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Record W2956939116 · doi:10.1002/smll.201902136

Graphene Quantum Dots for Optical Bioimaging

2019· review· en· W2956939116 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmall · 2019
Typereview
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Alberta
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsQuantum dotNanotechnologyGrapheneBiocompatibilityMaterials scienceSurface modificationGraphene quantum dotOptical imagingChemistryPhysicsOptics

Abstract

fetched live from OpenAlex

Graphene quantum dots (GQDs) have shown great potential in bioimaging applications due to their excellent biocompatibility, low cytotoxicity, feasibility for surface functionalization, physiological stability, and tunable fluorescence properties. This Review first introduces the intriguing optical properties of GQDs that are suitable for biological imaging, and is followed by the GQDs' synthetic strategies. The emergent and latest development methods for tuning GQDs' optical properties are further described in detail. The recent advanced applications of GQDs in vitro, particularly in cell imaging, targeted imaging, and theranostic nanoplatform fabrication, are included. The applications of GQDs for in vivo bioimaging are also covered. Finally, the Review is concluded with the challenges and prospectives that face this nascent yet exciting field.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.133
GPT teacher head0.362
Teacher spread0.229 · 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