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Record W2032486710 · doi:10.1021/jp303465u

Nitrogen-Functionalized Graphene Nanoflakes (GNFs:N): Tunable Photoluminescence and Electronic Structures

2012· article· en· W2032486710 on OpenAlexaff
J. W. Chiou, Sekhar C. Ray, Songang Peng, Cheng‐Hao Chuang, Bo-Yao Wang, Hsiu‐Min Tsai, Chih‐Wen Pao, H.-J. Lin, Yu Shao, Y. F. Wang, S. C. Chen, W. F. Pong, Yu‐Te Yeh, C. W. Chen, Li–Chyong Chen, Kuei‐Hsien Chen, M.‐H. Tsai, Ajay Kumar, Abhijit Ganguly, Pagona Papakonstantinou, Hiroyuki Yamane, Nobuhiro Kosugi, Tom Regier, L. Liu, Tsun‐Kong Sham

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2012
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsWestern UniversityCanadian Light Source (Canada)
Fundersnot available
KeywordsPhotoluminescenceMaterials scienceExcited stateLuminescenceGrapheneSpectroscopyBand gapAbsorption (acoustics)NanotechnologyOptoelectronicsAtomic physicsPhysics

Abstract

fetched live from OpenAlex

This study investigates the strong photoluminescence (PL) and X-ray excited optical luminescence observed in nitrogen-functionalized 2D graphene nanoflakes (GNFs:N), which arise from the significantly enhanced density of states in the region of π states and the gap between π and π* states. The increase in the number of the sp 2 clusters in the form of pyridine-like N–C, graphite-N-like, and the C═O bonding and the resonant energy transfer from the N and O atoms to the sp 2 clusters were found to be responsible for the blue shift and the enhancement of the main PL emission feature. The enhanced PL is strongly related to the induced changes of the electronic structures and bonding properties, which were revealed by the X-ray absorption near-edge structure, X-ray emission spectroscopy, and resonance inelastic X-ray scattering. The study demonstrates that PL emission can be tailored through appropriate tuning of the nitrogen and oxygen contents in GNFs and pave the way for new optoelectronic devices.

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 score0.020
Threshold uncertainty score0.302

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations61
Published2012
Admission routes1
Has abstractyes

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