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Record W3190270203 · doi:10.1002/cjce.24277

A superhydrophobic material based on an industrial solid waste for oil/water separation

2021· article· en· W3190270203 on OpenAlexvenueno aff
Tongyan Ren, Yuechuan Wang, Xiaowei Fu, Liang Jiang, Anqian Yuan, Zhengkai Wei, Hualiang Xu, Jingxin Lei, Ping He, Yao Xiao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
FundersChina West Normal UniversityOpening Project of Key Laboratory of Green Chemistry of Sichuan Institutes of Higher Education
KeywordsContact angleMaterials scienceStearic acidChemical engineeringMunicipal solid wasteComposite materialWaste management

Abstract

fetched live from OpenAlex

Abstract In this study, we employed phosphogypsum, an important solid waste of the phosphorus chemical industry, to prepare a superhydrophobic material for oil/water separation using sodium metasilicate and stearic acid as the chemical modifiers through a facile method. This superhydrophobic material has rough micro–nano structure with low surface energy and possesses an average water contact angle of 151.5° as well as an average water sliding angle of 1.8°, showing extremely low adhesion to water. Due to its superhydrophobicity and superoleophilicity, this material can separate various oil/water mixtures with high separation efficiencies in the range from 92% to 97% by means of a simple apparatus driven by gravity. Additionally, the superhydrophobic material has high resistance to acidic solution and repeated separation, showing the reliability for harsh conditions and long‐term use. This superhydrophobic material prepared based on industrial solid waste is a good example of waste valorization.

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 score0.019
Threshold uncertainty score0.508

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 head0.025
GPT teacher head0.240
Teacher spread0.215 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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