon atoms are named as follows methane CH4 one carbon and 4 hydrogen ethane C2H6 two carbon and 6 hydrogen
propane C3H8 three carbon and 8 hydrogen butane C4H10 four carbon and 10 hydrogen pentane C5H12 five carbon and 12 hydrogen hexane C6H14 six carbon and 14 hydrogen The first four names were derived from methanol, ether, propionic acid and butyric acid. Alkanes with five or more carbon atoms are named by adding the suffix ane to the appropriate numerical multiplier prefix with elision of any terminal vowel a or o from the basic numerical term. Hence, pentane, C5H12; hexane, C6H14; heptane, C7H16; octane, C8H18; etc. The numeral prefix is generally Greek, however alkanes with a carbon atom count ending in nine, for example nonane, use the Latin prefix non. For a more complete list, see list of straightchain alkanes. Branched alkanes Simple branched alkanes often have a common name using a prefix to distinguish them from linear alkanes, for example npentane, isopentane, and neopentane. IUPAC naming conventions can be used to produce a systematic name. The key steps in the naming of more complicat
ed branched alkanes are as follows Identify the longest continuous chain of carbon atoms Name this longest root chain using standard naming rules Name each side chain by changing the suffix of the name of the alkane from "ane" to "yl" Number the longest continuous chain in order to give the lowest possible numbers for the sidechains Number and name the side chains before the name of the root chain If there are multiple side chains of the same type, use prefixes such as "di" and "tri" to indicate it as such, and number each one. Add side chain names in alphabetical disregarding "di" etc. prefixes order in front of the name of the root chain Saturated cyclic hydrocarbons Though technically distinct from the alkanes, this class of hydrocarbons is referred to by some as the "cyclic alkanes." As their description implies, they contain one or more rings. Simple cycloalkanes have a prefix "cyclo" to distinguish them from alkanes. Cycloalkanes are named as per their acyclic counterparts with respect to the
number of carbon atoms in their backbones, e.g., cyclopentane C5H10 is a cycloalkane with 5 carbon atoms just like pentane C5H12, but they are joined up in a fivemembered ring. In a similar manner, propane and cyclopropane, butane and cyclobutane, etc. Substituted cycloalkanes are named similarly to substituted alkanes the cycloalkane ring is stated, and the substituents are according to their position on the ring, with the numbering decided by the CahnIngoldPrelog priority rules. Trivialcommon names The trivial nonsystematic name for alkanes is 'paraffins'. Together, alkanes are known as the 'paraffin series'. Trivial names for compounds are usually historical artifacts. They were coined before the development of systematic names, and have been retained due to familiar usage in industry. Cycloalkanes are also called naphthenes. Branchedchain alkanes are called isoparaffins. "Paraffin" is a general term and often does not distinguish between pure compounds and mixtures of isomers, i.e., compounds of the
same chemical formula, e.g., pentane and isopentane. In IUPAC The following trivial names are retained in the IUPAC system isobutane for 2methylpropane isopentane for 2methylbutane neopentane for 2,2dimethylpropane. NonIUPAC Some nonIUPAC trivial names are occasionally used cetane, for hexadecane cerane, for hexacosane Physical properties All alkanes are colorless. Alkanes with the lowest molecular weights are gasses, those of intermediate molecular weight are liquids, and the heaviest are waxy solids. Table of alkanes Boiling point Alkanes experience intermolecular van der Waals forces. Stronger intermolecular van der Waals forces give rise to greater boiling points of alkanes. There are two determinants for the strength of the van der Waals forces the number of electrons surrounding the molecule, which increases with the alkane's molecular weight the surface area of the molecule Under standard conditions, from CH4 to C4H10 alkanes are gaseous; from C5H12 to C17H36 they are liquids; and after
