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Record W2415430457 · doi:10.1021/acs.langmuir.5b02859

Mapping and Quantifying Surface Charges on Clay Nanoparticles

2015· article· en· W2415430457 on OpenAlex
Jun Liu, Ravi Gaikwad, Aharnish Hande, Siddhartha Das, Thomas Thundat

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsKaoliniteSurface chargeNanoparticleKelvin probe force microscopeCharge densityTailingsClay mineralsNanoscopic scaleMaterials scienceMineralogyChemical engineeringNanotechnologyChemistryAtomic force microscopyMetallurgy

Abstract

fetched live from OpenAlex

Understanding the electrical properties of clay nanoparticles is very important since they play a crucial role in every aspect of oil sands processing, from bitumen extraction to sedimentation in mature fine tailings (MFT). Here, we report the direct mapping and quantification of surface charges on clay nanoparticles using Kelvin probe force microscopy (KPFM) and electrostatic force microscopy (EFM). The morphology of clean kaolinite clay nanoparticles shows a layered structure, while the corresponding surface potential map shows a layer-dependent charge distribution. More importantly, a surface charge density of 25 nC/cm(2) was estimated for clean kaolinite layers by using EFM measurements. On the other hand, the EFM measurements show that the clay particles obtained from the tailings demonstrate a reduced surface charge density of 7 nC/cm(2), which may be possibly attributed to the presence of various bituminous compounds residing on the clay surfaces.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.254
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.061
GPT teacher head0.318
Teacher spread0.257 · 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