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Record W2990495882 · doi:10.1515/nanoph-2019-0418

Simply synthesized nitrogen‐doped graphene quantum dot (NGQD)‐modified electrode for the ultrasensitive photoelectrochemical detection of dopamine

2018· article· en· W2990495882 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

VenueNanophotonics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuantum dotPhotocurrentGraphene quantum dotGrapheneMaterials scienceElectrodePhotoelectrochemistryNanomaterialsNanotechnologyOptoelectronicsSemiconductorDetection limitChemistryElectrochemistry

Abstract

fetched live from OpenAlex

Abstract Recently, nitrogen‐doped graphene quantum dots (NGQDs), as a new type of quantum semiconductor and photoelectrochemical material, are promising candidates in photoelectric sensing, water splitting, and biological imaging and have various potential application prospects. In this work, NGQDs were prepared by a simple calcination method, and then a photoelectrochemical sensing platform based on the NGQDs electrode with superior photoelectrochemical activity was designed and fabricated for the detection of dopamine (DA). Benefitting from the quantum effect and size effect, NGQDs displayed an enhanced photocurrent effective within ultra‐low detection limit (0.03 μ m ), wide detection range (0.03–450 and 450–9680 μ m ), and high sensitivity in detecting DA with the assistance of ultraviolet light irradiation. The NGQDs electrode also showed continuous and stable photocurrent densities after long‐term experiment, indicating the excellent durability of NGQDs for DA detection.

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)0.001
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 score0.023
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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