C18H38 they are solids. As the boiling point of alkanes is primarily determined by weight, it should not be a surprise that the boiling point has almost a linear relationship with the size molecular weight of the molecule. As a rule of thumb, the boiling point rises 2030 C for each carbon added to the chain; this rule applies to other homologous series. A straightchain alkane will have a boiling point higher than a branchedchain alkane due to the greater surface area in contact, thus the greater van der Waals forces, between adjacent molecules. For example, compare isobutane 2methylpropane and nbutane butane, which boil at 12 and 0 C, and 2,2dimethylbutane and 2,3dimethylbutane which boil at 50 and 58 C, respectively. On the other hand, cycloalkanes tend to have higher boiling points than their linear counterparts due to the locked conformations of the molecules, which give a plane of intermolecular contact. Melting points The melting points of the alkanes follow a similar trend to boiling points for the
same reason as outlined above. That is, all other things being equal the larger the molecule the higher the melting point. There is one significant difference between boiling points and melting points. Solids have more rigid and fixed structure than liquids. This rigid structure requires energy to break down. Thus the better put together solid structures will require more energy to break apart. For alkanes, this can be seen from the graph above i.e., the blue line. The oddnumbered alkanes have a lower trend in melting points than even numbered alkanes. This is because even numbered alkanes pack well in the solid phase, forming a wellorganized structure, which requires more energy to break apart. The oddnumbered alkanes pack less well and so the "looser" organized solid packing structure requires less energy to break apart. For a visualization of the crystal structures see. The melting points of branchedchain alkanes can be either higher or lower than those of the corresponding straightchain alkanes, again de
pending on the ability of the alkane in question to pack well in the solid phase. Conductivity and solubility Alkanes do not conduct electricity in any way, nor are they substantially polarized by an electric field. For this reason, they do not form hydrogen bonds and are insoluble in polar solvents such as water. Since the hydrogen bonds between individual water molecules are aligned away from an alkane molecule, the coexistence of an alkane and water leads to an increase in molecular order a reduction in entropy. As there is no significant bonding between water molecules and alkane molecules, the second law of thermodynamics suggests that this reduction in entropy should be minimized by minimizing the contact between alkane and water Alkanes are said to be hydrophobic as they are insoluble in water. Their solubility in nonpolar solvents is relatively high, a property that is called lipophilicity. Alkanes are, for example, miscible in all proportions among themselves. The density of the alkanes usually in
creases with the number of carbon atoms but remains less than that of water. Hence, alkanes form the upper layer in an alkanewater mixture. Molecular geometry The molecular structure of the alkanes directly affects their physical and chemical characteristics. It is derived from the electron configuration of carbon, which has four valence electrons. The carbon atoms in alkanes are described as sp3 hybrids, that is to say that, to a good approximation, the valence electrons are in orbitals directed towards the corners of a tetrahedron which are derived from the combination of the 2s orbital and the three 2p orbitals. Geometrically, the angle between the bonds are cos1 109.47. This is exact for the case of methane, while larger alkanes containing a combination of CH and CC bonds generally have bonds that are within several degrees of this idealized value. Bond lengths and bond angles An alkane has only CH and CC single bonds. The former result from the overlap of an sp3 orbital of carbon with the 1s orbita
l of a hydrogen; the latter by the overlap of two sp3 orbitals on adjacent carbon atoms. The bond lengths amount to 1.09 1010 m for a CH bond and 1.54 1010 m for a CC bond. The spatial arrangement of the bonds is similar to that of the four sp3 orbitalsthey are tetrahedrally arranged, with an angle of 109.47 between them. Structural formulae that represent the bonds as being at right angles to one another, while both common and useful, do not accurately depict the geometry. Conformation The structural formula and the bond angles are not usually sufficient to completely describe the geometry of a molecule. There is a further degree of freedom for each carboncarbon bond the torsion angle between the atoms or groups bound to the atoms at each end of the bond. The spatial arrangement described by the torsion angles of the molecule is known as its conformation. Ethane forms the simplest case for studying the conformation of alkanes, as there is only one CC bond. If one looks down the axis of the CC bond, one
