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Record W2795087498 · doi:10.1002/cjce.23211

An investigation on dissolutive wetting of porous urea surface

2018· article· en· W2795087498 on OpenAlexvenueno aff
Abdul Basit, Parthiban Siwayanan, KuZilati KuShaari, Babar Azeem

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
FundersUniversiti Teknologi Petronas
KeywordsWettingUreaPorosityMaterials scienceContact angleCoatingDissolutionChemical engineeringAqueous solutionCoated ureaComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Dissolutive spreading over a porous surface is a phenomenon involving spreading, penetration, as well as dissolution. Droplet spreading forms the basis of numerous industrial operations including the spray coating of urea fertilizer to produce slow release urea. The slow release characteristics of urea highly depend on the coating uniformity that can be achieved by sound knowledge of droplet spreading behaviour over the urea surface. Since numerous waterborne coating materials are being studied presently, dissolutive wetting of the urea surface has been investigated in the current work. Response surface methodology has been utilized to study the interactive effects of process parameters on the dissolutive wetting of an aqueous glycerin solution on a porous urea surface. The effect of impact velocity, surface porosity, and urea solubility has been investigated on three response objectives namely contact angle, spreading factor, and normalized residual drop volume. The results have been compared with an earlier work performed under similar conditions for non‐dissolutive wetting. The comparison reveals that dissolutive wetting shows lesser optimum contact angle (81 %) and greater optimum spreading factor (21 %) as compared to non‐dissolutive wetting.

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)0.000
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 score0.002
Threshold uncertainty score0.223

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 head0.019
GPT teacher head0.218
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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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