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Record W2118860392 · doi:10.1039/c5cp00580a

Guanine tetrads: an IRMPD spectroscopy, energy resolved SORI-CID, and computational study of M(9-ethylguanine)<sub>4</sub><sup>+</sup>(M = Li, Na, K, Rb, Cs) in the gas phase

2015· article· en· W2118860392 on OpenAlexafffund
Mohammad Azargun, Travis D. Fridgen

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2015
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryTetradInfrared multiphoton dissociationGuaninePotential energySpectroscopyGas phaseTranslational energyComputational chemistryAnalytical Chemistry (journal)Atomic physicsCrystallographyMolecular physicsPhysical chemistryPhysicsOrganic chemistryDissociation (chemistry)

Abstract

fetched live from OpenAlex

The intrinsic properties of the alkali metal cationized G-tetrads, M(9eG)4(+) (M = Li, Na, K, Rb, Cs) composed of 9-ethylguanine (9eG), were studied by a combination of mass spectrometric techniques. The gas phase structures were probed by infrared multiple photon dissociation (IRMPD) spectroscopy in both the fingerprint region (900-1850 cm(-1)) and the N-H/C-H stretching region (2700-3800 cm(-1)). The gas phase structures were found to be similar for all five complexes and most consistent with the metal-centred G-tetrad structure. Energy-resolved CID was also used to compare the gas phase stabilities of the G-tetrads and showed that Na(9eG)4(+) was more stable than Li(9eG)4(+) followed by the K(+), Rb(+), and Cs(+) G-tetrads in order. The experimental energy ordering was reproduced by electronic structure calculations of the energies. Furthermore, the computations also showed that the lower stability to loss of 9-ethylguanine for the Li(+) complex could be due to a strong destabilization of the neutral G-tetrad due to the persistence toward maximizing the ion-dipole interactions while also maintaining hydrogen bonding interactions.

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 categoriesMeta-epidemiology (narrow)
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 score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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

Citations33
Published2015
Admission routes2
Has abstractyes

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