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Record W2801219673 · doi:10.1002/admi.201800150

Failure of Fermi Level in Referencing Chemical Shift of Molecules on Solid Surfaces

2018· article· en· W2801219673 on OpenAlexafffund
Yiying Li, Zheng‐Hong Lu

Bibliographic record

VenueAdvanced Materials Interfaces · 2018
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Foundation for InnovationGlobal Fund to Fight AIDS, Tuberculosis and Malaria
KeywordsFermi levelValence (chemistry)Chemical physicsMoleculeMaterials scienceDipoleChemical bondCore (optical fiber)Charge (physics)Atomic physicsCondensed matter physicsChemistryPhysicsElectronNuclear physicsOrganic chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Core level chemical shifts using Fermi level as an orthodox reference level are extensively used to detect the forming/breaking of bonds (i.e., change in oxidation states) during chemical reactions and are broadly used as an experimental proof of charge transfers across organic interfaces by several research communities for many decades. Based on core level and valence photoemission experimental measurements a paradoxical relationship between core‐level shift and charge transfer is shown, demonstrating the failure of conventional practice of using Fermi level to measure chemical shifts of molecules on solid surfaces. Thus, great care needs to be taken when interpreting experimentally observed shifts in core level binding energies as well as assigning core level energy peaks associated with certain oxidation states. To resolve this paradoxical problem, it is shown that measurement of interface dipoles is essential in investigating the charge transfers of molecules to/from solid surfaces.

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 score0.004
Threshold uncertainty score0.557

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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