Introduction
Membrane distillation (MD) represents a promising technology for addressing the challenges that humanity is facing with regard to the provision of potable water (Rodell et al., 2018; Vörösmarty et al., 2010). Membrane distillation (MD) presents opportunities for the production of freshwater and the reduction of wastewater discharge in a variety of industrial-scale operations, including seawater/brackish water desalination, ultra-pure water production, process water treatment, and active substance concentration (Drioli et al., 2011; Drioli et al., 2012; Drioli et al., 2015). Efficient MD requires bespoke membranes with well-defined properties that suit the task set out to perform. The advancement of membrane performance can be achieved through the development of novel membranes or through the modification of existing membranes (Peng et al., 2023; Shang et al., 2020).
The MD process is driven by a vapor pressure difference between the feed and permeate streams, which is induced by a temperature gradient across the membrane (Johnson & Nguyen, 2017). The majority of MD membranes are polymeric. Commonly used polymers include polypropylene (PP), polytetrafluoroethylene (PTFE), and polyvinylidene fluoride (PVDF). In the context of MD, a number of quantifiable factors, either affecting wetting resistance or mass and heat flux, are found to be correlated with membrane performance. Wetting resistance is defined by the Liquid Entry Pressure (LEP), as per the Laplace (Cantor) equation:
where B denotes a geometric factor (B=1 for cylindrical pores), γl represents the liquid surface tension, θ signifies the contact angle and rmax is the maximum pore radius. The contact angle (CA) is a measure of the surface energy of the membrane material. The aforementioned hydrophobic polymers provide wetting resistance towards water by exhibiting a low surface energy. The pore diameters of commercially available membranes suitable for MD applications typically range between 0.01 – 1 µm. The mass flux correlates with the pore diameter. Nevertheless, pore diameters > 0.3 µm only moderately enhance mass flux, while increasing the likelihood of wetting by reducing the LEP. The pore size distribution was found to have a negligible effect on flux. Mass flux and heat flux correlate inversely with membrane thickness, indicating the necessity of identifying an optimal thickness. The porosity of MD membranes should be as high as possible, while maintaining the required mechanical strength, given that mass flux correlates with porosity, whereas heat flux correlates inversely with porosity. Porosity is typically regarded as the most significant factor in MD (Eykens et al., 2016; Eykens et al., 2017).
The objective is to optimize membrane performance factors to enhance wetting resistance and flux. This can be achieved by carefully balancing the experimental factors involved in the membrane fabrication process. Each phase separation process, including NIPS, VIPS, EIPS, and TIPS, or a combination thereof, is associated with a distinct set of experimental factors. The type of polymer, the polymer content of the dope solution, the type of solvent, and process conditions, such as temperature and duration of the phase separation process, are common factors that must be considered. When considering NIPS and VIPS, the type of nonsolvent and the duration of the VIPS treatment must also be taken into account as additional experimental factors (Mulder, 1996). The development of new membranes is typically achieved by selecting a set of experimental factors and gradually varying each factor within a specified range while maintaining consistent levels for the remaining factors. The wetting resistance and flux of each specimen are then evaluated according to a defined set of performance factors to identify the most promising specimens. This approach, known as one-factor-at-a-time (OFAT), is not only laborious but also fails to identify interactions between experimental factors that affect performance (Montgomery, 2013). In order to circumvent the shortcomings of OFAT, the design of experiment (DoE) approach may be employed. The design of experiment (DoE) is a systematic approach that efficiently explores multiple factors and their interactions, thereby optimizing processes with minimal trials. The DoE approach saves time and resources, enhances result reliability through statistical rigor, and aids in targeted development by identifying key variables and generating predictive models (Siebertz et al., 2010). Khayet et al., used the DoE method to improve the performance of flat-sheet poly(vinylidene fluoride-co-hexafluoropropylene) membranes, prepared by a combined EIPS/NIPS method, for use in a DCMD application. A fractional factorial design yielded linear models of MD flux and NaCl rejection as functions of the experimental factors, namely the polymer concentration, the PEG additive concentration, the solvent evaporation time, and the coagulant temperature (Khayet et al., 2010). Dehban et al., prepared PPSU/PES/Nano silica composite ultrafiltration membranes by a combined VIPS/NIPS method, to obtain high flux ultrafiltration membranes. RSM models of PWP and bovine serum albumin retention were derived from the experimental factors, namely the blending ratio of PPSU:PES, the polymer concentration, the SiO2 nanoparticle concentration, and the VIPS duration (Dehban et al., 2020).
An additional strategy for improving membrane functionality is the incorporation of additives during the manufacturing process and/or the modification of existing membranes (Gontarek-Castro et al., 2022; Sadrzadeh & Bhattacharjee, 2013). In 2011, a novel type of artificial water channel (AWC) was developed. The design of AWCs is inspired by the structure of biological aquaporins. Aquaporins are integral cell membrane proteins that serve as channels for the transport of water molecules across cell membranes. The AWCs are constructed from hexylureido-ethyl-imidazole (HC6) monomers, among other materials. HC6 undergoes self-assembly to form scaffolds comprising oligomeric, planar imidazole-quartets (I-quartets). The resulting solid supramolecular tubular architecture is observed to surround one-dimensional water wires. The AWC display water-channel conductance states in bilayer membranes, exhibiting trans-membrane water channel behavior that is analogous to that observed in aquaporins (Le Duc et al., 2011; Licsandru et al., 2016). In 2021, the AWC were successfully incorporated into composite polyamide membranes for the purpose of water desalination. Colloidal AWC aggregates were obtained by sonication of an ethanolic aqueous solution of HC6. The AWC aggregates were dispersed on the active surface of a commercial polysulfone support. Subsequently, a thin-film composite polyamide membrane was synthesized on the support by conventional interfacial polymerization. It was demonstrated that the AWCs were successfully incorporated into and remained within the PA matrix. The modified membranes demonstrated a 75% improvement in flux relative to the SOTA commercial membranes under reverse osmosis desalination conditions (Di Vincenzo et al., 2021). Recently, we investigated the impact of HC6 incorporation into PVDF/DMAc dope on the fabrication of UF membranes via NIPS. The incorporation of CH6 into the membrane matrix was confirmed by FTIR-ATR, TGA, and DSC. The orientation of the amphiphilic HC6 molecule, with its hydrophobic tail oriented towards the PVDF matrix and its hydrophilic head oriented towards the pore space, was confirmed by scanning transmission electron microscopy-energy dispersive X-ray spectroscopy (STEM-EDX). Furthermore, STEM-EDX revealed that clusters of water molecules tend to agglomerate within the void space between adjacent PVDF/HC6 crystallites, thereby indicating a hydrophilization of the membrane pores. The presence of AWC clusters within the membrane matrix was confirmed by XRD analysis. It was proposed that the initial mass flux of water into the cast dope solution and the initial mass flux of DMAc into the precipitation bath is high during NIPS. Consequently, near-instantaneous liquid-liquid demixing occurs at the interface between the precipitation bath and the dope solution. The polymer-rich phase solidifies rapidly, forming a dense outer layer. This leaves the HC6 molecules with insufficient time to form AWC aggregates. During the formation of the skin, the hydrophobic tails of the amphiphilic HC6 molecule tend to become embedded in the emerging PVDF matrix, while the hydrophilic heads are oriented towards the polymer-lean phase. Once formed, the skin impedes the influx of additional water and the outflux of DMAc across the interface. This results in a delay to the phase separation of the layers beneath the skin, thereby enabling the in-situ formation of AWC clusters from HC6. The AWC clusters are incorporated into the emerging PVDF/HC6 matrix, while also associating with imidazole heads protruding from the PVDF/HC6 matrix. This hydrophilization of the polymer-rich phase during membrane formation has been demonstrated to increase membrane porosity. The PVDF/HC6 hybrid membranes are true mixed matrix membranes, comprising networks of PVDF cores covered with PVDF/HC6 hybrid material on the surface. Additionally, AWC nanoparticles are present on the inner pore surfaces and are associated with it, creating a hydrophilic pore space and consequently enhancing water permeability (Nursiah et al., 2023). The performance factors of the PVDF/HC6 hybrid membranes compared to pure PVDF membranes are shown in Table 1.
