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Record W2334806053 · doi:10.1021/nl402057j

Direct Observation of Spatially Heterogeneous Single-Layer Graphene Oxide Reduction Kinetics

2013· article· en· W2334806053 on OpenAlexaff
Matthew P. McDonald, Ahmed Eltom, Felix Vietmeyer, Janak Thapa, Yurii V. Morozov, Denis A. Sokolov, José H. Hodak, K. Vinodgopal, Prashant V. Kamat, Masaru Kuno

Bibliographic record

VenueNano Letters · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
FundersBasic Energy SciencesArmy Research OfficeCore Research for Evolutional Science and TechnologyFondo para la Investigación Científica y TecnológicaAmerican Chemical Society Petroleum Research FundU.S. Department of EnergyConsejo Nacional de Investigaciones Científicas y TécnicasNational Science FoundationAmerican Chemical SocietyNational Aeronautics and Space Administration
KeywordsGrapheneKineticsOxideMaterials scienceReduction (mathematics)Layer (electronics)NanotechnologyChemical engineeringChemical physicsChemistryPhysics

Abstract

fetched live from OpenAlex

Graphene oxide (GO) is an important precursor in the production of chemically derived graphene. During reduction, GO's electrical conductivity and band gap change gradually. Doping and chemical functionalization are also possible, illustrating GO's immense potential in creating functional devices through control of its local hybridization. Here we show that laser-induced photolysis controllably reduces individual single-layer GO sheets. The reaction can be followed in real time through sizable decreases in GO's photoluminescence efficiency along with spectral blueshifts. As-produced reduced graphene oxide (rGO) sheets undergo additional photolysis, characterized by dramatic emission enhancements and spectral redshifts. Both GO's reduction and subsequent conversion to photobrightened rGO are captured through movies of their photoluminescence kinetics. Rate maps illustrate sizable spatial and temporal heterogeneities in sp(2) domain growth and reveal how reduction "flows" across GO and rGO sheets. The observed heterogeneous reduction kinetics provides mechanistic insight into GO's conversion to chemically derived graphene and highlights opportunities for overcoming its dynamic, chemical disorder.

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 score0.005
Threshold uncertainty score0.431

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 head0.032
GPT teacher head0.248
Teacher spread0.215 · 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

Citations43
Published2013
Admission routes1
Has abstractyes

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