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Record W2115538642 · doi:10.1039/a905324g

Simultaneous pH–rate profiles applied to the two step consecutive sequence A → B → C : a theoretical analysis and experimental verification †

2000· article· en· W2115538642 on OpenAlexafffund
John Andraos, Edward C. Lathioor, William J. Leigh

Bibliographic record

VenueJournal of the Chemical Society Perkin Transactions 2 · 2000
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsMcMaster UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsPerchloric acidAmbiguityChemistryReaction rate constantAqueous solutionAbsorbanceContext (archaeology)Reaction rateKinetic energyBase (topology)ThermodynamicsCatalysisInorganic chemistryPhysical chemistryKineticsChromatographyComputer scienceMathematicsOrganic chemistryPhysicsMathematical analysisClassical mechanics

Abstract

fetched live from OpenAlex

The absorbance extremum method introduced to resolve the rate constant–reaction step ambiguity problem in two step consecutive reactions is applied to cases where the rate constant in each step is pseudo first order and dependent on catalyst concentration. In particular, acid and base catalyzed reactions of substrates in aqueous solution are examined. Resultant simultaneous pH–rate profile functions are treated in the context of the ambiguity problem and a detailed account of the possible interactions between pH–rate profiles is also given. The merits of this method include its general scope, its applicability to reactions for which it may be experimentally difficult to resolve the ambiguity assignment by other means, and its use of rate data acquired from original kinetic traces without requiring additional information. The theoretical analysis is verified and tested experimentally for the hydration of fluorenylideneketene in dilute aqueous perchloric acid solutions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2000
Admission routes2
Has abstractyes

Explore more

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