Table 1. A summary of the membrane performance factors of the best performing membranes prepared by NIPS.
| Membrane preparation | NIPS (Nursiah et al., 2023) |
|---|
| Membrane material | PVDF | PVDF/HC6 |
| PWP | 30.6 | 75.3 |
| LEP | 0.7 | 0.2 |
| MD flux | 48.9 | 54.1 |
| NaCl rejection | 98.4 | 99.8 |
This study expands upon our previous publication, which detailed the effects of incorporating HC6-based AWC nanoparticles into PVDF UF membranes for MD prepared by NIPS. In particular, the impact of a VIPS pretreatment has been evaluated with the objective of enhancing the membrane performance in terms of LEP and NaCl rejection, while maintaining the PWP and MD flux. To that end, four experimental factors were controlled and varied according to a DoE methodology. The factors investigated were the polymer concentration and HC6 concentration in the dope solution, the relative humidity (RH) of the VIPS stage, and the duration of the VIPS treatment. Four regression models based on RSM have been proposed with the objective of establishing a link between the experimental factors and the four membrane performance characteristics. The statistical analysis demonstrated that the performance of the membranes in terms of PWP and LEP was reasonably predictable. Qualitative predictive capacity was observed for the MD flux, whereas no predictive capacity was evident for the NaCl rejection. These findings corroborate the previously demonstrated efficacy of HC6 incorporation with respect to the enhancement of PWP and MD flux. Furthermore, the VIPS pretreatment significantly increased the LEP for both the pure PVDF and the PVDF/HC6 hybrid membranes, while maintaining NaCl rejection at 99.9%. It has been demonstrated that the DoE methodology and RSM modeling have the potential to reduce the necessary experimental effort while simultaneously establishing quantifiable correlations between experimental variables and observed outcomes.
Methods
Materials
PVDF pellets (CAS No.: 24937-79-9; MW = 530 000 g/mol) and N,N-Dimethylacetamide (CAS No.: 127-19-5) were provided by Sigma-Aldrich Germany. Laboratory produced distilled water (18 MΩ.cm-1) was used as coagulation bath for membrane preparation and in the DCMD experiments. The HC6 AWC were synthesized according to a procedure described elsewhere (Le Duc et al., 2011). All experimental designs, their analyses and modelling were performed using Minitab v. 20.3.
Membrane preparation
VIPS-NIPS assisted membrane preparation was performed according to the following protocol. Dope solutions, with compositions summarized in Table 4, were prepared by dissolving the PVDF pellets in N,N-dimethylacetamide (DMAc) for 6 h at a dissolution temperature Td of 55°C, followed by a degassing period of 18 h at room temperature (22°C). The stainless-steel support used for dope casting was previously fixed in a temperature-controlled vapor box maintained at 25 °C. Figure 1 shows a process flow diagram of the VIPS pretreatment. The humidity in the vapor box was controlled by a compressed air piping system consisting of two wet lines and one dry line. The wet lines each consisted of a 1 L double-jacketed glass frit impinger. Thus, the desired relative humidity (RH) in the vapor box was set by adjusting the air flow rates in the wet and dry lines. The temperature and air flow rate in the box were held constant at 22°C and 7.7 L/min, respectively, and the humidity and exposure time were varied according to the values specified by the DoE. The flow rate of the conditioned air stream, its relative humidity, and the pressure and temperature of the air stream were constantly monitored. The degassed dope solution was cast onto the support using a casting knife (Micrometer Adjust Applicator 400 mm) with a gap size of 250 µm. After casting, the vapor box was closed and the nascent membrane was exposed to humid air for the desired time. The support was then removed from the vapor box into a dry atmosphere (relative humidity <18%). The steel plate was immersed in a coagulation bath (distilled water at 25 °C) for 24 h. The resulting membranes were dried in a stack. Each membrane was laid flat on a stainless-steel plate and held in place by four rectangular blocks of PTFE placed on each of the four edges. The plates with wet membrane sheets and blocks were then stacked. The PTFE blocks have horizontal holes so that a constant flow of dry air was blown horizontally through the stack by a fan to ensure a gentle and uniform drying process.
Figure 1. Process flow diagram of VIPS pretreatment.
Membrane characterization
Morphological analysis by Scanning Electron Microscopy (SEM). The top surface and cross-section of the membranes were examined by means of scanning electron microscopy (SEM). The images were taken with a MEB Hitachi S4800, with a resolution of 1 nm at 15 kV and capable of an acceleration voltage range of 0.1 kV to 30 kV.
Pure Water Permeability (PWP) and Liquid Entry Pressure (LEP). The PWP permeability measurements were carried out according to the following protocol, which is also described in detail in our previous work (Nursiah et al., 2023).
The LEP was determined together with the pure water permeability to characterize the wetting resistance of the membranes. For this purpose, a water-filled dead-end filtration setup was used. The membrane was placed inside the cell and the pressure applied on the feed side was increased stepwise in 0.1 bar increments after two minutes of stabilization time after each step. After reaching the LEP, the membrane was compressed at 4.0 bar for 45 min. After compression, the pressure was decreased in 0.5 bar increments and the water flux was recorded and plotted as a function of transmembrane pressure. The PWP of the membrane was determined by considering the gradient of the slope of the obtained linear relationship.
