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Record W1986521260 · doi:10.1063/1.4748379

Inelastic tunneling spectroscopy of gold-thiol and gold-thiolate interfaces in molecular junctions: The role of hydrogen

2012· article· en· W1986521260 on OpenAlexaff

Bibliographic record

VenueThe Journal of Chemical Physics · 2012
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsThiolInelastic electron tunneling spectroscopyMoleculeMolecular vibrationSpectroscopyHydrogen bondQuantum tunnelling

Abstract

fetched live from OpenAlex

It is widely believed that when a molecule with thiol (S-H) end groups bridges a pair of gold electrodes, the S atoms bond to the gold and the thiol H atoms detach from the molecule. However, little is known regarding the details of this process, its time scale, and whether molecules with and without thiol hydrogen atoms can coexist in molecular junctions. Here, we explore theoretically how inelastic tunneling spectroscopy (IETS) can shed light on these issues. We present calculations of the geometries, low bias conductances, and IETS of propanedithiol and propanedithiolate molecular junctions with gold electrodes. We show that IETS can distinguish between junctions with molecules having no, one, or two thiol hydrogen atoms. We find that in most cases, the single-molecule junctions in the IETS experiment of Hihath et al. [Nano Lett. 8, 1673 (2008)] had no thiol H atoms, but that a molecule with a single thiol H atom may have bridged their junction occasionally. We also consider the evolution of the IETS spectrum as a gold STM tip approaches the intact S-H group at the end of a molecule bound at its other end to a second electrode. We predict the frequency of a vibrational mode of the thiol H atom to increase by a factor ~2 as the gap between the tip and molecule narrows. Therefore, IETS should be able to track the approach of the tip towards the thiol group of the molecule and detect the detachment of the thiol H atom from the molecule when it occurs.

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 score0.008
Threshold uncertainty score0.244

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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