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Integrated Spray Coating for Rapid Production of Thin-Film Composite Membranes with Improved Desalination Efficiency

2025· article· en· W4413096311 on OpenAlexaff
C Poonguzhali, S. D. Jadhav, Vinod Kumari, Bhanu Prakash, S. Arunkumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsPan Am Clinic
Fundersnot available
KeywordsDesalinationMaterials scienceMembraneCoatingComposite numberThin-film composite membraneComposite materialProcess engineeringEngineeringReverse osmosisChemistry

Abstract

fetched live from OpenAlex

To the current literature on synthesis of TFC membranes, this research presents a new and state-of-art technique to achieve spray coating of TFC membrane through one-step process. This three-stage formation process is more efficient than other conventional multi-step methods for forming the selective layer and the porous support because this new technique combines spray-assisted phase inversion and interfacial polymerization into a single process. This considerably minimises fabrication time, following a membrane fabrication process of less than 2 hours as opposed to conventional fabrication of between 2 to 5 days. The following TFC as proposed present high-performance enhancement achieved by obtaining a water permeance of 2.6Lm-2h-1bar compared to traditional membranes while maintaining high salt rejection rate of over 95% when tested with 2000ppm saline solution at moderate pressure and ambient temperatures. Furthermore, this process is sustainable oriented, that reduce using unnecessary material and considering the ecology severely. This revolutionary fabrication technique provides an economical, fast and sustainable process for fabricating highly effective TFC membranes that can lead to potential improvement in desalination technology and finding solution to major water issues in the world.

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)0.000
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 score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.242
Teacher spread0.231 · 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
Published2025
Admission routes1
Has abstractyes

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