Characterizing the hydraulic properties of paper coating layer using FIB-SEM tomography and 3D pore-scale modeling

AbstractPaper used in the printing industry generally contains a relatively thin porous coating covering a thicker fibrous base layer. The three-dimensional pore structure of coatings has a major effect on fluid flow patterns inside the paper medium. Understanding and quantifying the flow properties of thin coating layers is hence crucial. Pore spaces within the coating have an average size of about 180nm. We used scanning electron microscopy combined with focused ion beam (FIB-SEM) to visualize the nano-scale pore structure of the paper coating layer. Post-processing of the FIB-SEM images allowed us to reconstruct the three-dimensional pore space of the coating. The 3D FIB-SEM images were analyzed in detail to obtain pore size distribution and porosity value. The permeability was estimated using the GeoDict software, based on solutions of the Stokes equation. By determining the porosity and permeability of increasingly larger domain sizes, we estimated the size of a representative elementary volume (REV) for the coating layer to be 60µm3, which is well within the volume analyzed using FIB-SEM. The estimated porosity and permeability of the REV domain were 0.34 and 0.09 mDarcy, respectively. Using the pore morphology method, capillary pressure-saturation (Pc-S) and relative permeability curves of the REV domain could be constructed next. The Pc-S curves showed that the coating had a high air entry suction, which is very favorable for printing in that ink will invade the coating as soon as it is applied to the coating. Our results are essential for macroscale modelling of ink penetration into a coating layer during inkjet printing. Macroscopic models can be valuable tools for optimization of the penetration depth and the spreading of ink on and within paper substrates

Imbibition into a thin porous medium : an experimental and pore-scale modeling study of coated paper

The goal of this thesis was to understand water-based liquid transport in thin multi-layer porous materials. Existing imaging techniques were adopted in order to make it possible to image the layers and extract their three-dimensional (3D) geometrical information. The extracted geometry was then used in pore-scale modeling tools to determine main hydraulic properties of the layer. In the next step, fluid flow in porous layer modeling was carried out using open source modeling tool, OpenFOAM. Through the work packages, appropriate suggestions, regarding the digital design of layers and used liquid as liquid phase of interest, were proposed. In addition to that, the developed modeling and experimental tools were adopted in a way that they can be applied and used in a wide range of thin porous layers applications including papers, fuel cells, membranes, and catalyst layers. Inkjet printing is attracting much attention due to its potential in the printing of graphics, 3D objects, medical applications, paper-based diagnostic devices and electronics. The inkjet printing involves ejection of a fixed amount of liquid phase from a nozzle onto a substrate; paper in case of graphics. The ejected droplet falls due to gravity. The impinged droplet spreads and penetrates into paper due to surface tension aided flow. Capillarity is the dominant force drawing ink into the pore structure of the paper. Micro capillary penetration starts typically within 0.1 ms after the droplet arrives. In order to identify and understand the main characteristics of the paper substrates used in inkjet printing, manufacturing process of a certain coated paper sample (Magno Gloss) was described. The chosen coated paper was then used later in modeling and experimental parts of the thesis

Imbibition into a thin porous medium : an experimental and pore-scale modeling study of coated paper

The goal of this thesis was to understand water-based liquid transport in thin multi-layer porous materials. Existing imaging techniques were adopted in order to make it possible to image the layers and extract their three-dimensional (3D) geometrical information. The extracted geometry was then used in pore-scale modeling tools to determine main hydraulic properties of the layer. In the next step, fluid flow in porous layer modeling was carried out using open source modeling tool, OpenFOAM. Through the work packages, appropriate suggestions, regarding the digital design of layers and used liquid as liquid phase of interest, were proposed. In addition to that, the developed modeling and experimental tools were adopted in a way that they can be applied and used in a wide range of thin porous layers applications including papers, fuel cells, membranes, and catalyst layers. Inkjet printing is attracting much attention due to its potential in the printing of graphics, 3D objects, medical applications, paper-based diagnostic devices and electronics. The inkjet printing involves ejection of a fixed amount of liquid phase from a nozzle onto a substrate; paper in case of graphics. The ejected droplet falls due to gravity. The impinged droplet spreads and penetrates into paper due to surface tension aided flow. Capillarity is the dominant force drawing ink into the pore structure of the paper. Micro capillary penetration starts typically within 0.1 ms after the droplet arrives. In order to identify and understand the main characteristics of the paper substrates used in inkjet printing, manufacturing process of a certain coated paper sample (Magno Gloss) was described. The chosen coated paper was then used later in modeling and experimental parts of the thesis

