Rall et al., 2019 - Google Patents
Rational design of ion separation membranesRall et al., 2019
View PDF- Document ID
- 3420864620970426950
- Author
- Rall D
- Menne D
- Schweidtmann A
- Kamp J
- von Kolzenberg L
- Mitsos A
- Wessling M
- Publication year
- Publication venue
- Journal of Membrane Science
External Links
Snippet
Synthetic membranes for desalination and ion separation processes are a prerequisite for the supply of safe and sufficient drinking water as well as smart process water tailored to its application. This requires a versatile membrane fabrication methodology. Starting from an …
- 239000012528 membrane 0 title abstract description 215
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rall et al. | Rational design of ion separation membranes | |
Wang et al. | Pore model for nanofiltration: History, theoretical framework, key predictions, limitations, and prospects | |
Tong et al. | Nanofluidic membranes to address the challenges of salinity gradient power harvesting | |
Lin et al. | Charge inversion and calcium gating in mixtures of ions in nanopores | |
Rall et al. | Simultaneous rational design of ion separation membranes and processes | |
Teodosiu et al. | Neural network models for ultrafiltration and backwashing | |
Rall et al. | Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning | |
Labban et al. | Relating transport modeling to nanofiltration membrane fabrication: Navigating the permeability-selectivity trade-off in desalination pretreatment | |
Liu et al. | Evaluation of membrane fouling models based on bench-scale experiments: a comparison between constant flowrate blocking laws and artificial neural network (ANNs) model | |
Radu et al. | Modeling the effect of biofilm formation on reverse osmosis performance: Flux, feed channel pressure drop and solute passage | |
Lin et al. | Modulation of charge density and charge polarity of nanopore wall by salt gradient and voltage | |
Kotrappanavar et al. | Prediction of physical properties of nanofiltration membranes for neutral and charged solutes | |
Heiranian et al. | Molecular simulations to elucidate transport phenomena in polymeric membranes | |
Shefer et al. | Enthalpic and entropic selectivity of water and small ions in polyamide membranes | |
Zhai et al. | Roles of anion–cation coupling transport and dehydration-induced ion–membrane interaction in precise separation of ions by nanofiltration membranes | |
Dashti et al. | Molecular dynamics, grand canonical Monte Carlo and expert simulations and modeling of water–acetic acid pervaporation using polyvinyl alcohol/tetraethyl orthosilicates membrane | |
Razavi et al. | Dynamic prediction of milk ultrafiltration performance: A neural network approach | |
Ma et al. | Investigation of membrane fouling phenomenon using molecular dynamics simulations: A review | |
Wang et al. | Understanding selectivity in solute–solute separation: definitions, measurements, and comparability | |
Duong et al. | Molecular interactions and layer stacking dictate covalent organic framework effective pore size | |
Xu et al. | Molecular simulations of liquid separations in polymer membranes | |
Darwish et al. | Neural networks simulation of the filtration of sodium chloride and magnesium chloride solutions using nanofiltration membranes | |
Labban et al. | Design and modeling of novel low-pressure nanofiltration hollow fiber modules for water softening and desalination pretreatment | |
Baig et al. | Fractionation of dyes/salts using loose nanofiltration membranes: Insight from machine learning prediction | |
Lee et al. | Prediction of membrane fouling in the pilot-scale microfiltration system using genetic programming |