Direct Contact Membrane Distillation (DCMD). The DCMD performance was investigated according to the following protocol, as described in our previous work (Nursiah et al., 2023).
A pilot-scale MD setup was used for the DCMD experiments. The self-supporting flat sheet membranes were tightly clamped between two Teflon plates, leaving an effective operating area of 28.6 cm2. An aqueous sodium chloride solution (35 g/L) constituted the feed side, and the permeate was directly recovered in the circulating cooling system (18 MΩ.cm water). The feed inlet temperature Tf was fixed at 70°C (± 2°C) and the distillate inlet temperature Tp was maintained at 10°C (± 1°C). Two independent peristaltic pumps were used to circulate the feed and permeate sides. The pump for the former helped maintain a flow rate of 55 L/h. The main operating conditions used in the DCMD experiments are listed in Table 2.
Table 2. Operating conditions used in DCMD setup.
| Parameter | Range |
|---|
Membrane module
Support
Feed temperature [°C]
Feed flow rate [L/h]
Permeate temperature [°C]
Permeate flow rate[L/h] | Flat
No
70 ± 2
55
10 ± 1
55 |
Throughout the DCMD process, the distilled permeate was diluted in the cold circulating water. The diluted solution was collected and weighed after an average period of 3 h 30 min. The NaCl concentration Cp collected in the distilled permeate was monitored by conductivity σ (S.m-1) (Conductometer VWR ® Phenomenal ® MU 6100H) and calculated according to a calibration curve (Cp=σ/5.5758). Finally, the water permeate flux was calculated according to Equation 1,
where Jw represents the water permeate flux (kg/m2∙h), Δm is the permeate weight (kg), A is the effective membrane surface area (m2) and t is the experiment duration (h).
The NaCl rejection R was determined according to Equation 2,
where Cp and Cf represent the NaCl concentration in the permeate and the feed, respectively.
Model building and statistical analysis
A statistical approach such as RSM uses regression analysis to derive a model equation from the data that expresses the relationship between a response of interest, y (called the response variable), and a set of experimental parameters (called factors), denoted x1, x2, … , xk. Each experimental factor has several values, called levels. Each response is considered as a function of the factors according to Equation 3, where ε represents the random experimental error observed in the response yi.
A second-order response surface model describes the functional relationship between a response variable and the experimental factors by recognizing main effect coefficient βi, a quadratic effect coefficient βii, and a binary interaction coefficient βij, according to Equation 4. An effect quantifies the average change in a response variable with a change in factor level.
Each experimental run yields a model equation that describes the effect of a particular combination of factor levels on a particular response variable. A regression model of a particular response is the solution to the system of equations resulting from the sum of the experimental runs. An analysis of variance (ANOVA) of the total sum of squares evaluates the statistical significance and accuracy of the individual effect coefficients of a model. Each regression model includes only effects with a p-value less than the significance level α < 0.1. Those are color-coded blue, in the corresponding Pareto charts. Various residual plots were used to assess the validity and reliability of the regression models to ensure that the model assumptions of normality of residuals, constant variance of residuals and independence of errors were met. The adjusted coefficient of determination Adj R² compared the model fits. The utility of appropriate regression models for membrane performance optimization was tested by producing multiple response-optimized (MRO) membranes. In MRO, a desirability function simultaneously optimizes the factor levels of multiple regression models to yield a composite desirability.
First, each response yi is converted into a desirability function di that varies over the range of
where if the response y is at its target, then d = 1, and if y is outside an acceptable range, then d = 0. Then the factor levels are chosen to maximize the overall desirability
for m responses (Montgomery, 2008).
Experimental design
A major goal of model building is to optimize the response variables by identifying desirable combinations of factor levels. A full factorial Central Composite Circumscribed Design (CCCD) is a form of DoE that examines the factor space uniformly and measures each factor at five levels, allowing the fitting of the second-order RSM described above. This approach has several advantages over the common OFAT method. It significantly reduces the experimental workload; it reveals quadratic effects and binary factor interactions; it does not miss optimal factor combinations because all factors are compared to each other on all levels (Hubadillah et al., 2018).
In this case study, the investigated factor space consists of four experimental factors, also called predictors, on five levels each. The factors are the mass fraction of polymer in the dope solution wP, the mass fraction of HC6 additive in the dope solution wHC6, the duration of the VIPS pretreatment tVIPS, and the relative humidity during the VIPS pretreatment RH. Table 3 summarizes the factors and their respective values. The range of factor levels was determined in accordance with the literature, specifically.
Table 3. Each of the four experimental factors of the DoE is varied on 5 levels.
Polymer mass fraction
wP / wt.-% | Additive mass fraction
wHC6 / wt.% | VIPS duration
tVIPS / s | Relative humidity
RH / % |
|---|
| 14 | 0 | 180 | 70 |
| 16 | 0.025 | 360 | 75 |
| 18 | 0.05 | 540 | 80 |
| 20 | 0.075 | 720 | 85 |
| 22 | 0.1 | 900 | 90 |
A full factorial CCCD with four factors on five levels each results in 30 experimental runs (prepared membranes). The CCCD consist of 16 cube points with 4 center points in the cube and 8 axial points with 2 center points in the axial. At the center point, each factor level represents the mean of its range. The 6 center points are thus repeated measurements. Center points in DoE are essential for detecting non-linearity and estimating the experimental error, providing a basis for discussing model adequacy and statistical validity. By comparing the results of the center points with those of the cube points and the axial points, they help identify model curvature, confirm model appropriateness, and ensure the reliability of the experiment's conclusions. A randomized running order, pooled in three blocks, ensures independent experiments and minimizes systematic errors. Each block contains two center point replications. Table 4 lists the 24 individual factor combinations of the cube points and the axial points, as well as the 6 center point replication measurements comprising the CCCD.
Table 4. The CCCD comprises of 30 experimental runs, yielding 30 membranes.
Of these, 24 membranes were produced using unique factor level combinations, while the remaining 6 membranes were generated from identical center point replication.
