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Record W2886711387 · doi:10.1038/s41598-018-30090-9

A New Approach to Compute the Porosity and Surface Roughness of Porous Coated Capillary-Assisted Low Pressure Evaporators

2018· article· en· W2886711387 on OpenAlexafffund
Poovanna Cheppudira Thimmaiah, Asish Panda, Upendra Kumar Pandey, Claire McCague, Pradip Dutta, Majid Bahrami

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsSimon Fraser University
FundersMedical Research CouncilSocial Sciences and Humanities Research Council of CanadaInternational Development Research CentreFondation Rideau HallNatural Sciences and Engineering Research Council of CanadaFondations communautaires du CanadaGovernment of CanadaIndian Institute of Science
KeywordsPorosityMaterials scienceSurface roughnessCoatingComposite materialSurface finishCapillary actionThin filmThermal sprayingCopperMetallurgyNanotechnology

Abstract

fetched live from OpenAlex

The fundamental characteristics of metal coatings that influence heat transfer are porosity and surface roughness. It is a challenge to analyze the porosity and surface roughness due to the inadequate amount of copper per coated area. In this study, a new approach to non-invasively determine the porosity of metal films utilizing a helium pycnometer and computed micro-tomography (CMT) is presented. Furthermore, a telescope-goniometer is used to measure the surface roughness. Experiments are conducted on four varieties of thin film samples coated with copper powder using wire flame and plasma thermal spray coating methods. The porosities of the thin films were determined to be between 39 and 43%. The thermal spray coating increased the hydrophobicity of the surface and the plasma coating created super-hydrophobic surfaces. The new approach establishes that the porosity of thin films can be non-invasively determined and may also be applied to a wide variety of coated 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.

How this classification was reachedexpand

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)0.002
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 score0.012
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.023
GPT teacher head0.249
Teacher spread0.226 · 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 teacher head, 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

Citations13
Published2018
Admission routes2
Has abstractyes

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