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Record W4388633603 · doi:10.3390/chemosensors11110562

Electrochemical Sensor Based on Spent Coffee Grounds Hydrochar and Metal Nanoparticles for Simultaneous Detection of Emerging Contaminants in Natural Water

2023· article· en· W4388633603 on OpenAlexaff
Francisco Contini Barreto, Erika Ito, Naelle Kita Mounienguet, Letícia Soares, Jie Yang, Quan He, Ivana Cesarino

Bibliographic record

VenueChemosensors · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBisphenol ANanoparticleElectrochemistryVoltammetryDetection limitElectrochemical gas sensorNuclear chemistryCopperCyclic voltammetryChemistryMaterials scienceElectrodeNanotechnologyChromatographyOrganic chemistryEpoxy

Abstract

fetched live from OpenAlex

This research describes the modification of a glassy carbon electrode with spent coffee grounds hydrochar (HDC) and copper nanoparticles (CuNPs) for the simultaneous determination of hydroxychloroquine sulfate (HCS) and bisphenol A (BPA). Scanning electron microscopy, EDS and cyclic voltammetry were used to characterize the nanocomposite. The analytical parameters were optimized and the sensing platform was applied for the determination of HCS and BPA using square-wave voltammetry (SWV). For HCS, the linear range was from 1.0 μmol L−1 to 50 μmol L−1, with an LOD and LOQ of 0.46 and 1.53 μmol L−1, respectively. For BPA, the linear range was from 0.5 μmol L−1 to 10 μmol L−1, with an LOD and LOQ of 0.31 μmol L−1 and 1.06 μmol L−1, respectively. Finally, the developed electrochemical sensor was applied for the quantification of the emerging contaminants in natural water, with recoveries between 94.8% and 106.8% for HCS and 99.6% and 105.2% for BPA. Therefore, HDC-CuNPs demonstrated themselves to be a good alternative as a sustainable and cheaper material for application in electroanalyses.

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 score0.252
Threshold uncertainty score0.905

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 integrity0.0000.000
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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