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Record W4307762046 · doi:10.1177/23977914221127527

In situ- preparation and estimation of the physical characterization of Nanofluidic solution and its application

2022· article· en· W4307762046 on OpenAlexaff
Ameen Abdelrahman, Fouad Erchiqui, Mourd Nedil, Mohamed Siaj

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part N Journal of Nanomaterials Nanoengineering and Nanosystems · 2022
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsElectrolyteEthylene glycolGrapheneMaterials scienceNanotechnologyChemical engineeringDielectric spectroscopyNanoparticleElectrochemistryElectrodeChemistry

Abstract

fetched live from OpenAlex

In this work, the preparation and the physical evaluation of a series of miscible 0.2 M solutions in 50/50 volume ratios were addressed. A total of five solutions comprised of plain electrolyte/graphene, plain electrolyte/graphene-ethylene Glycol, plain electrolyte/graphene-poly-ethylene glycol, plain electrolyte/graphene-glycerol, and plain electrolyte/Graphene–Polyethylenimine. were assembled with Ag, and copper nanoparticles. The physical properties were studied by electrochemical impedance spectroscopy, solution conductivity calculations, and viscosity and flexibility measurements, particle size distribution analyses. Surface morphology characterizations were done by transmission electron microscopy. A comparative approach of the physical properties between the five solutions serves as a guide to select the most appropriate fluid applicable to the upcoming device.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part N Journal of Nanomaterials Nanoengineering and NanosystemsSame topicElectrochemical Analysis and ApplicationsFrench-language works237,207