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Record W4401578890 · doi:10.1021/acs.nanolett.4c01230

Counterion Distribution in the Stern Layer on Charged Surfaces

2024· article· en· W4401578890 on OpenAlexaff
Tianyi Han, Wanxing Xu, Jie Han, Vahid Adibnia, Hongjiang He, Chenhui Zhang, Jianbin Luo

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsDalhousie University
FundersChina National Funds for Distinguished Young ScientistsChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsCounterionCharge densityAdsorptionIonChemistrySurface chargeElectrolyteChemical physicsAqueous solutionValence (chemistry)Inorganic chemistryAnalytical Chemistry (journal)Physical chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Counterion adsorption at the solid-liquid interface affects numerous applications. However, the counterion adsorption density in the Stern layer has remained poorly evaluated. Here we report the direct determination of surface charge density at the shear plane between the Stern layer and the diffuse layer. By the Grahame equation extension and streaming current measurements for different solid surfaces in different aqueous electrolytes, we are able to obtain the counterion adsorption density in the Stern layer, which is mainly related to the surface charge density but is less affected by the bulk ion concentration. The charge inversion concentration is further found to be sensitive to the ion type and ion valence rather than to the charged surface, which is attributed to the ionic competitive adsorption and ion-ion correlations. Our findings offer a framework for understanding ion distribution in many physical and chemical processes where the Stern layer is ubiquitous.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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 designTheoretical or conceptual
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

Citations10
Published2024
Admission routes1
Has abstractyes

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