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X-ray crystallography Abstract Neurite self-recognition and avoidance are fundamental properties of all nervous systems 1. These processes facilitate dendritic arborization 2 , 3 , prevent formation of autapses 4 and allow free interaction among non-self neurons 1 , 2 , 4 , 5. Avoidance is observed between neurons that express identical protocadherin repertoires 2 , 5 , and single-isoform differences are sufficient to prevent self-recognition Protocadherins form isoform-promiscuous cis dimers and isoform-specific homophilic trans dimers 10 , 14 , 15 , 16 , 17 , 18 , 19 , Although these interactions have previously been characterized in isolation 15 , 17 , 18 , 19 , 20 , structures of full-length protocadherin ectodomains have not been determined, and how these two interfaces engage in self-recognition between neuronal surfaces remains unknown. Here we determine the molecular arrangement of full-length clustered protocadherin ectodomains in single-isoform self-recognition complexes, using X-ray crystallography and cryo-electron tomography.

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Email Abstract X-ray microcomputed tomography microCT has become an established method of testing and analyzing additively manufactured parts in recent years, being especially useful and accurate for dimensional measurement and porosity analysis.

While this nondestructive analysis method is gaining traction among additive manufacturing AM researchers and engineers, the capabilities of the method are not yet fully appreciated and are still being developed. This review aims to summarize the many diverse ways this technique has been applied to AM, including new and specialized applications. Examples are shown of many of these newly developed methods, while also discussing the practicality and limitations of each.

Introduction Additive manufacturing AM is a layer-by-layer manufacturing method that has grown considerably in recent years, especially for producing functional metal parts for critical applications in medical and aerospace industries. These issues include unwanted porosity from incorrect processing parameters or build conditions, surface roughness or other surface imperfections, deformation caused by residual stresses, and mechanical properties that are anisotropic, for example.

These imperfections can be exaggerated due to the complex nature of the designs possible by PBF. Due to these challenges, process qualification is required and manufactured parts require careful testing, especially for high value and critical parts such as those for aerospace or medical applications. X-ray microcomputed tomography microCT is becoming an established technique for nondestructive analysis in various fields of application.

In materials sciences, its increasingly widespread use was reviewed in Maire and Withers, 4 which makes it clear that the method has evolved from a qualitative imaging technique in the past to a mature and quantitative analytical technique in recent years. It finds particular use as a high-quality and nondestructive analysis tool in various industrial applications as reviewed in De Chiffre et al.

Since PBF allows the manufacturing of objects with complex shapes and internal design, including lattice structures and foams, microCT is very useful to quantify the porosity, to study the cell morphology, and to evaluate internal and external surface roughness, overall structural integrity, and the extent and distribution of internal defects.

It was concluded that the main drawbacks to the wider uptake of the technique are costs and lack of standards. The scope of the present review article is to demonstrate and discuss all the varied ways microCT has been used in AM, in addition to the abovementioned porosity and dimensional measurements. The aim of this review is therefore to broaden the general understanding of how this technique can be used to complement and support AM, which is not generally known and which is still under continuous development.

This includes various interesting applications and new developments applicable to AM at different levels, from powder characterization to surface roughness assessment and to image-based simulations. This summary of the capabilities will hopefully provide insight into how best to make use of this powerful technique, allowing a proper selection of microCT testing strategy for a particular application. Based on the various applications demonstrated and discussed in the review, suggestions are made of microCT testing strategies for most cost-effective use of the technique.

Considering the fast progress in both fields of X-ray microCT and in AM, regular reviews of the synergies between these two technologies will continue to remain important in the next few years as advances are made in both.

While the discussions are broadly applicable to all AM, the focus of the work is on the most critical applications for aerospace and medical applications, that is, metal PBF, in particular LPBF.

Background Basic principles of X-ray microCT X-ray microCT works on the principle of irradiating a sample with a beam of X-rays, measuring the subsequent absorption X-ray image, and repeatedly acquiring such images as the sample rotates. The X-ray absorption so-called projection images represent views of the sample from many angles, providing internal detail due to the penetration of X-rays. This volume comprises voxels volumetric pixels with the brightness of each pixel related to the X-ray density of the material it represents X-ray density depends on physical density and atomic mass.

