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Record W4401188207 · doi:10.1039/d4ra03815k

Iron-based metal–organic framework/graphene oxide composite electrodes for efficient flow-injection amperometric detection of dexamethasone

2024· article· en· W4401188207 on OpenAlexaff
Chanida Jakkrawhad, Fonthip Makkliang, Piyaluk Nurerk, Mohamed Siaj, Sujittra Poorahong

Bibliographic record

VenueRSC Advances · 2024
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsUniversité du Québec à Montréal
FundersWalailak University
KeywordsGrapheneAmperometryOxideElectrodeGraphiteGraphene oxide paperComposite numberMaterials scienceMetal-organic frameworkGraphite oxideMetalInorganic chemistryChemical engineeringNanotechnologyChemistryElectrochemistryComposite materialMetallurgyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Ag/AgCl). The concentration was linear in the range 0.10-5.0 μM and 0.010-5.0 mM with LOD of 0.030 μM based on 3(sd/slope). The modified electrode also exhibited a remarkably stable response under optimized conditions, and up to 55 injections can be used per electrode. The sensor exhibits high repeatability, reproducibility, and anti-interference properties when used for DEX detection. The effective determination of dexamethasone in real pharmaceutical and cosmetic samples demonstrated the feasibility of the electrochemical sensor, and the results were in good agreement with those obtained from the HPLC-DAD analysis. Acceptable percentage recoveries from the spiked pharmaceutical and cosmetic samples were obtained, ranging from 93-111% for this new method compared with 84-107% for the HPLC-DAD standard method.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.221
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2024
Admission routes1
Has abstractyes

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