Membrane
code | Block | Experimental factors and their respective levels |
|---|
| | Polymer mass fraction
wP /wt.-% | Additive mass fraction
wHC6 / wt.% | VIPS duration
tVIPS / s | Relative humidity
RH / % |
|---|
| M-1a | 1 | 16 | 0.025 | 360 | 85 |
| M-1b | 1 | 16 | 0.025 | 720 | 75 |
| M-1c | 1 | 16 | 0.075 | 360 | 75 |
| M-1d | 1 | 16 | 0.075 | 720 | 85 |
| M-1e* | 1 | 18 | 0.05 | 540 | 80 |
| M-1f* | 1 | 18 | 0.05 | 540 | 80 |
| M-1g | 1 | 20 | 0.025 | 360 | 75 |
| M-1h | 1 | 20 | 0.025 | 720 | 85 |
| M-1i | 1 | 20 | 0.075 | 360 | 85 |
| M-1j | 1 | 20 | 0.075 | 720 | 75 |
| M-2a | 2 | 16 | 0.025 | 360 | 75 |
| M-2b | 2 | 16 | 0.025 | 720 | 85 |
| M-2c | 2 | 16 | 0.075 | 360 | 85 |
| M-2d | 2 | 16 | 0.075 | 720 | 75 |
| M-2e* | 2 | 18 | 0.05 | 540 | 80 |
| M-2f* | 2 | 18 | 0.05 | 540 | 80 |
| M-2g | 2 | 20 | 0.025 | 360 | 85 |
| M-2h | 2 | 20 | 0.025 | 720 | 75 |
| M-2i | 2 | 20 | 0.075 | 360 | 75 |
| M-2j | 2 | 20 | 0.075 | 720 | 85 |
| M-3a | 3 | 14 | 0.05 | 540 | 80 |
| M-3b | 3 | 18 | 0 | 540 | 80 |
| M-3c | 3 | 18 | 0.05 | 180 | 80 |
| M-3d | 3 | 18 | 0.05 | 540 | 70 |
| M-3e* | 3 | 18 | 0.05 | 540 | 80 |
| M-3f* | 3 | 18 | 0.05 | 540 | 80 |
| M-3g | 3 | 18 | 0.05 | 540 | 90 |
| M-3h | 3 | 18 | 0.05 | 900 | 80 |
| M-3i | 3 | 18 | 0.1 | 540 | 80 |
| M-3j | 3 | 22 | 0.05 | 540 | 80 |
Results and discussion
In the following sections, a detailed ANOVA of the models derived from RSM evaluates their accuracy and statistical significance. Subsequently, the predictive power the models is investigated by MRO. The performance improvement of the MRO membranes was 7.0 times for PWP and 3.3 times for MD flux over the average performance of the DoE membranes, while maintaining LEP of 1.9 bar and NaCl rejection of 99.9%. In addition, PVDF/HC6 hybrid MRO membranes improved PWP by 3.2 times and MD flux by 1.6 times over pure PVDF MRO membranes. Lastly, the performance differences of the optimized membranes are discussed on the basis of SEM images of the surfaces and cross-sections.
Results of CCCD and RSM modelling
To effectively optimize membrane performance and to investigate the potential of the HC6 additive to further enhance performance, RSM was applied to the results of the CCCD. Table 5 lists the measured performance parameters of each membrane produced by the DoE, namely LEP, PWP, MD flux, and NaCl rejection. The range and mean values of each performance parameter are provided below the individual results. The upper end of each range was used as a benchmark for the ensuing MRO. The mean and the measurement uncertainty (n = 6, 95% confidence) of the center points are also provided in Table 5. The measurement uncertainty of the center point means indicates the reproducibility of the entire experiment. The data indicate a good reproducibility of the NaCl rejection and the MD flux, a moderate reproducibility of the LEP and a rather poor reproducibility of the PWP. A quadratic regression model, was fitted to the results of each of the four response parameters. Equation 6, Equation 7, Equation 8 and Equation 9 show the RSM models for PWP, LEP, MD flux, and NaCl rejection, respectively, in uncoded units. The statistical significance of each model and of its constituents is determined by ANOVA.
Table 5. Each of the 30 membranes produced by the CCCD and their respective performance as characterized by LEP, PWP, MD flux, and NaCl rejection.
The range and mean values for each performance parameter are presented in the two lower rows.
Membrane
code | Results of the characterization of the DoE membranes |
|---|
| LEP / bar | PWP / Lm-2h-1bar-1 | MD flux / Lm-2h-1 | NaCl rejection / % |
|---|
| M-1a | 1.5 | 16.2 | 0.1 | 99.9 |
| M-1b | 1.9 | 1.5 | 2.8 | 99.9 |
| M-1c | 3.4 | 3.6 | 2.6 | 99.9 |
| M-1d | 0.8 | 58.6 | 4.9 | 99.9 |
| M-1e* | 1.0 | 22.5 | 4.7 | 99.9 |
| M-1f* | 2.7 | 9.2 | 5.1 | 99.9 |
| M-1g | 3.9 | 0.8 | 2.7 | 99.9 |
| M-1h | 2.3 | 3.0 | 3.1 | 99.9 |
| M-1i | 2.2 | 0.9 | 4.0 | 93.9 |
| M-1j | 2.4 | 4.2 | 4.7 | 99.9 |
| M-2a | 3.7 | 8.4 | 2.0 | 99.9 |
| M-2b | 1.4 | 70.8 | 1.2 | 99.9 |
| M-2c | 1.9 | 10.1 | 0.2 | 99.9 |
| M-2d | 0.8 | 21.4 | 0.2 | <10.0 |
| M-2e* | 0.9 | 22.4 | 4.4 | 99.9 |
| M-2f* | 2.6 | 3.6 | 3.9 | 99.9 |
| M-2g | 3.3 | 1.5 | 2.5 | 99.9 |
| M-2h | 2.0 | 15.5 | 0.5 | 99.9 |
| M-2i | 2.4 | 4.5 | 2.0 | 98.5 |
| M-2j | 0.9 | 27.9 | 2.7 | 87.1 |
| M-3a | 0.4 | 68.3 | 2.2 | <10.0 |
| M-3b | 2.3 | 13.6 | 0.5 | 72.2 |
| M-3c | 3.4 | 6.1 | 1.8 | <10.0 |
| M-3d | 0.4 | 8.3 | 2.8 | 99.9 |
| M-3e* | 2.2 | 15.3 | 5.8 | 99.9 |
| M-3f* | 2.4 | 42.3 | 5.2 | 99.9 |
| M-3g | 2.0 | 6.2 | 2.8 | 99.9 |
| M-3h | 2.6 | 8.8 | 0.5 | 99.9 |
| M-3i | 2.0 | 8.6 | 0.2 | 99.9 |
| M-3j | 2.6 | 3.1 | 0.6 | 17.4 |
| Range of all results | 0.1 – 3.9 | 0.8 – 70.8 | 0.1 – 5.8 | <10 – 99.9 |
| Mean of all results | 2.1 | 16.2 | 2.5 | 86.6 |
| Mean of center points | 2.0 ± 0.8 | 19.2 ± 13.0 | 4.8 ± 0.6 | 99.9 ± 0 |
The ANOVA of the PWP model reported in Table 6 indicates that 42.77% of the variance is accounted for by linear terms, primarily wP (26.61%) and tVIPS (10.14%), followed by 13.87% from two 2-way interactions and 6.75% from square wP. These statistically significant predictors are highlighted in blue in Figure 2 (a). The residuals appear to be independent (Figure 2 (b)) and normally distributed (Figure 2 (c)), except that Figure 2 (d) shows four outliers representing comparatively large PWP values, possibly due to defects in the membrane. The model fit is 63.4%, but the error due to Lack-of-Fit is not statistically significant with a p value of 0.686. The PWP model performance as a function of factor levels is plotted in Figure 6 (a).
