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Enregistrement W2262200787 · doi:10.1353/vcr.2014.0040

The Accidental Discovery of Mauve

2014· article· en· W2262200787 sur OpenAlexvenueno aff
Anthony S. Travis

Notice bibliographique

RevueVictorian review · 2014
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueAmerican Sports and Literature
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAmateurChemistAccidentalGermanGuanoHistoryChemistryArchaeologyOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

The Accidental Discovery of Mauve Anthony S. Travis (bio) An accident can, in the hands of amateur experts, lead, as an unintended consequence, to new ways of doing and making things. One such instance occurred in the East End of London during the Easter of 1856. There, in a makeshift laboratory in the loft of his parents’ house, a teenaged [End Page 34] chemist discovered, by chance, a purple dyestuff that would pave the way for a scientific and manufacturing revolution based on chemistry. The young man was William Henry Perkin (1838–1907), assistant to the German chemist (August) Wilhelm von Hofman, then head of the Royal College of Chemistry, in London’s Oxford Street. Hofman, a former student of Justus von Liebig, of meat-extract fame, directed the college from its opening in 1845. This institution was backed by British agriculturalists interested in improved crop yields based on scientific studies of fertilizers such as guano, the bird excrement imported from Peru. However, while no significant contribution toward agriculture was forthcoming, Hofmann did investigate a group of chemicals that he hoped could serve other human needs. These chemicals had been isolated from coal tar, the viscous, oily waste from the manufacture of the coal gas that lit the streets and lanes of the metropolis. Coal tar was an unsightly inconvenience that was often dumped in local rivers. Hofmann’s compounds were members of the aromatic series, in particular the amino compounds. The latter were present as minor components in the tar. From the early 1850s, they were prepared in the laboratory, in two steps, from the abundant tar-derived hydrocarbon benzene. Their chemical structures were unknown and presented, on the basis of known combining powers (valencies) of atoms, a major puzzle to chemists. However, this did not prevent speculation about how such aromatic compounds might be used. All that was known was that the aromatic amino compounds contained the atoms carbon, hydrogen, and nitrogen (the amino component is made up of the chemical grouping of one nitrogen atom and two hydrogen atoms). This was enough to suggest that, on the basis of analysis for the ratios of chemical elements, synthesis of useful compounds might be achieved by applying known reactions of the amino group. Hofmann surmised that one such aromatic amine might, by a process of condensation of two of its molecules, be converted into synthetic quinine, a product much wanted by colonial administrators, explorers, and others engaged in distant lands where malaria struck. This was the challenge taken up by the eighteen-year-old William Perkin. After studying at the City of London School, he enrolled at Hofmann’s college, where, by around 1855, he was promoted to assistant in the professor’s private laboratory. There, Perkin studied the aromatic amino compounds and heard Hofmann discuss their possible uses outside the laboratory. Practical applications of chemistry were important for enhancing the status of the discipline and its practitioners, as well as for ensuring much-needed funding for the college, which had fallen upon hard times once the agriculturalists realized that no breakthroughs had been forthcoming. Despite limitations in available scientific knowledge—which in retrospect was a distinct advantage—and many other uncertainties, during the 1856 Easter break, Perkin undertook an experiment to synthesize quinine, starting, [End Page 35] as Hofmann had suggested, with an amine derived from coal-tar naphthalene. It failed miserably. Undaunted, Perkin made use of his scientific training and decided to find out why the reaction did not work, repeating the method with the simplest aromatic amine, aniline. Again, an unpromising mixture resulted. Perkin could have thrown it down the drain and spent the rest of Easter with his family and friends in the East End. Instead he treated the dark oily mixture with alcohol, hoping to extract from it a compound that might provide answers to the nature of the reaction. The result was a brilliant purple solution, something not altogether unusual, since strongly coloured solutions often resulted from chemical reactions and were worthy of note, even if not of practical application outside the laboratory or lecture hall. While Perkin was manipulating the solution an accident occurred, though we do not have the exact...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,930
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,224
Écart entre enseignants0,215 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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