A schematic of the process is shown in Figure 1 , which is a modified version taken from a tutorial review of X-ray CT in food sciences, 10 which also describes the fundamentals of the process in more detail. The schematic shown in Figure 1 is a representation of most typical laboratory microCT setups, with a microfocus X-ray source, a rotating sample stage, a planar detector, and integrated software, to acquire images and reconstruct the volume data. After scanning and reconstruction, data visualization and analysis are further performed, typically in dedicated software.

In this schematic, a polyamide polymer hexagonal sample with internal lattice structure is shown, which was built by selective laser sintering. The CT slice image shows the presence of remaining powder, indicating a simple yet powerful visualization by CT, without any postprocessing. Schematic of an X-ray microCT scan.

Color images available online at www. For metal AM parts, high beam voltages and beam filtering are also important factors to consider and some guidelines are also presented in Ref. Some variations of systems exist with regard to the types of detectors bit depth, pixel size, sensitivity , X-ray sources maximum voltage, brightness, stability, smallest spot size , and translation and rotation hardware stability, accuracy.

These variations in practice extend from small desktop microCT systems with limited sample size capabilities to large room-sized cabinets. One of the latest developments is helical scanning as demonstrated in Seifi et al. Another new development, which can be very useful in practice, is off-axis CT: rotation around an arbitrary selected point in the sample.

This allows higher resolution scans to be obtained with less sample mounting limitations. Besides the typical systems using geometric magnification, some systems utilize a different concept where a collimated beam is used, allowing for higher resolution imaging of small regions inside an object. These are excellent for high-resolution research studies, but not for quality inspections due to long scan times.

Similarly, synchrotron tomography is extremely useful for research studies, due to the high-resolution, high X-ray flux and fast scan times possible, see for example, Refs. Reconstruction of acquired 2D images into 3D volumetric data is typically performed using system-supplied software, based on variations of a filtered back-projection algorithm.

Due to the cost of typical hardware and the technical experience and skill required to obtain good scans, generate good reconstructions, and allow access to suitable computing power and software, multiuser facilities such as in du Plessis et al.

This is one way to lower the cost barrier to the use of the technology. Applications Porosity and defect analysis Despite the advantages of AM, various forms of defects can occur, which can be detrimental to the mechanical properties of the produced parts. Pores can be caused by many different physical processes and can often be due to different melt pool dynamics that depend on process parameters and conditions as discussed in Khairallah et al.

Since LPBF is a process involving selective laser fusion of a predeposited powder layer, it can be expected that the quality of the powder layer is crucial. Therefore, the powder particle shapes, the presence of satellites, the particle size distribution, oxidation level, humidity, static charge, and so on, which all can influence the flowability of the powder, packing density, and homogeneity of the deposited powder layer, ultimately impact on the process and resulting porosity.

Laser beam energy was insufficient to melt material in a high layer thickness of several consecutive layers, and inhomogeneity in various regions leads to different melting and solidifying behavior and ultimately to irregular porosity.

It was shown that thermal fluid dynamics has a great impact on the temperature fields and melt interface on the evolution of porosity in LPBF material. The procedure for measurement and characterization of porosity in LPBF small parts using X-ray CT scans may depend on image analysis procedures and one methodology is described in Cai et al. Gas entrapped in the melt pool originating from the feedstock powder was identified in recent studies, such as in Cunningham et al.

For another study in LPBF, pores in the feed stock powder were observed and mentioned that they can cause porosity in the final part. During LPBF, the laser scans a thin predeposited powder layer. As shown in Refs. It was found that when the deposited powder was agglomerated, the inhomogeneous powder layer led to high porosity in sintered samples. Processing homogenous powder layers at the same process parameters resulted in fully dense samples as shown in Kouprianoff et al.

An increased porosity in bottom areas of the samples built with support structures was found in an article by Damon et al. This showed that much of the planar porosity remains after the HIP process. This is therefore one very important type of porosity to identify nondestructively and to eliminate from the process if possible. At optimal conditions, LPBF samples have fairly randomly distributed defects such as small spherical gas pores and defects in the form of microshrinkages between the connected layers, and the mechanical behavior of the samples is not determined by the presence of these small defects, but mostly by specific microstructure after LPBF.