Table 6. The ANOVA of the PWP model details how the various sources contribute to the model.
| Source | DF | Seq SS | Contribution / % | Adj SS | Adj MS | F-Value | P-Value |
|---|
| Model | 6 | 6844.8 | 63.40 | 6844.8 | 1140.8 | 6.64 | 0.000 |
| Linear | 3 | 4618.0 | 42.7 | 4618.0 | 1539.3 | 8.96 | 0.000 |
| wP | 1 | 2872.8 | 26.61 | 2872.8 | 2872.8 | 16.72 | 0.000 |
| tVIPS | 1 | 1095.1 | 10.14 | 1095.1 | 1095.1 | 6.37 | 0.019 |
| RH | 1 | 650.0 | 6.02 | 650.0 | 650.0 | 3.78 | 0.064 |
| Square | 1 | 729.0 | 6.75 | 729.0 | 729.0 | 4.24 | 0.051 |
| wP*wP | 1 | 729.0 | 6.75 | 729.0 | 729.0 | 4.24 | 0.051 |
| 2-Way Interaction | 2 | 1497.8 | 13.87 | 1497.8 | 748.9 | 4.36 | 0.025 |
| wP*RH | 1 | 791.9 | 7.33 | 791.9 | 791.9 | 4.61 | 0.043 |
| tVIPS*RH | 1 | 706.0 | 6.54 | 706.0 | 706.0 | 4.11 | 0.054 |
| Error | 23 | 3951.6 | 36.60 | 3951.6 | 171.8 | | |
| Lack-of-Fit | 20 | 3322.4 | 30.77 | 3322.4 | 166.1 | 0.79 | 0.686 |
| Pure Error | 3 | 629.1 | 5.83 | 629.1 | 209.7 | | |
| Total | 29 | 10796.3 | 100.00 | | | | |
| Std. Dev | 13.11 | | | | | | |
| PRESS | 7290.1 | | | | | | |
| R² | 0.634 | | | | | | |
| Adj R² | 0.539 | | | | | | |
| Pred R² | 0.325 | | | | | | |
Figure 2. Effects and residual plots for PWP model.
(a) The Pareto chart highlights the statistically significant predictors (α < 0.1) of the PWP performance, represented in blue. The residuals of the PWP model are shown in relation to the run order (b), a normal probability distribution (c), and the fitted values (d).
The LEP model explains 31.78% of the variance with linear terms, namely tVIPS (20.59%) and wP (11.18%), and 10.16% with a square tVIPS term. These sources represent the significant effects (Figure 3 (a)). The validity and reliability of the model are sufficient according to Figure 3 (b) – (d). The model fit is 42.4%, but the main error due to Lack-of-Fit is not statistically significant with a p-value of 0.829, as summarized in Table 7. Figure 6 (b) plots the LEP model as a function of factor levels.
Figure 3. Effects and residual plots for LEP model.
(a) The Pareto chart highlights the statistically significant predictors (α < 0.1) of the LEP performance, represented in blue. The residuals of the LEP model are shown in relation to the run order (b), a normal probability distribution (c), and the fitted values (d).
Table 7. The ANOVA of the LEP model details how the various sources contribute to the model.
| Source | DF | Seq SS | Contribution / % | Adj SS | Adj MS | F-Value | P-Value |
|---|
| Model | 3 | 11.143 | 42.38 | 11.143 | 3.7142 | 6.37 | 0.002 |
| Linear | 2 | 8.355 | 31.78 | 8.355 | 4.1775 | 7.17 | 0.003 |
| wP | 1 | 2.940 | 11.18 | 2.940 | 2.9400 | 5.05 | 0.033 |
| tVIPS | 1 | 5.415 | 20.59 | 5.415 | 5.4150 | 9.29 | 0.005 |
| Square | 1 | 2.788 | 10.60 | 2.788 | 2.7876 | 4.78 | 0.038 |
| tVIPS*tVIPS | 1 | 2.788 | 10.60 | 2.788 | 2.7876 | 4.78 | 0.038 |
| Error | 26 | 15.151 | 57.62 | 15.151 | 0.5827 | | |
| Lack-of-Fit | 23 | 12.241 | 46.56 | 12.241 | 0.5322 | 0.55 | 0.829 |
| Pure Error | 3 | 2.910 | 11.07 | 2.910 | 0.9700 | | |
| Total | 29 | 26.294 | 100.00 | | | | |
| Std. Dev | 0.76 | | | | | | |
| PRESS | 21.1 | | | | | | |
| R² | 0.424 | | | | | | |
| Adj R² | 0.357 | | | | | | |
| Pred R² | 0.199 | | | | | | |
The MD flux model includes three quadratic terms, namely wHC6*wHC6 (23.0%), tVIPS*tVIPS (16.84%), and wP*wP (7.63%). The quadratic terms all show significant effect sizes (Figure 4 (a)). This suggests an optimum of MD performance for intermediate factor levels, as shown in Figure 6 (c), to which all factors contribute significantly. Another source of statistically significant variance is a block term with a contribution of 14.45%. The residual analysis is unremarkable (Figure 4 (b) – (d)). The model fits the data to 61.9% and the Lack-of-Fit error is statistically significant with a p-value of 0.031, concluding that the model does not correctly specify the relationship between response and predictor (see Table 8).
Figure 4. Effects and residual plots for MD flux model.
(a) The Pareto chart highlights the statistically significant predictors (α < 0.1) of the MD flux performance, represented in blue. The residuals of the MD flux model are shown in relation to the run order (b), a normal probability distribution (c), and the fitted values (d).
