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Record W2596780502 · doi:10.1149/ma2017-01/38/1780

(Invited) Impact of Carbon Surface Functionalities on the Electrochemical Detection of Hemoglobin

2017· article· en· W2596780502 on OpenAlexaff
Heather A. Andreas, Justin Tom

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCyclic voltammetryChemistryElectrochemistryCarbon fibersDifferential pulse voltammetryX-ray photoelectron spectroscopyInorganic chemistryFourier transform infrared spectroscopyBiosensorElectrodeAttenuated total reflectionInfrared spectroscopyMaterials scienceChemical engineeringOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The electrochemical biosensing of hemoglobin (Hb) may provide a relatively fast method of detection with the possibility of developing point-of-care diagnostics or at-home monitoring; however, obtaining a Hb electrochemical signal is often slow and difficult because the four redox active heme groups are buried in the interior hydrophobic regions of the protein. Ideally, the biosensor electrode would be inexpensive and formed from environmentally sustainable materials, such as carbon. However, the electroactivity of Hb is inconsistent on different carbon electrode materials. Additionally, Hb electroactivity is strongly dependent on whether the Hb is immobilized on the electrode surface or is in a solution-based sample. Clearly, for a point-of-care diagnostic, it would be useful to be able to measure the Hb concentrations within a liquid phase, and therefore a carbon material which evidences Hb-electroactivity is required. Until this work, there was little understanding of the role that carbon-oxygen surface functional groups (from the carbon electrode) played in Hb electroactivity. We examine Hb electroactivity of several carbons are examined through cyclic voltammetry and differential pulse voltammetry in a neutral phosphate buffered electrolyte. Carbon-oxygen surface functionalities were characterized using X-ray photoelectron spectroscopy (XPS), thermal programmed desorption (TPD) and attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR). Our findings show that Hb electroactivity is inhibited by ether and carbonyl surface groups present on the carbon electrode. Using this information, a Hb-inactive carbon (Vulcan XC-72) was made active for Hb detection by removal of these surface groups (see figure). Ultrasonication removed the ether functionalities, resulting in a significant increase in the Hb electroreduction. The amount of reduction was increased further if the ultrasonication was followed by a relatively simple 15 minute electroreduction at -1.95 V vs. Hg/Hg2SO4 (saturated K2SO4) in stirred 10 mM phosphate electrolyte (pH 5.03) purged with N2. The electroreduction was shown to selectively remove the carbonyl and quinone functionalities, resulting in the increase Hb electroactivity. The knowledge of carbon-oxygen surface functionalities is essential to better understand hemoglobin’s electroactivity on carbon and influences the choice of carbon electrode materials for further development of hemoglobin electrochemical biosensors. Figure: Representative CVs (a) and DPVs (b) of glassy carbon (dotted green curve), ultrasonicated Vulcan XC-72 on glassy carbon (dashed purple curve) and ultrasonication+electrochemically reduced Vulcan XC-72 on glassy carbon (solid purple curve) in 0.1 M PB containing 0.2 g L-1 BHb from at least three replicate trials. Figure 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 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.222
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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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