MétaCan
Menu
Back to cohort
Record W2164872973 · doi:10.1038/srep02250

Quantum confinement-induced tunable exciton states in graphene oxide

2013· article· en· W2164872973 on OpenAlexaff
Dongwook Lee, Jiwon Seo, Xi Zhu, Jiyoul Lee, Hyeon‐Jin Shin, Jacqueline M. Cole, T. Shin, Jaichan Lee, Hangil Lee, Haibin Su

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of New Brunswick
FundersMinistry of Education, Science and TechnologyNational Research Foundation of KoreaNational Research FoundationRoyal Society
KeywordsGrapheneOxideQuantum dotExcitonMaterials scienceValence (chemistry)LuminescenceGraphene nanoribbonsNanocrystalOptoelectronicsNanotechnologyChemical physicsChemistryCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

Graphene oxide has recently been considered to be a potential replacement for cadmium-based quantum dots due to its expected high fluorescence. Although previously reported, the origin of the luminescence in graphene oxide is still controversial. Here, we report the presence of core/valence excitons in graphene-based materials, a basic ingredient for optical devices, induced by quantum confinement. Electron confinement in the unreacted graphitic regions of graphene oxide was probed by high resolution X-ray absorption near edge structure spectroscopy and first-principles calculations. Using experiments and simulations, we were able to tune the core/valence exciton energy by manipulating the size of graphitic regions through the degree of oxidation. The binding energy of an exciton in highly oxidized graphene oxide is similar to that in organic electroluminescent materials. These results open the possibility of graphene oxide-based optoelectronic device technology.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.107
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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.

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

Citations61
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicGraphene research and applicationsFrench-language works237,207