Table 8. The ANOVA of the MD flux model details how the various sources contribute to the model.
| Source | DF | Seq SS | Contribution / % | Adj SS | Adj MS | F-Value | P-Value |
|---|
| Model | 5 | 54.7262 | 61.93 | 54.7262 | 10.9452 | 7.81 | 0.000 |
| Blocks | 2 | 12.7708 | 14.45 | 12.7708 | 6.3854 | 4.55 | 0.021 |
| Square | 3 | 41.9553 | 47.48 | 41.9553 | 13.9851 | 9.98 | 0.000 |
| wP*wP | 1 | 6.7454 | 7.63 | 12.5324 | 12.5324 | 8.94 | 0.006 |
| wHC6*wHC6 | 1 | 20.3288 | 23.00 | 24.5578 | 24.5578 | 17.52 | 0.000 |
| tVIPS*tVIPS | 1 | 14.8811 | 16.84 | 14.8811 | 14.8811 | 10.61 | 0.003 |
| Error | 24 | 33.6460 | 38.07 | 33.6460 | 1.4019 | | |
| Lack-of-Fit | 21 | 33.2528 | 37.63 | 33.2528 | 1.5835 | 12.08 | 0.031 |
| Pure Error | 3 | 0.3932 | 0.44 | 0.3932 | 0.1311 | | |
| Total | 29 | 88.3721 | 100.00 | | | | |
| Std. Dev | 1.18 | | | | | | |
| PRESS | 49.4 | | | | | | |
| R² | 0.619 | | | | | | |
| Adj R² | 0.540 | | | | | | |
| Pred R² | 0.441 | | | | | | |
The ANOVA of the NaCl rejection model in Table 9 suggests only square wP (29.93%) as a source of variation with a significant effect size (Figure 5 (a)), suggesting an optimal factor level within the factor space examined. Furthermore, a block contribution of 15.59% with a p value of 0.038 suggests a change in response due to different conditions between runs. The residuals vs. run plot in Figure 5 (b) shows several severe outliers, which are further confirmed by Figure 5 (c) & (d). The pattern suggests a violation of the model assumptions proposed in the section “Model building and statistical analysis”. NaCl rejection was consistently 99.9%, except for a few runs where it was <10%. This suggests defective membranes as a source of poor rejection. The RSM is not suitable for modelling this type of behavior.
Table 9. The ANOVA of the NaCl rejection model details how the various sources contribute to the model.
| Source | DF | Seq SS | Contribution / % | Adj SS | Adj MS | F-Value | P-Value |
|---|
| Model | 3 | 12149.1 | 45.53 | 12149.1 | 4049.68 | 7.24 | 0.001 |
| Blocks | 2 | 4161.0 | 15.59 | 4161.0 | 2080.52 | 3.72 | 0.038 |
| Square | 1 | 7988.0 | 29.93 | 7988.0 | 7988.00 | 14.29 | 0.001 |
| wP*wP | 1 | 7988.0 | 29.93 | 7988.0 | 7988.00 | 14.29 | 0.001 |
| Error | 26 | 14536.6 | 54.47 | 14536.6 | 559.10 | | |
| Lack-of-Fit | 23 | 14536.6 | 54.47 | 14536.6 | 632.02 | * | * |
| Pure Error | 3 | 0.0 | 0.00 | 0.0 | 0.00 | | |
| Total | 29 | 26685.6 | 100.00 | | | | |
| Std. Dev | 23.65 | | | | | | |
| PRESS | 18569.9 | | | | | | |
| R² | 0.455 | | | | | | |
| Adj R² | 0.392 | | | | | | |
| Pred R² | 0.304 | | | | | | |
Figure 5. Effects and residual plots for NaCl rejection model.
(a) The Pareto chart highlights the statistically significant predictor (α < 0.1) of the NaCl rejection performance, represented in blue. The residuals of the NaCl rejection model are shown in relation to the run order (b), a normal probability distribution (c), and the fitted values (d).
The four regression models and the comparison of their errors and their coefficients of correlation are summarized in Figure 6 and Table 10, respectively. Each model is plotted as a function of the four experimental factors. The predictors are on the x-axis and the responses are on the y-axis. A plot with a grey background indicates the absence of a statistically significant relationship between predictor and response. The PWP shown in Figure 6 (a) improves with decreasing wP, which is expected as porosity increases. The lack of relationship between PWP performance and HC6 content is puzzling, but a potential effect may be buried in the noise of the data. This is investigated in the section “Results of Multiple Response-Optimization”. In addition, PWP increases linearly with increasing tVIPS and RH. The hypothesis that longer tVIPS at higher RH promotes delayed demixing leading to skin defects will be tested in the section “Morphological analysis”. The plots in Figure 6 (b) show an increasing LEP with increasing wP and decreasing tVIPS. This behavior is reasonable as both actions promote skin densification. HC6 has no statistically significant effect on LEP. At first glance, no effect would be expected as LEP is mainly dependent on the porosity and pore size of the skin. However, the possible effects of HC6 on membrane morphology are discussed in the section “Morphological analysis”. The dependence of LEP on RH cannot be modelled due to the step response between 70% and 75% RH. The MD flux regression model in Figure 6 (c) shows optimal flux for intermediate factor levels of wP, wAWC and tVIPS. This suggests good specified factor level ranges. RH appears to have an inconsistent effect on MD flux. In the case of NaCl rejection, no statistically significant relationships are expected due to the inadequacy of the RSM to fit the data, see Figure 6 (d).
Figure 6. Each model is plotted as a function of the four predictor variables.
The x-axis of each predictor shows its range. A grey background signifies no statistically significant dependence of the predictor on the respective model. (a) The PWP performance is determined by wP, tVIPS, and RH. (b) The LEP performance is determined by wP, and tVIPS. (c) The MD flux performance is determined by wP, wHC6, and tVIPS. (d) The NaCl rejection model is determined only by wP.
Table 10. The Lack-of-Fit error and the adjusted coefficient of correlation of the four regression models.
| Model | PWP | LEP | MD flux | NaCl rejection |
|---|
| Lack-of-Fit | insignificant | insignificant | significant | significant |
| Adj R² | 0.539 | 0.357 | 0.540 | 0.3924 |
According to Table 10, the models for MD flux and NaCl rejection have statistically significant Lack-of-Fit errors, meaning that these models do not correctly specify the relationship between response and predictors. A likely cause could be that the experiment contained an uncontrolled predictor variable. This proposition is supported by the fact that the ANOVA of both models shows statistically significant contributions of blocks as a source of variance. The Lack-of-Fit is not statistically significant for the remaining models, namely PWP and LEP. Overall, the adjusted coefficient of determination is below our expectations. Due to the inadequacy of the RSM model to fit the NaCl rejection data it is excluded from further investigation and verification.
