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Record W4283700027 · doi:10.1002/celc.202200334

Direct Laser Writing of Poly(furfuryl Alcohol)/Graphene Oxide Electrodes for Electrochemical Determination of Ascorbic Acid

2022· article· en· W4283700027 on OpenAlexaff
Lara Fernandes Loguercio, Anderson Thesing, Bruno S. Noremberg, Bruno Vasconcellos Lopes, Guilherme Kurz Maron, Giovanna Machado, Michael A. Pope, Neftalí Lênin Villarreal Carreño

Bibliographic record

VenueChemElectroChem · 2022
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsUniversity of Waterloo
FundersFundação de Amparo à Pesquisa do Estado do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsAscorbic acidGrapheneCarbonizationMaterials scienceDetection limitReproducibilityOxideElectrochemical gas sensorElectrodeElectrochemistryChemical engineeringNanotechnologyChemistryChromatographyComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract Due to the considerable importance of preventing and treating diseases, efficient detection methods are required to monitor levels of ascorbic acid (AA) in beverages, foods, dietary supplements, and biological fluids. In this work, an efficient, easy handling, low cost, and simple fabrication process for non‐enzymatic electrochemical sensors was fabricated through the carbonization of a graphene oxide filled biomass‐derived polymer poly(furfuryl alcohol) (PFA/GO), as sustainable alternative, using a high throughput CO 2 laser‐scribing process. The laser power was found to determine the physicochemical properties of the resulting graphene‐like electrodes. As an electrochemical sensor, devices presented a detection limit of 1.0 μmol cm 2 L −1 with good reproducibility towards AA oxidation. For real sample measurements, recovery rates between 97 and 113 % were found in commercial vitamin‐C tablet. Analysis of AA in synthetic sweat presented good intra‐electrode reproducibility and limit of detection of 1.3 μmol cm 2 L −1 .

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 categoriesMeta-epidemiology (narrow)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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.

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

Citations19
Published2022
Admission routes1
Has abstractyes

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