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Record W2331790998 · doi:10.1021/jp412742u

Molecular-Level Surface Structure from Nonlinear Vibrational Spectroscopy Combined with Simulations

2014· article· en· W2331790998 on OpenAlexaff
Shaun A. Hall, Kailash C. Jena, Paul A. Covert, Sandra Roy, Travis G. Trudeau, Dennis K. Hore

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsChromophoreMoleculeSum-frequency generationSpectroscopyChemical physicsMolecular vibrationInfrared spectroscopyAdsorptionMaterials scienceSum frequency generation spectroscopyMolecular dynamicsSensitivity (control systems)Computational chemistryChemistryMolecular physicsNonlinear systemNonlinear opticsPhysical chemistryPhotochemistryPhysicsOrganic chemistryElectronic engineering

Abstract

fetched live from OpenAlex

Vibrational sum-frequency generation spectroscopy is valued for its ability to selectively probe molecules at a variety of interfaces without the use of extrinsic chromophores. The spectra contain valuable information regarding the molecular structure and the interfacial environment through the observation of vibrational resonances associated with specific moieties. Chemical information is obtained by close inspection of the frequencies of these bands and the amplitude of the response under conditions of different beam polarizations. Such sensitivity motivates the development of techniques that can provide structural details. We illustrate several approaches by which various types of calculations and molecular simulations may be used to enhance the sought structural interpretation of experimental data. By applying these techniques to the adsorbate molecules, interfacial water, and the substrate surfaces themselves, we are able to achieve a holistic picture of the adsorption environment.

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 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 score0.018
Threshold uncertainty score0.408

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)0.0000.000

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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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.

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

Citations39
Published2014
Admission routes1
Has abstractyes

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