3D profile micrographs from the center of the wear zone of the femoral head (a) and cup (c) in control group after 3MC wear test.

<p>More protein deposition, shallow grooves, less wear. 3D profile micrographs from the wear zone of the femoral head (b) and cup (d) in experimental group after 3MC wear test. Less protein deposition, deeper grooves, more wear.</p

Demographics of the cumulative volumetric wear of cups and heads.

<p>Demographics of the cumulative volumetric wear of cups and heads.</p

Figure 4

<p>(a)The image of Durom cup before implantation (b)The image of Durom cup after implantation. (c) the image of the cup in the pelvis and location of deformation.</p

Figure 8

<p>(a)Size distribution curve of wear particles (b)Image of aggregated CoCr wear particles (SEM, ×20,000) (c) The elemental composition in the wear particles by EDS analysis.</p

The loads used in hip simulator study.

<p>The loads used in hip simulator study.</p

The ion levels(Levels of Co, Cr) in lubricant from 0.33, 0.66, 1.0, 1.5, 2.0. 2.5 and 3.0 MC test-points.

<p>Mean values displayed in parts per billion (ppb).</p

Six-Station Prosim 2 Hip Simulator (Simsol, Stockport, UK).

<p>Six-Station Prosim 2 Hip Simulator (Simsol, Stockport, UK).</p

Demographics of the deformation of each cup along with the corresponding wear value for each bearing.

<p>Demographics of the deformation of each cup along with the corresponding wear value for each bearing.</p

Figure 1

<p>(a)Durom cup after white sprayed (b)Durom after black dots sprayed (c) Implantation of the Durom cup.</p

Crystal Structure Assignment for Unknown Compounds from X‑ray Diffraction Patterns with Deep Learning

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation