Optimization of FIB-SEM Tomography and Reconstruction for Soft, Porous, and Poorly Conducting Materials

A soft touch with electron beams: Digging out structural information of nanomaterials with advanced scanning low energy electron microscopy coupled with deep learning

Nanostructured materials continue to find applications in various electronic and sensing devices, chromatography, separations, drug delivery, renewable energy, and catalysis. While major advancements on the synthesis and characterization of these materials have already been made, getting information about their structures at sub-nanometer resolution remains challenging. It is also unfortunate to find that many emerging or already available powerful analytical methods take time to be fully adopted for characterization of various nanomaterials. The scanning low energy electron microscopy (SLEEM) is a good example to this. In this report, we show how clearer structural and surface information at nanoscale can be obtained by SLEEM, coupled with deep learning. The method is demonstrated using Au nanoparticles-loaded mesoporous silica as a model system. Moreover, unlike conventional scanning electron microscopy (SEM), SLEEM does not require the samples to be coated with conductive films for analysis; thus, not only it is convenient to use but it also does not give artifacts. The results further reveal that SLEEM and deep learning can serve as great tools to analyze materials at nanoscale well. The biggest advantage of the presented method is its availability, as most modern SEMs are able to operate at low energies and deep learning methods are already being widely used in many fields.

Development and application of a 3D image analysis strategy for focused ion beam - Scanning electron microscopy tomography of porous soft materials

In recent years, the potential of porous soft materials in various device technologies has increased in importance due to applications in fields, such as wearable electronics, medicine, and transient devices. However, understanding the 3-dimensional architecture of porous soft materials at the microscale remains a challenge. Herein, we present a method to structurally analyze soft materials using Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) tomography. Two materials, polymethyl methacrylate (PMMA) membrane and pine wood veneer were chosen as test-cases. FIB-SEM was successfully used to reconstruct the true topography of these materials in 3D. Structural and physical properties were subsequently deduced from the rendered 3D models. The methodology used segmentation, coupled with optimized thresholding, image processing, and reconstruction protocols. The 3D models generated pore size distribution, pore inter-connectivity, tortuosity, thickness, and curvature data. It was shown that FIB-SEM tomography provides both an informative and visual depiction of structure. To evaluate and validate the FIB-SEM reconstructions, porous properties were generated from the physical property analysis techniques, gas adsorption analysis using Brunauer-Emmett-Teller (BET) surface area analysis and mercury intrusion porosimetry (MIP) analysis. In general, the data obtained from the FIB-SEM reconstructions was well-matched with the physical data. RESEARCH HIGHLIGHTS: Porous specimens of both synthetic and biological nature, a poly(methyl methacrylate) membrane and a pine veneer respectively, are reconstructed via FIB-SEM tomography without resin-embedding. Different thresholding and reconstruction methods are explored whereby shadowing artifacts are present with the aid of free open-source software. Reconstruction data is compared to physical data: MIP, gas adsorption isotherms which are analyzed via BET and Barrett-Joyner-Halenda (BJH) analysis to yield a full picture of the materials.

Sub-surface Imaging of Porous GaN Distributed Bragg Reflectors via Backscattered Electrons

In this article, porous GaN distributed Bragg reflectors (DBRs) were fabricated by epitaxy of undoped/doped multilayers followed by electrochemical etching. We present backscattered electron scanning electron microscopy (BSE-SEM) for sub-surface plan-view imaging, enabling efficient, non-destructive pore morphology characterization. In mesoporous GaN DBRs, BSE-SEM images the same branching pores and Voronoi-like domains as scanning transmission electron microscopy. In microporous GaN DBRs, micrographs were dominated by first porous layer features (45 nm to 108 nm sub-surface) with diffuse second layer (153 nm to 216 nm sub-surface) contributions. The optimum primary electron landing energy (LE) for image contrast and spatial resolution in a Zeiss GeminiSEM 300 was approximately 20 keV. BSE-SEM detects porosity ca. 295 nm sub-surface in an overgrown porous GaN DBR, yielding low contrast that is still first porous layer dominated. Imaging through a ca. 190 nm GaN cap improves contrast. We derived image contrast, spatial resolution, and information depth expectations from semi-empirical expressions. These theoretical studies echo our experiments as image contrast and spatial resolution can improve with higher LE, plateauing towards 30 keV. BSE-SEM is predicted to be dominated by the uppermost porous layer's uppermost region, congruent with experimental analysis. Most pertinently, information depth increases with LE, as observed.