Most work thus far by manufacturers of AM systems has optimized processes based only on microstructure. This study was for electron beam melting and typical porosity was in the region of 0. A similar type of study was reported in Maskery et al.

In this study, various heat treatments were used on the same samples, which affected the microstructure but left pores unaffected. Interestingly, the pores disappear below the resolution limit of the microCT scan after HIP, but regrow to detectable sizes after heat treatment. While the amount of regrowth is limited, it does indicate the presence of porosity even after HIPping, despite being small. Clearly, microCT can be used to identify porosity in small AM samples, but how does this relate to real parts?

The larger part size of typical functional components limits the best possible resolution in microCT, and hence, small pores can be missed. The major question is: which small pores are missed in any particular case? In an ideal scenario, where a perfect quality scan is analyzed, the typical minimum pore size that is positively identified is at least 3 voxels wide, that is, in 3D it comprises at least 27 voxels.

This rule is valid for whole parts and sometimes varies depending on the angle of scanning required to eliminate artifacts. Despite these drawbacks, the method can be used effectively for quality control purposes as demonstrated in Refs.

This methodology can be used as part of a strategy to check the 3D distribution of porosity and this information can be used to minimize porosity by varying process parameters. In this case, most of the detected microporosity was located on the underside of the part and is related to the surface connected to support structures similar to those mentioned in Ref.

Various recent research efforts were aimed at qualification of AM processes, often for specific process parameters and material types. One such qualification was recently presented for AM of biomedical implants. X-ray inspection 2D is a widely used and low-cost alternative nondestructive test method for the detection of flaws in castings, welds, and various metal processed parts. What is not widely known or understood is that typical microCT systems also have the ability to do 2D X-ray inspections, with the same or better quality than dedicated 2D inspection systems.

Obviously, a full microCT scan provides a much clearer view of defects, with more quantitative data of the defects, and with a higher sensitivity and contrast.

However, 2D X-ray imaging is almost real time, allowing for time and cost savings. There are obvious challenges to 2D imaging with complex parts as those produced in AM, due to different path lengths of material for the X-ray projection image.

The possibility for combining 2D inspection of large numbers of parts with 3D microCT scans of selected parts allows for cost-effective inspections, especially for large production volumes. Due to the need to ensure the detection capability of microCT for detection of small pores and cavities, various studies have created artifacts containing artificial cavities seeded flaws. Such AM artifacts were produced containing cavities of varying sizes, and it was shown how microCT can accurately quantify the cavity sizes.

It is demonstrated that the initial X-ray image, when contrasted properly, positively identifies all the cavities, while the microCT data allow much clearer viewing of the cavities in slice images. A further porosity analysis allows quantification of pore volumes, cavity wall thickness, and more, including 3D visualization of the extent and 3D distribution of the cavities.

The contrasted X-ray image A reveals the presence of all the cavities, while CT allows much clearer 3D visualization and quantitative assessment as seen in a CT slice image B and 3D rendering C. LPBF, laser powder bed fusion. The subsequent microCT scan requires longer time investment in scanning and data processing to obtain results such as those presented in the figure.

This sample was machined after AM to a cylinder of 3 mm diameter and height—the 2D X-ray image is shown, followed by different microCT visualizations: a cropped 3D rendering, a slice image, and finally a 3D porosity analysis. This sample contains a total of 4. The microCT data show in a cropped 3D view, lots of pores not visible on the surface, presumably due to the machining process the surface pores were closed up. The microCT slice image shows in black the pore distribution in a horizontal plane, or virtual cross section.

Example of porosity analysis in a small cylindrical sample 3 mm diameter produced by LPBF with nonoptimal processing parameters, leading to total porosity of 4.

The porosity can be seen in an X-ray image A and visualized and quantified in different ways from CT data, shown in a 3D cropped view B , CT slice image showing unmelted powder inside the pores C , or a transparent view of the porosity analysis in 3D D.

Finally, a porosity analysis highlights the pores in 3D by color coding based on size. This analysis seems visually excessive, but this corresponds to 4. This excessive visual effect is due to the full 3D nature of the data and is not a misrepresentation. Therefore, it is important to realize that even small amounts of porosity are clearly visualized, and the statistical data are as important as the visual representation.

In this case the porosity is likely caused by lack of fusion porosity due to nonoptimal energy input for the chosen layer thickness.

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