Results of Multiple Response-Optimization
To test the predictive capabilities of the remaining three models and to elucidate the effect of HC6 on membrane performance, two multiple response-optimized (MRO) membranes were prepared. Each was part of a set of four additional membranes prepared at the MRO predicted factor levels, but with varying HC6 weight fractions. Finally, both sets consisted of one pure PVDF membrane and four PVDF/HC6 hybrid membranes with 0.025, 0.05, 0.075, and 0.1 wt. % HC6, respectively. The performance of the membranes was evaluated as described in the section “Membrane characterization”.
The first MRO was fitted using the PWP and LEP model. The objective was to maximize PWP while LEP was set at a target of 2.0 bar, which is suitable for MD application. The MRO recommends factor levels to achieve the selected response targets. Table 11 summarizes the experimental parameters of the first MRO membrane set.
Table 11. The dope solution compositions and process conditions of the VIPS pretreatment of first set of MRO membranes.
| Membrane code | Polymer mass fraction
wP/wt.-% | Additive mass fraction
wHC6/wt.% | VIPS duration
tVIPS | Relative humidity
RH / % |
|---|
| M-T0* | 17.3 | 0 | 900 | 90 |
| M-T1 | 17.3 | 0.025 | 900 | 90 |
| M-T2 | 17.3 | 0.050 | 900 | 90 |
| M-T3 | 17.3 | 0.075 | 900 | 90 |
| M-T4 | 17.3 | 0.100 | 900 | 90 |
The predicted targets of the first MRO, along with the Standard Error (SE Fit), 95% Confidence Interval (95% CI), and 95% Prediction Interval (95% PI) of the fitted target values are shown in Table 12. The Standard Error of the Fit (SE Fit) estimates the variation in the estimated mean response for the specified factor levels. The Confidence Interval (CI) provides a range of likely values for the mean response given the specified factor levels. The Prediction Interval (PI) is a range that is likely to contain a single response for the specified factor levels.
Table 12. The model prediction for the first set of MRO membranes.
| Response | Target | SE Fit | 95% CI | 95% PI |
|---|
| PWP / Lm-2h-1bar-1 | 72.1 | 15.6 | 39.8 – 104.3 | 29.9 – 114.2 |
| LEP / bar | 2.0 | 0.6 | 0.8 – 3.2 | 0.1 – 4.0 |
Table 13 summarizes the membrane performance of the first MRO set. Green underlay indicates values within the 95% CI, orange within the 95% PI. This indicates that the predictive capabilities of the PWP and LEP models are within useful limits, although the MRO overpredicted membrane performance in both cases. An increase in PWP and MD flux performance was reported for all PVDF/HC6 hybrid membranes compared to the pure PVDF membrane, while LEP was also improved. In addition, NaCl rejection remained consistently high at 99%. These results confirm the efficacy of HC6 as an agent to improve membrane flux without compromising selectivity or membrane wetting resistance.
Table 13. The performance of the first set of MRO membranes.
| Membrane | LEP / bar | PWP /
Lm-2h-1bar-1 | MD flux / Lm-2h-1 | NaCl rejection / % |
|---|
| M-T0* | 1.3 | 35.4 | 5.2 | 99.9 |
| M-T1 | 1.7 | 39.7 | 5.6 | 99.9 |
| M-T2 | 1.6 | 66.8 | 6.2 | 99.9 |
| M-T3 | 1.8 | 47.4 | 8.3 | 99.9 |
| M-T4 | 1.9 | 113.6 | 6.4 | 99.9 |
For the second MRO, both MD flux and PWP were maximized. The required factor levels and fitted response targets, including estimated errors and 95% intervals, are shown in Table 14.
Table 14. The model prediction for the second set of MRO membranes.
| Response | Target | SE Fit | 95% CI | 95% PI |
|---|
| MD flux / Lm-2h-1 | 3.2 | 0.3 | 2.6 – 4.0 | 0.7 – 5.8 |
| PWP / Lm-2h-1bar-1 | 70.5 | 11.1 | 47.6 – 93.4 | 35.0 – 106.0 |
Table 15 summarizes the experimental conditions of the second MRO membrane set.
Table 15. The dope solution compositions and VIPS the process conditions of the VIPS pretreatment of second set of MRO membranes.
| Membrane code | Polymer mass fraction
wP / wt.-% | Additive mass fraction
wHC6 / wt.-% | VIPS duration
tVIPS / s | Relative humidity
RH / % |
|---|
| M-T5 | 15.6 | 0 | 638 | 90 |
| M-T6 | 15.6 | 0.025 | 638 | 90 |
| M-T7* | 15.6 | 0.051 | 638 | 90 |
| M-T8 | 15.6 | 0.075 | 638 | 90 |
| M-T9 | 15.6 | 0.100 | 638 | 90 |
The membrane performance of the second MRO set is summarized in Table 16. The PWP is within the 95% CI, further confirming the predictive ability of the model. The MD flux performance as a function of HC6 concentration qualitatively agrees exactly with the model prediction, although the model underpredicts the membrane performance to an extent outside the 95% PI. This suggests that the MD flux model is only qualitatively suitable for predicting membrane MD performance. Both PWP and MD flux are improved for the PVDF/HC6 hybrid membranes over the pure PVDF membrane, except for the MD flux of M-T9. LEP and NaCl rejection are lower compared to the first MRO, possibly due to reduced polymer content, which increases porosity.
Table 16. The performance of the second set of MRO membranes.
| Membrane | LEP / bar | PWP /
Lm-2h-1bar-1 | MD flux / Lm-2h-1 | NaCl rejection / % |
|---|
| M-T5 | 1.5 | 62.5 | 5.2 | 99.9 |
| M-T6 | 1.5 | 81.6 | 5.3 | 98.43 |
| M-T7* | 1.3 | 91.8 | 7.9 | 87.89 |
| M-T8 | 1.3 | 67.8 | 6.0 | 98.12 |
| M-T9 | 1.4 | 73.4 | 4.3 | 80.5 |
The multiple response optimization was a twofold success. On the one hand, the combined DoE and MRO approach resulted in membranes with a performance improvement that clearly exceeded the benchmark of the best performing CCCD membranes. Compared to these membranes, the performance improvement was 7.0 times for PWP (M-T4) and 3.3 times for MD flux (M-T3). In addition, a comparison of pure PVDF and PVDF/HC6 hybrid membranes within each MRO set shows the improved performance of the hybrid membranes. The first MRO set maximized PWP and targeted an LEP of 2.0 bar. The pure PVDF membrane M-T0 showed a PWP of 35.4 Lm-2h-1bar-1 and an LEP of 1.3 bar. The best performing hybrid membrane, M T4, measured 113.6 Lm-2h-1bar-1 with an LEP of 1.9 bar. In addition, MD flux improved from 5.2 Lm-2h-1 to 8.3 Lm-2h-1 for M-T3 over M-T0. This represents a 3.2-fold increase in PWP and a 1.6-fold increase in MD flux. MD selectivity remained high for both hybrid membranes with a NaCl rejection of 99.99%. The second MRO was optimized to maximize MD flux and PWP. The pure PVDF membrane M-T5 showed an MD flux of 5.2 Lm-2h-1 and a PWP flux of 62.5 Lm-2h-1bar-1. The best performing hybrid membrane, M-T7, showed an MD flux of 7.9 Lm-2h-1 and a PWP of 91.8 Lm-2h-1bar-1, representing a 1.5-fold increase in MD and PWP performance. On the other hand, three out of four regression models, namely LEP, PWP and MD flux, were validated despite the mediocre Adj R². The incorporation of a VIPS pretreatment resulted in a notable enhancement in LEP, accompanied by an optimal NaCl rejection, at the cost of MD flux. Furthermore, the PWP exhibited enhanced performance in comparison to the pure NIPS approach. The details of the comparison are shown in Table 17.