Quantitative analysis of printed nanostructured networks using high-resolution 3D FIB-SEM nanotomography

Networks of solution-processed nanomaterials are becoming increasingly important across applications in electronics, sensing and energy storage/generation. Although the physical properties of these devices are often completely dominated by network morphology, the network structure itself remains difficult to interrogate. Here, we utilise focused ion beam - scanning electron microscopy nanotomography (FIB-SEM-NT) to quantitatively characterise the morphology of printed nanostructured networks and their devices using nanometre-resolution 3D images. The influence of nanosheet/nanowire size on network structure in printed films of graphene, WS2 and silver nanosheets (AgNSs), as well as networks of silver nanowires (AgNWs), is investigated. We present a comprehensive toolkit to extract morphological characteristics including network porosity, tortuosity, specific surface area, pore dimensions and nanosheet orientation, which we link to network resistivity. By extending this technique to interrogate the structure and interfaces within printed vertical heterostacks, we demonstrate the potential of this technique for device characterisation and optimisation.

Controlling the structure of spin-coated multilayer ethylcellulose/hydroxypropylcellulose films for drug release

Porous phase-separated ethylcellulose/hydroxypropylcellulose (EC/HPC) films are used to control drug transport out of pharmaceutical pellets. Water-soluble HPC leaches out and forms a porous structure that controls the drug transport. Industrially, the pellets are coated using a fluidized bed spraying device, and a layered film exhibiting varying porosity and structure after leaching is obtained. A detailed understanding of the formation of the multilayered, phase-separated structure during production is lacking. Here, we have investigated multilayered EC/HPC films produced by sequential spin-coating, which was used to mimic the industrial process. The effects of EC/HPC ratio and spin speed on the multilayer film formation and structure were investigated using advanced microscopy techniques and image analysis. Cahn-Hilliard simulations were performed to analyze the mixing behavior. A gradient with larger structures close to the substrate surface and smaller structures close to the air surface was formed due to coarsening of the layers already coated during successive deposition cycles. The porosity of the multilayer film was found to vary with both EC/HPC ratio and spin speed. Simulation of the mixing behavior and in situ characterization of the structure evolution showed that the origin of the discontinuities and multilayer structure can be explained by the non-mixing of the layers.

Multiscale X-ray imaging and characterisation of pharmaceutical dosage forms

A correlative, multiscale imaging methodology for visualising and quantifying the morphology of solid dosage forms by combining ptychographic X-ray computed nanotomography (PXCT) and scanning small- and wide-angle X-ray scattering (S/WAXS) is presented. The methodology presents a workflow for multiscale analysis, where structures are characterised from the nanometre to millimetre regime. Here, the method is demonstrated by characterising a hot-melt extruded, partly crystalline, solid dispersion of carbamazepine in ethyl cellulose. Characterisation of the morphology and solid-state phase of the drug in solid dosage forms is central as this affects the performance of the final formulation. The 3D morphology was visualised at a resolution of 80 nm over an extended volume through PXCT, revealing an oriented structure of crystalline drug domains aligned in the direction of extrusion. Scanning S/WAXS, showed that the nanostructure is similar over the cross section of the extruded filament, with minor radial changes in domain sizes and degree of orientation. The polymorphic forms of carbamazepine were qualified with WAXS, showing a heterogeneous distribution of the metastable forms I and II. This demonstrates the methodology for multiscale structural characterization and imaging to enable a better understanding of the relationships between morphology, performance, and processing conditions of solid dosage forms.

Structure formation and coarsening kinetics of phase-separated spin-coated ethylcellulose/hydroxypropylcellulose films

