SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous γ-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer.

The pore network architecture of porous heterogeneous catalyst supports has a significant effect on the kinetics of mass transfer occurring within them. Therefore, characterizing and understanding structure-transport relationships is essential to guide new designs of heterogeneous catalysts with higher activity and selectivity and superior resistance to deactivation. This study combines classical characterization via N2 adsorption and desorption and mercury porosimetry with advanced scanning electron microscopy (SEM) imaging and processing approaches to quantify the spatial heterogeneity of γ-alumina (γ-Al2O3), a catalyst support of great industrial relevance. Based on this, a model is proposed for the spatial organization of γ-Al2O3, containing alumina inclusions of different porosities with respect to the alumina matrix. Using original, advanced SEM image analysis techniques, including deep learning semantic segmentation and porosity measurement under gray-level calibration, the inclusion volume fraction and interphase porosity difference were identified and quantified as the key parameters that served as input for effective tortuosity factor predictions using effective medium theory (EMT)-based models. For the studied aluminas, spatial porosity heterogeneity impact on the effective tortuosity factor was found to be negligible, yet it was proven to become significant for an inclusion content of at least 30% and an interphase porosity difference of over 20%. The proposed methodology based on machine-learning-supported image analysis, in conjunction with other analytical techniques, is a general platform that should have a broader impact on porous materials characterization.

Adsorption of Nickel Ions on the γ-Alumina Surface: Competitive Effect of Protons and Its Impact on Concentration Profiles.

Mesoporous γ-alumina is used as an adsorbent in the decontamination of water from heavy metals (e.g., nickel and cobalt) and as a support for heterogeneous catalysts prepared by impregnation. In these cases, alumina extrudates are in contact with aqueous solutions containing precursors of the active metal phase to be deposited. The proton concentration (or pH) in the metal solution in contact with alumina can impact the adsorption efficiency of decontamination processes and the activities of catalysts. Yet, it is difficult to quantify the effect of the pH inside the pores since protons are not detected by classical imaging techniques. In this article, the effect of protons on nickel adsorption on alumina is evaluated using a novel technique coupling liquid analysis (pH, conductivity, and UV/vis) and laser-induced breakdown spectroscopy (or LIBS) analysis of concentration gradients inside the solid. Both methods are in excellent agreement. The results show a slow diffusion of protons inside alumina pores (diffusion continues even after 940 min), yielding high proton concentration gradients. On the other hand, the nickel species penetrate the extrudates faster but are slowly displaced by protons under certain operating conditions. As a result, different metal concentration profiles are obtained, depending on the initial pH and contact time. These findings are interesting in catalysis since they prove the possibility of controlling the deposition of the active metal on catalysts by regulating the operating conditions of impregnation. For typical industrial impregnation times (a few minutes to 1 to 2 h), protons do not have enough time to deeply penetrate inside extrudates, so the initial pH of the metal solution will have nearly no effect on the metal distribution. Conversely, decontamination processes have much longer contact times; therefore, lower initial pH values should have negative impacts on the adsorption efficiency due to the protons displacing the adsorbed nickel.

Small-angle X-ray scattering intensity of multiscale models of spheroids.

The microstructure of heterogeneous catalysts often consists of multiscale aggregates of nanoparticles, some of which are highly anisotropic. Therefore, small-angle X-ray scattering, in classical or anomalous mode, is a valuable tool to characterize this kind of material. Yet, the classical exploitation of the scattered intensities through form and structure factors or by means of Boolean models of spheres is questionable. Here, it is proposed to interpret the scattered intensities through the use of multiscale Boolean models of spheroids. The numerical procedure to compute scattered intensities of such models is given and then validated on asymptotic diluted Boolean models, and its applicability is demonstrated for the characterization of alumina catalyst supports.

Local Porosity Measurement From Scanning Electron Microscopy Images in the Backscattered Electrons Mode.

We propose a method to measure the local porosity of porous samples from scanning electron microscopy images in the backscattered electron mode. The porous samples are impregnated with a polymer resin and observed in polished cross sections. Image intensities are calibrated with intensities from pure resin and the bulk phase. The calibration model is justified with Monte Carlo simulations on perfectly homogeneous virtual samples. Uncertainties in measured porosity are given as a function of uncertainties on physical properties of the resin and the bulk phase and on measured signals. The methodology is applied to a series of heterogeneous alumina catalyst supports with varying porosities. A good agreement is found between the averaged local porosity by scanning electron microscopy and global porosity determined by mercury intrusion porosimetry. The use of local porosity statistics allowed the quantitative characterization of the porosity fluctuations of these supports that appeared to be linked with their preparation parameters.

Obtaining 3D Chemical Maps by Energy Filtered Transmission Electron Microscopy Tomography.

