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Record W4416611694 · doi:10.1149/ma2025-02121101mtgabs

Influence of Different Gold-Doped Carbon Electrodes on Interactions with L-Cysteine – a Comparative Study of Glassy Carbon and Paraffin-Impregnated Graphite Electrode

2025· article· W4416611694 on OpenAlexaff
Peter Slovenský, S. Marzieh Kalantarian, Maroš Halama, Yolanda S. Hedberg

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsWestern University
Fundersnot available
KeywordsElectrodeColloidal goldGlassy carbonCarbon fibersElectrochemistryAdsorptionGraphiteSubstrate (aquarium)

Abstract

fetched live from OpenAlex

A comparative electrochemical study was conducted to evaluate two gold-doped carbon electrodes: a glassy carbon electrode (GCE) and paraffin-impregnated graphite electrode (PIGE) on their performance to detect interactions between L-cysteine (Cys) and differently sized gold nanoparticles using cyclic voltammetry. The study investigated how the choice of carbon electrode and the size of gold nanoparticles (AuNPs) influence the oxidative response of Cys. Gold, known for its strong affinity toward amino acids, interacts with Cys through both its amino (–NH₂) and thiol (–SH) functional groups, enhancing detection sensitivity, as was shown in our previous study [1] [2]. Different types of carbon electrodes can also directly detect Cys in solution. Their high electrocatalytic activity, attributed to oxygen-containing functional groups and edge-plane graphite sites, facilitates efficient electron transfer. Additionally, their porous structure provides a large surface area, further enhancing detection sensitivity [3]. The stabilization of AuNPs likely depends on the electrode surface properties. The rougher surface of PIGE, offering more adsorption sites, may facilitate stronger attachment of the nanoparticles relative to GCE. Both gold-doped electrodes exhibited a pronounced oxidative response to Cys as compared to the electrodes without gold nanoparticles, confirming their suitability for the interaction detection. The peak potential of gold oxidation remained largely independent of the electrode substrate (GCE or PIGE), suggesting that the underlying carbon material does not significantly affect the thermodynamics of the Au-Cys interaction. The oxidation potential shifted positively with increasing AuNP size, consistent with literature reports [4]. The most significant difference occurred at pH 2 on the gold-doped GCE electrode. The Cys still significantly increased the gold oxidation, but it did not prevent the gold oxide reduction from Au III to Au 0 (reduction peak around 0.45 mV Ag/AgCl ) in contrast to other pH values and the PIGE electrode [5]. This limited inhibition of the gold reduction peak was probably because of a weaker adsorption of the Cys molecules on the surface of the GCE. While both electrodes demonstrated good electrochemical activity, the PIGE provided enhanced sensitivity and better resolution of overlapping peaks, making it more suitable for mechanistic studies. In contrast, the GCE exhibited superior reproducibility and stability of the current response, which is advantageous for quantitative analyses. These findings highlight the importance of electrode material selection and AuNP morphology in optimizing the electrochemical detection of thiol-containing biomolecules such as L-cysteine. References [1] M. Kalantarian, P. Slovensky, et al. Part. Part. Syst. Charact . DOI 10.1002/ppsc.202400230. [2] K. Kudpeng, P. Thiravetyan, Min. Metall. Explor. 2021, 38 , 2185. [3] M. Zhou, J. Ding, L. Guo, Q. Shang, Anal. Chem . 2007, 79 , 14. [4] K. Branina, L. Galperin, E. Vikulova, J. Solid State Electrochem. 2011, 15 , 1049. [5] N. Spãtaru, B. V. Sarada, E. Popa, D. A. Tryk, A. Fujishima, Anal. Chem.2001, 73 , 514 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)0.001
metaresearch head score (Gemma)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.235
Teacher spread0.227 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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