Porous phase-separated ethylcellulose/hydroxypropylcellulose (EC/HPC) films are used to control drug transport from pharmaceutical pellets. The drug transport rate is determined by the structure of the porous films that are formed as water-soluble HPC leaches out. However, a detailed understanding of the evolution of the phase-separated structure in the films is lacking. In this work, we have investigated EC/HPC films produced by spin-coating, mimicking the industrial fluidized bed spraying. The aim was to investigate film structure evolution and coarsening kinetics during solvent evaporation. The structure evolution was characterized using confocal laser scanning microscopy and image analysis. The effect of the EC:HPC ratio (15 to 85 wt% HPC) on the structure evolution was determined. Bicontinuous structures were found for 30 to 40 wt% HPC. The growth of the characteristic length scale followed a power law, L(t) ∼ t⁽ⁿ⁾, with n ∼ 1 for bicontinuous structures, and n ∼ 0.45-0.75 for discontinuous structures. The characteristic length scale after kinetic trapping ranged between 3.0 and 6.0 μm for bicontinuous and between 0.6 and 1.6 μm for discontinuous structures. Two main coarsening mechanisms could be identified: interfacial tension-driven hydrodynamic growth for bicontinuous structures and diffusion-driven coalescence for discontinuous structures. The 2D in-plane interface curvature analysis showed that the mean curvature decreased as a function of time for bicontinuous structures, confirming that interfacial tension is driving the growth. The findings of this work provide a good understanding of the mechanisms responsible for morphology development and open for further tailoring of thin EC/HPC film structures for controlled drug release.

Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release

Phase-separated polymer films are commonly used as coatings around pharmaceutical oral dosage forms (tablets or pellets) to facilitate controlled drug release. A typical choice is to use ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. When an EC/HPC film is in contact with water, the leaching out of the water-soluble HPC phase produces an EC film with a porous network through which the drug is transported. The drug release can be tailored by controlling the structure of this porous network. Imaging and characterization of such EC porous films facilitates understanding of how to control and tailor film formation and ultimately drug release. Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography is a well-established technique for high-resolution imaging, and suitable for this application. However, for segmenting image data, in this case to correctly identify the porous network, FIB-SEM is a challenging technique to work with. In this work, we implement convolutional neural networks for segmentation of FIB-SEM image data. The data are acquired from three EC porous films where the HPC phases have been leached out. The three data sets have varying porosities in a range of interest for controlled drug release applications. We demonstrate very good agreement with manual segmentations. In particular, we demonstrate an improvement in comparison to previous work on the same data sets that utilized a random forest classifier trained on Gaussian scale-space features. Finally, we facilitate further development of FIB-SEM segmentation methods by making the data and software used open access.

Structure evolution during phase separation in spin-coated ethylcellulose/hydroxypropylcellulose films

Porous phase-separated films made of ethylcellulose (EC) and hydroxypropylcellulose (HPC) are commonly used for controlled drug release. The structure of these thin films is controlling the drug transport from the core to the surrounding liquids in the stomach or intestine. However, detailed understanding of the time evolution of these porous structures as they are formed remains elusive. In this work, spin-coating, a widely applied technique for making thin uniform polymer films, was used to mimic the industrial manufacturing process. The focus of this work was on understanding the structure evolution of phase-separated spin-coated EC/HPC films. The structure evolution was determined using confocal laser scanning microscopy (CLSM) and image analysis. In particular, we determined the influence of spin-coating parameters and EC : HPC ratio on the final phase-separated structure and the film thickness. The film thickness was determined by profilometry and it influences the ethanol solvent evaporation rate and thereby the phase separation kinetics. The spin speed was varied between 1000 and 10 000 rpm and the ratio of EC : HPC in the polymer blend was varied between 78 : 22 wt% and 40 : 60 wt%. The obtained CLSM micrographs showed phase separated structures, typical for the spinodal decomposition phase separation mechanism. By using confocal laser scanning microscopy combined with Fourier image analysis, we could extract the characteristic length scale of the phase-separated final structure. Varying spin speed and EC : HPC ratio gave us precise control over the characteristic length scale and the thickness of the film. The results showed that the characteristic length scale increases with decreasing spin speed and with increasing HPC ratio. The thickness of the spin-coated film decreases with increasing spin speed. It was found that the relation between film thickness and spin speed followed the Meyerhofer equation with an exponent close to 0.5. Furthermore, good correlations between thickness and spin speed were found for the compositions 22 wt% HPC, 30 wt% HPC and 45 wt% HPC. These findings give a good basis for understanding the mechanisms responsible for the morphology development and increase the possibilities to tailor thin EC/HPC film structures.

