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Nature utilizes the available resources to construct lightweight, strong and tough materials under constrained environmental conditions. The impact surface of the fast-striking dactyl club from the mantis shrimp is an example of one such composite material; the shrimp has evolved the capability to localize damage and avoid catastrophic failure from high-speed collisions during its feeding activities. Here we report that the dactyl club of mantis shrimps contains an impact-resistant coating composed of densely packed (about 88 per cent by volume) ~65-nm bicontinuous nanoparticles of hydroxyapatite integrated within an organic matrix. These mesocrystalline hydroxyapatite nanoparticles are assembled from small, highly aligned nanocrystals. Under impacts of high strain rates (around 104 s-1), particles rotate and translate, whereas the nanocrystalline networks fracture at low-angle grain boundaries, form dislocations and undergo amorphization. The interpenetrating organic network provides additional toughening, as well as substantial damping, with a loss coefficient of around 0.02. An unusual combination of stiffness and damping is therefore achieved, outperforming many engineered materials.

High throughput nanoindentation techniques can provide rapid materials screening and property mapping and can span millimeter length scales and up to 106 data points. To facilitate rapid sorting of these data into similar groups, a necessary task for establishing structure–property relationships, use of an unsupervised machine learning analysis called clustering has grown in popularity. Here, a method is proposed and tested that evaluates the uncertainty associated with various clustering algorithms for an example high entropy alloy data set and explores the effect of the number of data points in a second Damascus steel data set. The proposed method utilizes the bootstrapping method of Efron to resample a modeled probability distribution function based upon the original data, which allows the uncertainty related to the clustering to be evaluated in contrast to the classical standard error on the mean calculations. For the Damascus, it was found that results data from a 104 point subsample are comparable to those from the full 106 set while representing a significant reduction in data acquisition. [ABSTRACT FROM AUTHOR]

Development of structural materials for service under extreme conditions is slowed by the lack of high-throughput test protocols. Here, a method that integrates high-throughput nanoindentation mapping with precise temperature control under a vacuum atmosphere is demonstrated. High-entropy alloys (HEAs) may possess the strength and stability required of high-temperature structural materials in next-generation nuclear applications. These alloys, including the compositional variation AlxFeCrNiMn (x = 0, 0.3, 1) presented in this work, have distinct microstructural morphologies, and nanoindentation mapping reveals the mechanical behavior of the distinct phases as a function of temperature up to 400°C. FeCrNiMn (Al = 0) consists of a face-centered cubic (FCC) matrix with body-centered cubic (BCC) precipitates and exhibits significant softening in both phases at elevated temperature. In contrast, both the FCC phase and FCC–BCC phases present in Al0.3FeCrNiMn show approximately 90% retention of the room temperature hardness at 400°C, and AlFeCrNiMn with BCC and B2 structures shows a similar 85% retention of hardness. [ABSTRACT FROM AUTHOR]