Table 17. A comparison of the membrane performance factors of the best-performing membranes prepared by NIPS and those prepared by VIPS/NIPS.
| Membrane preparation | NIPS (Nursiah et al., 2023) | VIPS/NIPS (this publication) |
|---|
| Membrane material | PVDF | PVDF/HC6 | PVDF M(HC6)-T5 | PVDF/HC6 M(HC6)-T4 |
|---|
| PWP | 30.6 | 75.3 | 62.5 | 113.6 |
| LEP | 0.7 | 0.2 | 1.5 | 1.9 |
| MD flux | 48.9 | 54.1 | 5.2 | 6.4 |
| NaCl rejection | 98.4 | 99.8 | 99.9 | 99.9 |
Morphological analysis
The impact of HC6 on the morphology of MRO membranes was investigated through the analysis of scanning electron microscopy (SEM) micrographs of the top surfaces and cross-sections. For the first set of MRO membranes, the surface of the pure PVDF membrane (Figure 7(a)) displays a skin layer devoid of defects, while the surfaces of the PVDF/HC6 hybrid membranes exhibit a progressive increase in skin defects with rising HC6 content (Figure 7(b)–(e)). This observation can be attributed to the VIPS pretreatment. The sole application of NIPS, however, has been observed to yield the opposite effect, namely a decrease in surface porosity with increasing HC6 presence (Nursiah et al., 2023). Furthermore, the incorporation of AWCs appears to result in an increased volume of voids and cavities throughout the membrane cross-sections. This is evidenced by the evolution of the finger-like voids observed in the reference membrane (Figure 7(f)) into deeper and larger voids in the hybrid membrane (Figure 7(g)–(j)). This shift in the cross-section morphology due to HC6 presence is comparable to that observed in our previous publication employing only NIPS (Nursiah et al., 2023).
Figure 7. SEM micrographs of top surfaces and cross-sections of the second MRO membranes.
(a)–(e): Top surfaces; (f)–(j): Cross-sections. The HC6 content increases from (a) to (e) in 0.025 incremental steps from 0 to 0.1 wt.-%.
A comparative analysis of the surface morphologies of the two sets of MRO membranes reveals a notable increase in surface porosity for the second set, as illustrated in Figure 8 (a)–(e). Even the pure PVDF reference membrane (Figure 8 (a)) exhibits evidence of skin defects. The top surfaces of the PVDF/HC6 hybrid membranes (Figure 8 (b)–(e)) also demonstrate a correlation between surface porosity and HC6 content, exhibiting an increase in surface porosity with an increase in HC6 content. The mean LEP and NaCl rejection values for the first MRO set are 1.7 ± 0.2 bar and 99.9 ± 0 %, respectively, while those for the second MRO set are 1.4 ± 0.1 bar and 93.0 ± 6.6 %. This can be attributed to the fact that the total porosity of the skins throughout the second MRO set is greater than that of the first set. This is a consequence of the lower wp and the shorter tVIPS employed in the second MRO. The cross-sections of the membranes of the second MRO (Figure 8 (f)–(j)) follow the same trend as those observed in the first MRO. The morphology of the cross-sections of the PVDF/HC6 hybrid membranes substantiates the suggested mechanism of incorporation of HC6 and AWCs into the PVDF matrix, as described in the introduction. The incorporation of HC6 results in an increase in the size of the voids that constitute the sublayer situated beneath the skin. In the case of a VIPS pretreatment, the voids exhibit a further extension towards the lower interface, which is in contact with the support. In contrast, in the case of solay NIPS, a sponge-like third sublayer, situated beneath the finger-like sublayer, forms the lower interface.
Figure 8. SEM micrographs of top surfaces and cross-sections of the second MRO membranes.
(a)–(e): Top surfaces; (f)–(j): Cross-sections. The HC6 content increases from (a) to (e) in 0.025 incremental steps from 0 to 0.1 wt.-%.
The morphological analysis of the two sets of MRO membranes allows for three assertions to be made.
1. The incorporation of a VIPS pretreatment, followed by a NIPS stage, results in the formation of membranes with asymmetric cross-sections, indicating that the VIPS pretreatment alone is insufficient to achieve complete phase separation. The asymmetric cross-sections suggest that the phase separation process is completed during the NIPS stage.
2. The VIPS pretreatment does not prevent skin formation. However, skin porosity correlates with HC6 content. It is proposed that the reduced water mass flux during VIPS facilitates the formation of AWC crystals within the dope prior to phase separation, thereby disrupting the skin formation during the NIPS stage.
3. The hydrophilicity of the HC6 imidazole head enhances the water mass flux during the NIPS stage, leading to the formation of larger macrovoids in the sublayer with increasing HC6 content. The porous skin, resulting from the VIPS pretreatment, further elevates the mass flux during the NIPS stage, thereby inhibiting the development of a third sponge-like sublayer.
Conclusion
The influence of a VIPS pretreatment on the NIPS of a PVDF/HC6/DMAc/water system has been investigated. The VIPS pretreatment was demonstrated to significantly improve PWP, LEP, and NaCl rejection. The hybrid membranes, comprising PVDF and HC6-based AWCs, demonstrated superior performance in terms of PWP, MD flux, and LEP in comparison to pure PVDF membranes. The presence of HC6 in the dope solution, as well as the incorporation of HC6 and AWCs into the PVDF matrix, have been identified as the primary factors responsible for the enhanced performance and alterations in skin porosity and cross-section morphology of the hybrid membranes. The employed DoE methodology and RSM modeling have demonstrated the potential to reduce the experimental effort while establishing quantifiable correlations between experimental variables and membrane performance.
Ethics and consent
Ethical approval and consent were not required.