Energy filtered transmission electron microscopy tomography (EFTEM tomography) can provide three-dimensional (3D) chemical maps of materials at a nanometric scale. EFTEM tomography can separate chemical elements that are very difficult to distinguish using other imaging techniques. The experimental protocol described here shows how to create 3D chemical maps to understand the chemical distribution and morphology of a material. Sample preparation steps for data segmentation are presented. This protocol permits the 3D distribution analysis of chemical elements in a nanometric sample. However, it should be noted that currently, the 3D chemical maps can only be generated for samples that are not beam sensitive, since the recording of filtered images requires long exposure times to an intense electron beam. The protocol was applied to quantify the chemical distribution of the components of two different heterogeneous catalyst supports. In the first study, the chemical distribution of aluminum and titanium in titania-alumina supports was analyzed. The samples were prepared using the swing-pH method. In the second, the chemical distribution of aluminum and silicon in silica-alumina supports that were prepared using the sol-powder and mechanical mixture methods was examined.

Use of the Distance Transform for Integration of Local Measurements: Principle and Application in Chemical Engineering.

We propose an original methodology to integrate local measurement for nontrivial object shape. The method employs the distance transform of the object and least-square fitting of numerically computed weighting functions extracted from it. The method is exemplified in the field of chemical engineering by calculating the global metal concentration in catalyst grains from uneven metal distribution profiles. Applying the methodology on synthetic profiles with the help of a very simple deposition model allows us to evaluate the accuracy of the method. For high symmetry objects such as an infinite cylinder, relative errors on global concentration are lower than 1% for well-resolved profiles. For a less symmetrical object, a tetralobe, the best estimator gives a relative error below 5% at the cost of increased measurement time. Applicability on a real case is demonstrated on an aged hydrodemetallation catalyst. Sampling of catalyst grains at the inlet and outlet of the reactor allowed conclusions concerning different reactivity for the trapped metals.

Measurement of palladium crust thickness on catalysts by optical microscopy and image analysis.

Selective hydrogenation is an important process in petrochemistry to purify feedstock for polymer synthesis. For this process, catalysts containing metallic palladium deposited with an eggshell distribution on porous alumina are usually employed. For this kind of catalyst, the activity is known to be in close relation with the thickness of the palladium crust. As palladium oxide is brown and alumina is white, the palladium distribution in a catalyst bead before the reduction step can be characterized by optical microscopy. We propose an original and automatic procedure of optical image analysis to obtain a fast and robust method to measure the mean crust thickness of a catalyst batch and the corresponding standard deviation. The approach is validated by two different methods. First, we compared the crust thickness with those obtained by electron probe microanalysis. Then, catalytic tests of four samples with varying palladium crust thicknesses were performed and confirmed the expected correlation between activity and crust thickness measured by optical microscopy coupled with image analysis.

Three-dimensional chemistry of multiphase nanomaterials by energy-filtered transmission electron microscopy tomography.

A three-dimensional (3D) study of multiphase nanostructures by chemically selective electron tomography combining tomographic approach and energy-filtered imaging is reported. The implementation of this technique at the nanometer scale requires careful procedures for data acquisition, computing, and analysis. Based on the performances of modern transmission electron microscopy equipment and on developments in data processing, electron tomography in the energy-filtered imaging mode is shown to be a very appropriate analysis tool to provide 3D chemical maps at the nanoscale. Two examples highlight the usefulness of analytical electron tomography to investigate inhomogeneous 3D nanostructures, such as multiphase specimens or core-shell nanoparticles. The capability of discerning in a silica-alumina porous particle the two different components is illustrated. A quantitative analysis in the whole specimen and toward the pore surface is reported. This tool is shown to open new perspectives in catalysis by providing a way to characterize precisely 3D nanostructures from a chemical point of view.

3D-TEM investigation of the nanostructure of a δ-Al2O3 catalyst support decorated with Pd nanoparticles.

The electron tomography technique applied in a quantitative way allowed us to characterize a heterogeneous catalyst made of Pd nanoparticles deposited on a δ-Al(2)O(3) lamellar support. In the first step, high resolution tomographic experiments carried out on several typical areas of support have confirmed the hypothesis of formation of δ-Al(2)O(3) proposed in the literature by the coalescence of lateral facets of the γ-Al(2)O(3) precursor. A bimodal porosity was also observed in the arrangement of δ-Al(2)O(3) platelets. In the second step, the Pd nanoparticles were found preferentially anchored on the lateral facets of δ-Al(2)O(3) platelets or on the defects situated on their basal planes. From a general point of view, we have demonstrated once again that the electron tomography technique implemented with nanometre resolution provides unique insight into the structure, morphology and spatial arrangement of components in a complex 3D nanostructure.

Microanalysis of porous materials.

A signal loss is generally reported in electron probe microanalysis (EPMA) of porous, highly divided materials like heterogeneous catalysts. The hypothesis generally proposed to explain this signal loss refers to porosity, roughness, energy losses at interfaces, or charging effects. In this work we investigate by Monte Carlo simulation all these physical effects and compare the simulated results with measurements obtained on a mesoporous alumina. A program using the PENELOPE package and taking into account these four physical phenomena has been written. Simulation results show clearly that neither porosity nor roughness, nor specific energy losses at interfaces, nor charging effects are responsible for the observed signal loss. Measurements performed with analysis of carbon and oxygen lead to a correct total of concentration. The signal loss is thus explained by a composition effect due to a carbon contamination brought by the sample preparation and to a lesser extent by a stoichiometry of the porous alumina different from a massive alumina. For this kind of high specific surface porous sample, a little surface contamination layer becomes an important volume contamination that can produce large quantification errors if the contaminant is not analyzed.