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Record W1731380894 · doi:10.1039/c5cp03517a

Mode-specific fragmentation of amino acid-containing clusters

2015· article· en· W1731380894 on OpenAlexafffund
W. Scott Hopkins, Rick A. Marta, Vincent Steinmetz, Terry B. McMahon

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsFragmentation (computing)IsomerizationChemistryCluster (spacecraft)Amino acidPhotochemistryStereochemistryOrganic chemistryBiochemistryBiologyCatalysisComputer science

Abstract

fetched live from OpenAlex

A combination of infrared multiple photon dissociation (IRMPD) spectroscopy and density functional theory calculations have been employed to study the structures and mode-specific dissociation pathways of the proton-bound dimer of 3-trifluoromethylphenylalanine (3-CF3-Phe) and trimethylamine (TMA). Three structural motifs are identified: canonical (charge-solvated), zwitterionic (charge-separated), and TMA-bridged. In the 1000-1350 cm(-1) region, similar spectra are observed in the TMA·H(+) and 3-CF3-Phe·H(+) product channels. At wavenumbers above 1350 cm(-1), infrared excitation of charge-solvated structures leads exclusively to production of protonated TMA, while excitation of zwitterionic or TMA-bridged structures results exclusively in production of protonated 3-CF3-Phe. The cluster potential energy landscape is topologically mapped and mechanisms for isomerization and mode-selective dissociation are proposed. In particular, cluster transparency as a result of IR-induced isomerization is implicated in deactivation of some IRMPD channels.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.284
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations27
Published2015
Admission routes2
Has abstractyes

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