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Record W4285399087 · doi:10.1149/ma2022-0112841mtgabs

Towards Understanding the Impact of Electrochemical Double Layer on the Performance of Graphene Devices

2022· article· en· W4285399087 on OpenAlexaff
Shayan Angizi, Lea Hong, P. Ravi Selvaganapathy, Peter Kruse

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGrapheneIonic strengthZeta potentialRedoxElectrochemistryAqueous solutionIonChemical physicsIonic bondingMaterials scienceSurface chargeDouble layer (biology)NanotechnologyLayer (electronics)Chemical engineeringInorganic chemistryChemistryElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of electrochemical double layer (EDL) on the performance of graphene-based sensing platforms has been an area of controversy over the last two decades 1. The hydrophobic nature of bare graphene tends to repel the water and minimizes the solution/solid surface interactions 2. However, the presence of oxygen-based functional groups on graphene introduces negatively charged sites and causes a negative surface zeta potential. Hence, the instant formation of an EDL on a graphene surface in an aqueous solution is inevitable, affecting the graphene surface's chemical/physical interactions 3. Thus far, multiple theories and techniques have been developed to investigate the impact of EDL on graphene sensing performance; however, they either suffer from complexity in design or have ignored the co-existence of other solution parameters such as pH and oxidation-reduction potential 4. In this work, we propose the use of alkaline chloride salts to understand the impact of the ionic strength and EDL of the solution on the few-layer graphene (FLG) based chemiresistive sensors. Considering the full ionization of NaCl and the low redox potential of the generated ions, alkaline chlorides are considered good candidates to modulate the electronic band structure of FLG without altering its surface chemistry 5. By use of Helmholtz theory of EDL, it is postulated the Inner Helmholtz Layer (IHL) to be formed by Na+ ions due to the negative surface charge of FLG. Whereas the hydrated counter ions, Cl-, form the Outer Helmholtz Layer (OHL). Upon the formation of a positively charged immobile layer of Na+ ions on the surface (IHL), the electrostatic gating effect of EDL dopes the graphene with electrons. This charge separation can be modelled as two parallel plates of a capacitor with infinitely thin dielectric 6. Since the presence of oxygen-based functional groups on FLG is inevitable during the synthesis process, it is considered inherently p-doped. Accordingly, upon the addition of NaCl concentrations, the FLG surface current decreases. However, at higher concentrations (around 2000 ppm NaCl), the sensor response is inversed, and surface current increases by the addition of NaCl (Figure-left). In fact, high ionic strength increases the EDL compactness and decreases the charge screening length (Debye length). As a result of this sharper difference in solid/solution potentials (Figure-right), a more n-doped surface having electron as majority charge carriers is obtained, changing the nature of the response. We have also demonstrated that the formation of sodium-oxygen metal complexes on FLG is not likely in an aqueous environment. According to the Raman spectroscopy results of FLG, an increase in the intensity ratio of D (defect) to G bands is observed upon exposure to water and NaCl. Moreover, the noticeable right shift in the 2D band position demonstrates the p-doping of FLG 7. Furthermore, the reversible response of the sensor during multiple exposures to NaCl demonstrates the sensor response originates from EDL, not the formation of sodium-oxygen metal complexes. In fact, the full or half hydrated Na+ ions in IHL electrostatically interact with surrounding oxygen. However, since the oxygen atom in water is more negatively charged compared to the oxygen on the graphene surface, Na+ is unlikely to bound to the surface after hydration. Therefore, using NaCl could be a suitable medium to study the impact of EDL on the performance of graphene devices. Reference Jurado et al., Scientific Reports, 7(1), pp.1-12 (2017). Angizi, S et al., Langmuir, 37(41), pp.12163-12178 (2021). Jung et al., Nano Letters, 21(1), pp.34-42 (2020). Pak et al., The Journal of Physical Chemistry C, 118(38), pp.21770-21777 (2014). Lee et al., ACS Applied Materials & Interfaces, 11(45), pp.42520-42527 (2019). Kwon et al., The Journal of Physical Chemistry C, 116(50), pp.26586-26591 (2012). Bruna et al., ACS Nano, 8(7), pp.7432-7441 (2014). Figure 1

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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 score0.007

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 communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.264
Teacher spread0.229 · 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
Published2022
Admission routes1
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