MétaCan
Menu
Back to cohort
Record W4399933705 · doi:10.1149/1945-7111/ad5b21

Construction of rGO and GSH Electrochemical Sensor by Electrodeposition for Naringenin Sensing

2024· article· en· W4399933705 on OpenAlexaff
Huiting Hu, Jiangtao Xu, Bing-Lun Li, Guo‐Cheng Han, Xiao‐Zhen Feng, Heinz‐Bernhard Kraatz

Bibliographic record

VenueJournal of The Electrochemical Society · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsNaringeninElectrochemistryElectrochemical gas sensorMaterials scienceMetallurgyNanotechnologyChemistryElectrodeAntioxidantOrganic chemistryPhysical chemistryFlavonoid

Abstract

fetched live from OpenAlex

Naringenin (NRG), a flavanone compound present in citrus fruits, has a variety of beneficial physiological active functions such as antioxidant, anti-inflammatory, and hypoglycaemic. In this study, an sensor was constructed by electrodeposition and used for the electrochemical study of NRG. Reduced graphene oxide (rGO) and glutathione (GSH) showed the ability to synergistically amplify NRG signals and possessed good linearity in the concentration range of 10.00–1200.00 μ mol l −1 . The linear equation is I p = 0.0776logc + 0.9353 (R 2 = 0.9901), and the limit of detection is 3.33 μ mol l −1 . The sensor performed well in terms of reproducibility, stability, and selectivity, which in turn enabled the detection of NRG in tomatoes. The average recovery of the sensor is 95.68% to 111.92%, with RSD less than 11.89%. The results were also verified by Ultraviolet–visible spectroscopy(UV-vis). Furthermore, density-functional theory was employed to analyse the front track of the NRG, speculating that the NRG underwent a transfer of two electrons and two protons.

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 score0.149
Threshold uncertainty score0.554

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 integrity0.0000.001
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 head0.003
GPT teacher head0.202
Teacher spread0.199 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicAdvanced Chemical Sensor TechnologiesFrench-language works237,207