MétaCan
Menu
Back to cohort
Record W2262200787 · doi:10.1353/vcr.2014.0040

The Accidental Discovery of Mauve

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

Bibliographic record

VenueVictorian review · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsAmateurChemistAccidentalGermanGuanoHistoryChemistryArchaeologyOrganic chemistry

Abstract

fetched live from 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...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.224
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueVictorian reviewSame topicAmerican Sports and LiteratureFrench-language works237,207