will see the socalled Newman projection. The hydrogen atoms on both the front and rear carbon atoms have an angle of 120 between them, resulting from the projection of the base of the tetrahedron onto a flat plane. However, the torsion angle between a given hydrogen atom attached to the front carbon and a given hydrogen atom attached to the rear carbon can vary freely between 0 and 360. This is a consequence of the free rotation about a carboncarbon single bond. Despite this apparent freedom, only two limiting conformations are important eclipsed conformation and staggered conformation. The two conformations differ in energy the staggered conformation is 12.6 kJmol 3.0 kcalmol lower in energy more stable than the eclipsed conformation the least stable. This difference in energy between the two conformations, known as the torsion energy, is low compared to the thermal energy of an ethane molecule at ambient temperature. There is constant rotation about the CC bond. The time taken for an ethane molecule to p
ass from one staggered conformation to the next, equivalent to the rotation of one CH3 group by 120 relative to the other, is of the order of 1011 seconds. The case of higher alkanes is more complex but based on similar principles, with the antiperiplanar conformation always being the most favored around each carboncarbon bond. For this reason, alkanes are usually shown in a zigzag arrangement in diagrams or in models. The actual structure will always differ somewhat from these idealized forms, as the differences in energy between the conformations are small compared to the thermal energy of the molecules Alkane molecules have no fixed structural form, whatever the models may suggest. Spectroscopic properties Virtually all organic compounds contain carboncarbon, and carbonhydrogen bonds, and so show some of the features of alkanes in their spectra. Alkanes are notable for having no other groups, and therefore for the absence of other characteristic spectroscopic features of a functional group like OH, CHO,
COOH etc. Infrared spectroscopy The carbonhydrogen stretching mode gives a strong absorption between 2850 and 2960 cm1, while the carboncarbon stretching mode absorbs between 800 and 1300 cm1. The carbonhydrogen bending modes depend on the nature of the group methyl groups show bands at 1450 cm1 and 1375 cm1, while methylene groups show bands at 1465 cm1 and 1450 cm1. Carbon chains with more than four carbon atoms show a weak absorption at around 725 cm1. NMR spectroscopy The proton resonances of alkanes are usually found at H 0.51.5. The carbon13 resonances depend on the number of hydrogen atoms attached to the carbon C 830 primary, methyl, CH3, 1555 secondary, methylene, CH2, 2060 tertiary, methyne, CH and quaternary. The carbon13 resonance of quaternary carbon atoms is characteristically weak, due to the lack of nuclear Overhauser effect and the long relaxation time, and can be missed in weak samples, or samples that have not been run for a sufficiently long time. Mass spectrometry Alkanes have a hig
h ionization energy, and the molecular ion is usually weak. The fragmentation pattern can be difficult to interpret, but, in the case of branched chain alkanes, the carbon chain is preferentially cleaved at tertiary or quaternary carbons due to the relative stability of the resulting free radicals. The fragment resulting from the loss of a single methyl group M 15 is often absent, and other fragments are often spaced by intervals of fourteen mass units, corresponding to sequential loss of CH2 groups. Chemical properties Alkanes are only weakly reactive with most chemical compounds. The acid dissociation constant pKa values of all alkanes are estimated to range from 50 to 70, depending on the extrapolation method, hence they are extremely weak acids that are practically inert to bases see carbon acids. They are also extremely weak bases, undergoing no observable protonation in pure sulfuric acid H0 12, although superacids that are at least millions of times stronger have been known to protonate them to giv
e hypercoordinate alkanium ions see methanium ion. Similarly, they only show reactivity with the strongest of electrophilic reagents e.g., dioxiranes and salts containing the NF4 cation. By virtue of their strongly CH bonds 100 kcalmol and CC bonds 90 kcalmol, but usually less sterically accessible, they are also relatively unreactive toward free radicals, although many electrondeficient radicals will react with alkanes in the absence of other electronrich bonds see below. This inertness is the source of the term paraffins with the meaning here of "lacking affinity". In crude oil the alkane molecules have remained chemically unchanged for millions of years. Free radicals, molecules with unpaired electrons, play a large role in most reactions of alkanes, such as cracking and reformation where longchain alkanes are converted into shorterchain alkanes and straightchain alkanes into branchedchain isomers. Moreover, redox reactions of alkanes involving free radical intermediates, in particular with oxygen and t
