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Açık Erişim
Özet
Graphene oxide (GO) membranes present a compelling alternative to the permeability-selectivity trade-off inherent in conventional polymer membranes. However, the incomplete mechanistic understanding and the absence of scalable, defect-controlled fabrication processes continue to hinder their practical deployment. This review synthesizes and integrates transport mechanisms, computational modeling, fabrication, and translational constraints across graphene-based membrane architectures into a comprehensive design-oriented framework. Five key aspects of this synthesis are highlighted. Firstly, the available evidence supports a three-regime transport model, which unifies viscous near-frictionless flow, activated molecular hopping, and solution–diffusion. This reframes selectivity as a tunable function of the C/O ratio and interlayer chemistry. Secondly, a quantitative parity analysis of literature data reveals that classical molecular dynamics tends to overestimate GO laminate water permeance by a representative factor of approximately 3–8× across the matched comparisons examined. This discrepancy can be corrected using a tortuosity–porosity factor derived from wet-state XRD. Machine-learning force fields (GAP, MACE), while still in an early stage of development with limited reported applications, narrow the residual discrepancy to within 1.5–2× in the studies reviewed. Thirdly, a tiered computational roadmap identifies nuclear quantum effects as critical for proton-transport applications but unresolved for water permeance in GO laminate geometry. Fourthly, performance across water nanofiltration, gas separation, ion recovery, and osmotic energy harvesting is benchmarked against commercial references, with explicit caveats regarding the heterogeneity of testing conditions across cited studies, alongside a technology readiness assessment. Lastly, a standardized 500-h hydraulic stability protocol is proposed to facilitate cross-laboratory comparison. Collectively, this synthesis provides a structured, albeit not exhaustively validated, basis for the discussion of next-generation membrane design.
Web of Science Eşleşmesi Bulundu
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Review
Belge Türü
Kaynak: MEMBRANES
Anahtar Kelimeler (WoS)
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Anahtar Kelimeler
graphene oxide membranes
transport mechanisms
interlayer spacing
ion sieving
machine-learning force fields
antifouling
scalable fabrication
WoS |
Bir kelimeye tıklayıp ilgili kaynaktaki yayınları görün.
Makale Bilgileri
Dergi
Membranes
ISSN
2077-0375
Yıl
2026
/ 7. ay
Cilt / Sayı
16
Makale Türü
Özgün Makale
Hakemlik
Hakemli
Endeks
SCI-Expanded
JCR Quartile
Q2
Yayın Dili
Türkçe
Kapsam
Uluslararası
Toplam Yazar
4 kişi
Erişim Türü
Basılı+Elektronik
Alan
Temel Alan
YÖKSİS Yazar Kaydı
Yazar Adı
AYDIN DİDEM,ERSÖZ MUSTAFA,GÜBBÜK İLKAY HİLAL,Alzebair Awad
YÖKSİS ID
9645881