The directional freezing of micro-fiber suspensions is used to assemble highly porous (porosities ranging between 92 to 98%) SiC networks. These networks exhibit a unique hierarchical architecture in which thin layers with honeycomb-like structure and internal strut length in the order of 1 to 10 µm in size are aligned with an interlayer spacing ranging between 15 to 50 microns. The resulting structures exhibit strengths (up to 3 MPa) and stiffness (up to 0.3 GPa) that are higher than aerogels of similar density and comparable to other ceramic micro-lattices fabricated by vapor deposition. Furthermore, this wet processing technique allows the fabrication of large-size samples that are stable at high temperature, with acoustic impedance that can be manipulated over one order of magnitude (0.03-0.3 MRayl), electrically conductive and with very low thermal conductivity. The approach could be extended to other ceramic materials and opens new opportunities for the fabrication of ultralight structures with unique mechanical and functional properties in practical dimensions.2

BackgroundNon-contact resonant ultrasound spectroscopy (NC-RUS) has been proven as a reliable technique for the dynamic determination of leaf water status. It has been already tested in more than 50 plant species. In parallel, relative water content (RWC) is highly used in the ecophysiological field to describe the degree of water saturation in plant leaves. Obtaining RWC implies a cumbersome and destructive process that can introduce artefacts and cannot be determined instantaneously.ResultsHere, we present a method for the estimation of RWC in plant leaves from non-contact resonant ultrasound spectroscopy (NC-RUS) data. This technique enables to collect transmission coefficient in a [0.15–1.6] MHz frequency range from plant leaves in a non-invasive, non-destructive and rapid way. Two different approaches for the proposed method are evaluated: convolutional neural networks (CNN) and random forest (RF). While CNN takes the entire ultrasonic spectra acquired from the leaves, RF only uses four relevant parameters resulted from the transmission coefficient data. Both methods were tested successfully in Viburnum tinus leaf samples with Pearson’s correlations between 0.92 and 0.84.ConclusionsThis study showed that the combination of NC-RUS technique with deep learning algorithms is a robust tool for the instantaneous, accurate and non-destructive determination of RWC in plant leaves.

Fresh water is a key natural resource for food production, sanitation and industrial uses and has a high environmental value. The largest water use worldwide (~70%) corresponds to irrigation in agriculture, where use of water is becoming essential to maintain productivity. Efficient irrigation control largely depends on having access to reliable information about the actual plant water needs. Therefore, fast, portable and non-invasive sensing techniques able to measure water requirements directly on the plant are essential to face the huge challenge posed by the extensive water use in agriculture, the increasing water shortage and the impact of climate change. Non-contact resonant ultrasonic spectroscopy (NC-RUS) in the frequency range 0.1–1.2 MHz has revealed as an efficient and powerful non-destructive, non-invasive and in vivo sensing technique for leaves of different plant species. In particular, NC-RUS allows determining surface mass, thickness and elastic modulus of the leaves. Hence, valuable information can be obtained about water content and turgor pressure. This work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure. A sensing prototype is proposed, described and, as application example, used to study two different species: Vitis vinifera and Coffea arabica, whose leaves present thickness resonances in two different frequency bands (400–900 kHz and 200–400 kHz, respectively), These species are representative of two different climates and are related to two high-added value agricultural products where efficient irrigation management can be critical. Moreover, the technique can also be applied to other species and similar results can be obtained.

The large water requirements of Vitis vinifera L. together with an increase in temperature and drought events imply the need for irrigation in the driest areas of its distribution range. Generous watering may reduce grape quality so irrigation should be precisely regulated through the development of new methods of accurate irrigation scheduling based on plant 'stress sensing'. Two new methods, the reflectivity in the S-band and the broadband ultrasonic spectroscopy, can be used as non-invasive and reproducible techniques for the study of plant water relations in V. vinifera. On one hand, the measurement of reflectance at frequencies around 2.4 GHz gives an excellent accuracy when the changes in the existing area (S) between two reflectance curves are correlated with the relative water content (RWC). On the other hand, an improvement of the broadband ultrasonic spectroscopy based on the enlargement of the analysis frequency window provides, apart from the determination of the turgor loss point (TLP), additional information about the leaves without additional computational cost or additional leaf information requirements. Before TLP, the frequency associated with the maximum transmittance (f/f(o)), the macroscopic elastic constant of the leaf in the Z direction (c(33)) and, specially, the variation of the attenuation coefficient with the frequency (n), were highly correlated with changes in RWC. Once turgor is lost, a shift in the parameters directly related to the attenuation of the signal was also observed. The use of both techniques allows for a more convincing knowledge of the water status in V. vinifera.