New Characterization Measures of Pore Shape and Connectivity Applied to Coatings used for Controlled Drug Release

Pore geometry characterization-methods are important tools for understanding how pore structure influences properties such as transport through a porous material. Bottlenecks can have a large influence on transport and related properties. However, existing methods only catch certain types of bottleneck effects caused by variations in pore size. We here introduce a new measure, geodesic channel strength, which captures a different type of bottleneck effect caused by many paths coinciding in the same pore. We further develop new variants of pore size measures and propose a new way of visualizing 3-D characterization results using layered images. The new measures together with existing measures were used to characterize and visualize properties of 3-D FIB-SEM images of three leached ethyl-cellulose/hydroxypropyl-cellulose films. All films were shown to be anisotropic, and the strongest anisotropy was found in the film with lowest porosity. This film had very tortuous paths and strong geodesic channel-bottlenecks, while the paths through the other two films were relatively straight with well-connected pore networks. The geodesic channel strength was shown to give important new visual and quantitative insights about connectivity, and the new pore size measures provided useful information about anisotropies and inhomogeneities in the pore structures. The methods have been implemented in the freely available software MIST.

Three-dimensional reconstruction of porous polymer films from FIB-SEM nanotomography data using random forests

Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography is a well-established technique for high resolution imaging and reconstruction of the microstructure of a wide range of materials. Segmentation of FIB-SEM data is complicated due to a number of factors; the most prominent is that for porous materials, the scanning electron microscope image slices contain information not only from the planar cross-section of the material but also from underlying, exposed subsurface pores. In this work, we develop a segmentation method for FIB-SEM data from ethyl cellulose porous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These materials are used for coating pharmaceutical oral dosage forms (tablets or pellets) to control drug release. We study three samples of ethyl cellulose and hydroxypropyl cellulose with different volume fractions where the hydroxypropyl cellulose phase has been leached out, resulting in a porous material. The data are segmented using scale-space features and a random forest classifier. We demonstrate good agreement with manual segmentations. The method enables quantitative characterization and subsequent optimization of material structure for controlled release applications. Although the methodology is demonstrated on porous polymer films, it is applicable to other soft porous materials imaged by FIB-SEM. We make the data and software used publicly available to facilitate further development of FIB-SEM segmentation methods. LAY DESCRIPTION: For imaging of very fine structures in materials, the resolution limits of, e.g. X-ray computed tomography quickly become a bottleneck. Scanning electron microscopy (SEM) provides a way out, but it is essentially a two-dimensional imaging technique. One manner in which to extend it to three dimensions is to use a focused ion beam (FIB) combined with a scanning electron microscopy and acquire tomography data. In FIB-SEM tomography, ions are used to perform serial sectioning and the electron beam is used to image the cross section surface. This is a well-established method for a wide range of materials. However, image analysis of FIB-SEM data is complicated for a variety of reasons, in particular for porous media. In this work, we analyse FIB-SEM data from ethyl cellulose porous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These films are used as coatings for controlled drug release. The aim is to perform image segmentation, i.e. to identify which parts of the image data constitute the pores and the solid, respectively. Manual segmentation, i.e. when a trained operator manually identifies areas constituting pores and solid, is too time-consuming to do in full for our very large data sets. However, by performing manual segmentation on a set of small, random regions of the data, we can train a machine learning algorithm to perform automatic segmentation on the entire data sets. The method yields good agreement with the manual segmentations and yields porosities of the entire data sets in very good agreement with expected values. The method facilitates understanding and quantitative characterization of the geometrical structure of the materials, and ultimately understanding of how to tailor the drug release.

3D high spatial resolution visualisation and quantification of interconnectivity in polymer films

A porous network acts as transport paths for drugs through films for controlled drug release. The interconnectivity of the network strongly influences the transport properties. It is therefore important to quantify the interconnectivity and correlate it to transport properties for control and design of new films. This work presents a novel method for 3D visualisation and analysis of interconnectivity. High spatial resolution 3D data on porous polymer films for controlled drug release has been acquired using a focused ion beam (FIB) combined with a scanning electron microscope (SEM). The data analysis method enables visualisation of pore paths starting at a chosen inlet pore, dividing them into groups by length, enabling a more detailed quantification and visualisation. The method also enables identification of central features of the porous network by quantification of channels where pore paths coincide. The method was applied to FIB-SEM data of three leached ethyl cellulose (EC)/hydroxypropyl cellulose (HPC) films with different weight percentages. The results from the analysis were consistent with the experimentally measured release properties of the films. The interconnectivity and porosity increase with increasing amount of HPC. The bottleneck effect was strong in the leached film with lowest porosity.