he halogens, are possible as the carbon atoms are in a strongly reduced state; in the case of methane, carbon is in its lowest possible oxidation state 4. Reaction with oxygen if present in sufficient quantity to satisfy the reaction stoichiometry leads to combustion without any smoke, producing carbon dioxide and water. Free radical halogenation reactions occur with halogens, leading to the production of haloalkanes. In addition, alkanes have been shown to interact with, and bind to, certain transition metal complexes in CH bond activation reactions. In highly branched alkanes, the bond angle may differ significantly from the optimal value 109.5 to accommodate bulky groups. Such distortions introduce a tension in the molecule, known as steric hindrance or strain. Strain substantially increases reactivity. However, in general and perhaps surprisingly, when branching is not extensive enough to make highly disfavorable 1,2 and 1,3alkylalkyl steric interactions worth 3.1 kcalmol and 3.7 kcalmol in the case of
the eclipsing conformations of butane and pentane, respectively unavoidable, the branched alkanes are actually more thermodynamically stable than their linear or less branched isomers. For example, the highly branched 2,2,3,3tetramethylbutane is about 1.9 kcalmol more stable than its linear isomer, noctane. Due to the subtlety of this effect, the exact reasons for this rule have been vigorously debated in the chemical literature and is yet unsettled. Several explanations, including stabilization of branched alkanes by electron correlation, destabilization of linear alkanes by steric repulsion, stabilization by neutral hyperconjugation, andor electrostatic effects have been advanced as possibilities. The controversy is related to the question of whether the traditional explanation of hyperconjugation is the primary factor governing the stability of alkyl radicals. Reactions with oxygen combustion reaction All alkanes react with oxygen in a combustion reaction, although they become increasingly difficult to
ignite as the number of carbon atoms increases. The general equation for complete combustion is CnH2n2 n O2 n 1 H2O n CO2 or CnH2n2 O2 n 1 H2O n CO2 In the absence of sufficient oxygen, carbon monoxide or even soot can be formed, as shown below CnH2n2 n O2 n 1 H2O n CO CnH2n2 n O2 n 1 H2O n C For example, methane 2 CH4 3 O2 4 H2O 2 CO CH4 O2 2 H2O C See the alkane heat of formation table for detailed data. The standard enthalpy change of combustion, cH, for alkanes increases by about 650 kJmol per CH2 group. Branchedchain alkanes have lower values of cH than straightchain alkanes of the same number of carbon atoms, and so can be seen to be somewhat more stable. Reactions with halogens Alkanes react with halogens in a socalled free radical halogenation reaction. The hydrogen atoms of the alkane are progressively replaced by halogen atoms. Free radicals are the reactive species that participate in the reaction, which usually leads to a mixture of products. The reaction is h
ighly exothermic with halogen fluorine and can lead to an explosion. These reactions are an important industrial route to halogenated hydrocarbons. There are three steps Initiation the halogen radicals form by homolysis. Usually, energy in the form of heat or light is required. Chain reaction or Propagation then takes placethe halogen radical abstracts a hydrogen from the alkane to give an alkyl radical. This reacts further. Chain termination where the radicals recombine. Experiments have shown that all halogenation produces a mixture of all possible isomers, indicating that all hydrogen atoms are susceptible to reaction. The mixture produced, however, is not a statistical mixture Secondary and tertiary hydrogen atoms are preferentially replaced due to the greater stability of secondary and tertiary freeradicals. An example can be seen in the monobromination of propane Cracking Cracking breaks larger molecules into smaller ones. This can be done with a thermal or catalytic method. The thermal cracking

Subsets and Splits

No saved queries yet

Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.