Elastic ceramics: Aerogels hold promise as lightweight replacements for thermal insulation. However, poor mechanical stability has hampered progress in moving toward commercialization. Xu et al. designed a mechanical metamaterial that pinches in a small amount when you compress it (see the Perspective by Chhowalla and Jariwala). This is characteristic of materials with a negative Poisson's ratio and dramatically improves mechanical stability. The trick was using three-dimensional graphene structures to template the ceramic aerogels, thus producing a superinsulating material endowed with excellent mechanical properties. Science , this issue p. 723 ; see also p. 694

The widespread application of sophisticated structural health monitoring systems in civil infrastructures produces a large volume of data. As a result, the analysis and mining of structural health monitoring data have become hot research topics in the field of civil engineering. However, the harsh environment of civil structures causes the data measured by structural health monitoring systems to be contaminated by multiple anomalies, which seriously affect the data analysis results. This is one of the main barriers to automatic real-time warning, because it is difficult to distinguish the anomalies caused by structural damage from those related to incorrect data. Existing methods for data cleansing mainly focus on noise filtering, whereas the detection of incorrect data requires expertise and is very time-consuming. Inspired by the real-world manual inspection process, this article proposes a computer vision and deep learning–based data anomaly detection method. In particular, the framework of the proposed method includes two steps: data conversion by data visualization, and the construction and training of deep neural networks for anomaly classification. This process imitates human biological vision and logical thinking. In the data visualization step, the time series signals are transformed into image vectors that are plotted piecewise in grayscale images. In the second step, a training dataset consisting of randomly selected and manually labeled image vectors is input into a deep neural network or a cluster of deep neural networks, which are trained via techniques termed stacked autoencoders and greedy layer-wise training. The trained deep neural networks can be used to detect potential anomalies in large amounts of unchecked structural health monitoring data. To illustrate the training procedure and validate the performance of the proposed method, acceleration data from the structural health monitoring system of a real long-span bridge in China are employed. The results show that the multi-pattern anomalies of the data can be automatically detected with high accuracy.

Thermal insulation under extreme conditions requires materials that can withstand complex thermomechanical stress and retain excellent thermal insulation properties at temperatures exceeding 1,000 degrees Celsius^1–3. Ceramic aerogels are attractive thermal insulating materials; however, at very high temperatures, they often show considerably increased thermal conductivity and limited thermomechanical stability that can lead to catastrophic failure^4–6. Here we report a multiscale design of hypocrystalline zircon nanofibrous aerogels with a zig-zag architecture that leads to exceptional thermomechanical stability and ultralow thermal conductivity at high temperatures. The aerogels show a near-zero Poisson’s ratio (3.3 × 10⁻⁴) and a near-zero thermal expansion coefficient (1.2 × 10⁻⁷ per degree Celsius), which ensures excellent structural flexibility and thermomechanical properties. They show high thermal stability with ultralow strength degradation (less than 1 per cent) after sharp thermal shocks, and a high working temperature (up to 1,300 degrees Celsius). By deliberately entrapping residue carbon species in the constituent hypocrystalline zircon fibres, we substantially reduce the thermal radiation heat transfer and achieve one of the lowest high-temperature thermal conductivities among ceramic aerogels so far—104 milliwatts per metre per kelvin at 1,000 degrees Celsius. The combined thermomechanical and thermal insulating properties offer an attractive material system for robust thermal insulation under extreme conditions.