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Record W4406130898 · doi:10.3389/frmst.2024.1542869

Editorial: Reviews in membrane modules and processes

2025· editorial· en· W4406130898 on OpenAlexaff
Nalan Kabay, Mohammad Mahdi A. Shirazi, Enver Güler, Marek Bryjak

Bibliographic record

VenueFrontiers in Membrane Science and Technology · 2025
Typeeditorial
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceCognitive sciencePsychology

Abstract

fetched live from OpenAlex

The design of membrane modules plays a crucial role in determining the efficiency, scalability, and cost-effectiveness of membrane processes used in various applications such as water treatment, resource recovery, and energy production [1]. A well-optimized module design enhances mass and heat transfer, minimizes fouling, and improves operational stability, making membrane technologies more viable for industrial and municipal use [2,3].The design of membrane modules for membrane processes hinges on several critical parameters to ensure efficiency, durability, and adaptability across various applications. These parameters are shown in Figure 1.Membrane material must be chemically compatible and mechanically durable for long-term performance and cleaning [4]. Module configurations, such as spiral wound or hollow fiber, aim to maximize packing density while ensuring ease of maintenance. A high surface area-tovolume ratio is crucial for enhanced flux but must be balanced against pressure drop considerations [5]. Effective hydrodynamic design ensures uniform flow distribution, reduces dead zones, and minimizes fouling through turbulence promoters or optimized spacers [6].Additionally, fouling and scaling control features, such as anti-fouling coatings or spacer designs, enhance performance and facilitate cleaning, making these parameters integral to robust and efficient module design [7,8].For example, in nanofiltration (NF), module design influences salt rejection rates, flux performance, and energy efficiency, which are critical for applications like softening and desalination [9]. Similarly, in membrane distillation (MD), the module design, including membrane arrangement and thermal integration, significantly impacts the recovery of clean water and valuable resources from challenging feed streams such as brines and industrial effluents [10]. In membrane bioreactors (MBRs), as another example, module design directly affects aeration efficiency, fouling control, and energy consumption, which are crucial for treating municipal and industrial wastewater while maintaining high-quality effluent standards [11,12]. Advances in module designs, such as spacer configurations, hollow fiber membranes, and spiral wound setups, are pivotal for pushing the boundaries of performance and ensuring sustainable and cost-effective solutions in these membrane-based processes [13,14]. The Web of Science Engin shows 15411 hints for the phrase 'membrane module' and counts 946 reviews. This Research Topic covers four review papers on "Membrane modules and processes". The above review papers demonstrate the interdisciplinary fields of membrane science and technology, covering materials, chemistry, chemical engineering and environmental engineering. In addition, these review papers clearly indicate the flexibility of membrane processes in various applications including wastewater treatment and energy production. We consider that much remains to be explored as the field of membrane modules and processes continues to expand.

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)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.050
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0070.007
Open science0.0040.002
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.0500.051

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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2025
Admission routes1
Has abstractyes

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