MétaCan
Menu
Back to cohort
Record W2314526564 · doi:10.1021/la5048968

Probing the Adsorption of Weak Acids on Graphite Using Amplitude Modulation–Frequency Modulation Atomic Force Microscopy

2015· article· en· W2314526564 on OpenAlexafffund
Ahmed Moustafa, Jun Huang, Kerry N. McPhedran, Hongbo Zeng, Mohamed Gamal El‐Din

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaHelmholtz-GemeinschaftAlberta Innovates - Technology FuturesShell
KeywordsAdsorptionHighly oriented pyrolytic graphiteMoleculeChemistryChemical physicsAnalytical Chemistry (journal)GraphiteMaterials scienceChemical engineeringPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Recent thermodynamics calculations and adsorption isotherms showed that the adsorption of a self-assembled layer (SAL) of ionized weak acids to carbon was attributed to the negatively charged hydrogen bonding (-CAHB), yet the direct visualization and characterization of this adsorption behavior have not been reported. Here, an amplitude modulation-frequency modulation atomic force microscopy (AM-FM AFM) technique was applied to discriminate the adsorption of decanoic acids (DA) on highly ordered pyrolytic graphite (HOPG). Thermodynamics calculations revealed that the adsorption of SAL was driven by the formation of -CAHB with negatively charged functional groups of HOPG. Multilayer adsorption could occur over the adsorbed ionized SAL, leading to the development of aggregates. AM-FM AFM imaging showed that the adsorption of the DA molecules forming aggregates occurred only for the HOPG-functionalized steps, while DA molecules were found to adsorb over the entire functionalized HOPG surface after water-plasma treatment, as evident from the frequency shifts identified in AFM images.

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 score0.323
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 head0.027
GPT teacher head0.300
Teacher spread0.274 · 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
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueLangmuirSame topicForce Microscopy Techniques and ApplicationsFrench-language works237,207