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Record W3128977910 · doi:10.1039/d0ra07614g

Recent developments in voltammetric and amperometric sensors for cysteine detection

2021· review· en· W3128977910 on OpenAlexaff
Somayeh Tajik, Zahra Dourandish, Peyman Mohammadzadeh Jahani, Iran Sheikhshoaie, Hadi Beitollahi, Mehdi Shahedi Asl, Ho Won Jang, Mohammadreza Shokouhimehr

Bibliographic record

VenueRSC Advances · 2021
Typereview
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research Foundation
KeywordsAmperometryCysteineChemistryNanotechnologyVoltammetryChromatographyComputer scienceElectrochemistryMaterials scienceBiochemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

This review article aims to provide an overview of the recent advances in the voltammetric and amperometric sensing of cysteine (Cys). The introduction summarizes the important role of Cys as an essential amino acid, techniques for its sensing, and the utilization of electrochemical methods and chemically modified electrodes for its determination. The main section covers voltammetric and amperometric sensing of Cys based on glassy carbon electrodes, screen printed electrodes, and carbon paste electrodes, modified with various electrocatalytic materials. The conclusion section discusses the current challenges of Cys determination and the future perspectives.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.032
GPT teacher head0.309
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations60
Published2021
Admission routes1
Has abstractyes

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