Study of Hydraulic Properties of Uncoated Paper : Image Analysis and Pore-Scale Modeling

In this study, uncoated paper was characterized. Three-dimensional structure of the layer was reconstructed using imaging results of micro-CT scanning with a relatively high resolution (0.9 μm)(0.9 μm) . Image analysis provided the pore space of the layer, which was used to determine its porosity and pore size distribution. Representative elementary volume (REV) size was determined by calculating values of porosity and permeability values for varying domain sizes. We found that those values remained unchanged for domain sizes of 400×400×150μm3400×400×150μm3 and larger; this was chosen as the REV size. The determined REV size was verified by determining capillary pressure–saturation Open image in new window imbibition curves for various domain sizes. We studied the directional dependence of Open image in new window curves by simulating water penetration into the layer from various directions. We did not find any significant difference between Open image in new window curves in different directions. We studied the effect of compression of paper on Open image in new window curves. We found that up to 30% compression of the paper layer had very small effect on the Open image in new window curve. Relative permeability as a function of saturation was also calculated. Water penetration into paper was visualized using confocal laser scanning microscopy. Dynamic visualization of water flow in the paper showed that water moves along the fibers first and then fills the pores between them

Characterization of the Interface Between Coating and Fibrous Layers of Paper

Coated paper is an example of a multi-layer porous medium, involving a coating layer along the two surfaces of the paper and a fibrous layer in the interior of the paper. The interface between these two media needs to be characterized in order to develop relevant modeling tools. After careful cutting of the paper, a cross section was imaged using focused ion beam scanning electron microscopy. The resulting image was analyzed to characterize the coating layer and its transition to the fibrous layer. Such image analysis showed that the coating layer thickness is highly variable, with a significant fraction of it being thinner than a minimum thickness required to keep ink from invading into the fibrous layer. The overall structure of the coating and fibrous layers observed in this analysis provide insights into how the system should be modeled, with the resulting conclusion pointing to a specific kind of multi-scale modeling approach

Movement of a liquid droplet within a fibrous layer : Direct pore-scale modeling and experimental observations

In this study, the spreading of a liquid droplet on the surface of a fibrous paper and its penetration into the paper is studied. The spreading of the droplet was visualized using confocal microscopy and the penetration depth was quantified using Automatic Scanning Absorptiometry (ASA) measurements. The three-dimensional structure of the paper was obtained through micro-tomography imaging with a resolution of 0.9 µm. The obtained images were used to reconstruct the pore space, which was in turn used in direct numerical simulations of penetration of a droplet into paper. Simulations were performed using open source code OpenFOAM, which solves equations of two-phase flow (in our case air and water) in pores based on the Volume of Fluid Method. Simulation results showed a good agreement with the experimental observations. In particular, the dimensions of spreading area of a droplet and the depth of penetration were simulated reasonably well. Then, we used the model to investigate effects of changes in various liquid properties on spreading and penetration of a droplet liquid. We made calculations for three different values of contact angle (CA): 0 , 60 , and 120. We found the largest penetration depth for CA = 0. For CA = 60 and CA = 120, we found that the liquid droplet moved sideways from the jetted location, which is not favorable in inkjet printing. We also made simulations with larger values for viscosity and density, based on properties of an ink-based liquid used in inkjet printing. The results have shown a slower spreading and penetration compared with water. The model can be used to study effects of changes in either ink physical properties or paper layer microstructure on final spreading/penetration extent

Occurrence of temperature spikes at a wetting front during spontaneous imbibition

It is reported that temperature rises at wetting front during water infiltration into soil. The temperature goes back to the background value after passage of water front. Different explanations have been provided for source of energy causing temperature spike. Some have contributed it to heat of condensation released due to condensation of vapor on “dry” solid surface. Some other stated that the heat of wetting or heat of adsorption is responsible for the temperature rise. In this research, we revisited this issue. First, we provide a comprehensive review about occurrence of temperature spike at a wetting front. Then, we report about experiments we performed on the rise of water in dry paper. Using infrared and optical imaging techniques, we could monitor temperature changes in time and space. For all samples maximum temperature rise occurred at the wetting front. The magnitude of temperature spike depended on paper material, thickness, and liquid composition. It was larger for cellulose-fiber-based paper than for plastic-based paper. For a given paper type, thicker samples showed a larger temperature spike. Adding salt to the water caused reduction of temperature spike. It was concluded that replacement of air-solid interface with water-solid interface releases energy, which causes temperature rise

Characterization of the Interface Between Coating and Fibrous Layers of Paper

Coated paper is an example of a multi-layer porous medium, involving a coating layer along the two surfaces of the paper and a fibrous layer in the interior of the paper. The interface between these two media needs to be characterized in order to develop relevant modeling tools. After careful cutting of the paper, a cross section was imaged using focused ion beam scanning electron microscopy. The resulting image was analyzed to characterize the coating layer and its transition to the fibrous layer. Such image analysis showed that the coating layer thickness is highly variable, with a significant fraction of it being thinner than a minimum thickness required to keep ink from invading into the fibrous layer. The overall structure of the coating and fibrous layers observed in this analysis provide insights into how the system should be modeled, with the resulting conclusion pointing to a specific kind of multi-scale modeling approach

Application of machine learning in colloids transport in porous media studies: Lattice Boltzmann simulation results as training data

Colloid transport through a porous medium changes geometrical and hydraulic properties of the pore space. The impact of this effect depends on the colloid types and pore space surface properties which determine the likelihood of pore clogging. Colloid attachment and subsequent detachment are key factors in pore clogging. In this study, the impact of four major fluid and colloids properties on the pore surface and hydraulic conductivity alteration during colloids transport were evaluated using machine learning. These four parameters include solution ionic strength, zeta potential, colloid size and fluid flow velocity. A combined lattice Boltzmann-smoothed profile method was used to simulate accurately coupled mechanisms governing colloid transport to evaluate the impact of the four parameters on the resulting pore space properties after colloid transport. The result of several simulations revealed significant changes of pore surface coverage by the attached colloids, and conductivity, void fraction and coordination number of colloid agglomerates created during transport of individual colloids. Since the simulation of the impact of combination of all possible sets of four parameters is very time consuming, an Artificial Neural Network (ANN) was used as a prognostic method to use the results of several simulations to predict the behavior for a wide range of pore, colloidal and fluid properties. Reported results from a set of 162 simulation case studies for different possible combination of solution ionic strength, zeta potentials, colloid size and flow velocity were selected as input parameters for the machine learning. Four output parameters, namely, pore surface coverage, conductivity, void fraction and coordination number of the colloidal particles were selected. To lower the prediction error value, which is targeted to be lower than 10%, networks were trained 50 times using a MATLAB code, and in each training, after at most 10 epochs, networks were trained. A maximum relative error value of 8.95% was obtained, which is very well within the range of training quality criteria. The results show that the ANN can profoundly predict the simulation outcomes for a wide range of ionic strength (IS) and can be directly used to obtain the value of dependent variables through simple calculations using network weights and transfer functions

Application of machine learning in colloids transport in porous media studies: Lattice Boltzmann simulation results as training data

Colloid transport through a porous medium changes geometrical and hydraulic properties of the pore space. The impact of this effect depends on the colloid types and pore space surface properties which determine the likelihood of pore clogging. Colloid attachment and subsequent detachment are key factors in pore clogging. In this study, the impact of four major fluid and colloids properties on the pore surface and hydraulic conductivity alteration during colloids transport were evaluated using machine learning. These four parameters include solution ionic strength, zeta potential, colloid size and fluid flow velocity. A combined lattice Boltzmann-smoothed profile method was used to simulate accurately coupled mechanisms governing colloid transport to evaluate the impact of the four parameters on the resulting pore space properties after colloid transport. The result of several simulations revealed significant changes of pore surface coverage by the attached colloids, and conductivity, void fraction and coordination number of colloid agglomerates created during transport of individual colloids. Since the simulation of the impact of combination of all possible sets of four parameters is very time consuming, an Artificial Neural Network (ANN) was used as a prognostic method to use the results of several simulations to predict the behavior for a wide range of pore, colloidal and fluid properties. Reported results from a set of 162 simulation case studies for different possible combination of solution ionic strength, zeta potentials, colloid size and flow velocity were selected as input parameters for the machine learning. Four output parameters, namely, pore surface coverage, conductivity, void fraction and coordination number of the colloidal particles were selected. To lower the prediction error value, which is targeted to be lower than 10%, networks were trained 50 times using a MATLAB code, and in each training, after at most 10 epochs, networks were trained. A maximum relative error value of 8.95% was obtained, which is very well within the range of training quality criteria. The results show that the ANN can profoundly predict the simulation outcomes for a wide range of ionic strength (IS) and can be directly used to obtain the value of dependent variables through simple calculations using network weights and transfer functions