A polymer sponge with dual absorption of mechanical and electromagnetic energy.

Multifunctional hydrophilic MXene/Gelatin composite aerogel with vertically aligned channels for efficient sustainable solar water evaporation and Oil/Water separation

Carbon Layer Encapsulation Strategy for Designing Multifunctional Core-Shell Nanorod Aerogels as High-Temperature Thermal Superinsulators

https://www.mdpi.com/2079-6374/12/10/876/pdf?version=1665840346

Electrospinning-Based Biosensors for Health Monitoring

Ceramic matrix composites (CMCs) are designed to overcome the main drawback of monolithic ceramics, namely their brittleness, and are constituently expressed as a continuous phase, or matrix, a distributed phase, commonly referred to as the reinforcing fibers, and also an interphase layer or coating layer between them. In this regard, there is a necessity to understand the principles for choosing the reinforcing fibers and designing the fiber–matrix interfaces. Hence, an attempt is made to review the recent progresses on ceramic micro-nano fibers and their effect on the interfaces in CMCs. The development trend for CMCs is discussed, especially the future CMCs based on ceramic micro-nano fibers, including the strengthening and interface construction concerning with these fibers. • A brief review of current progress in the field of ceramic matrix composites is presented. • Two essential issues of fiber candidates and interface design are discussed. • Evolution from microfibers to nanofibers toward next-generation ceramic matrix composites is prospected. 

Evolution from microfibers to nanofibers toward next-generation ceramic matrix composites: A review

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 Celsius1-3. 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 failure4-6. 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. 

/pdf/hypocrystalline-ceramic-aerogels-for-thermal-insulation-at-1wuicd9b.pdf

Hypocrystalline ceramic aerogels for thermal insulation at extreme conditions

An intelligent data-driven method of modeling nonlinear dynamical systems named as LSTM network with output recurrence (OR-LSTM) is proposed, which can learn the inherent characteristics of the dynamical systems from data and predict the states of the systems given external excitation and initial conditions. For the proposed OR-LSTM architecture, the most important key point is output recurrence connections. In order to evaluate it, a teacher-forcing LSTM network without output recurrence connections (TF-LSTM) is designed for comparison. The differences between OR-LSTM and TF-LSTM from the perspective of gradient propagation and parameter updating are analyzed firstly. To verify the capability of the LSTM networks in dynamical systems modeling, the oscillation of a 42-story reinforced concrete frame-core tube building under severe and extreme seismic excitations are modeled. The results show that the OR-LSTM network performs well and maintains the long-term robustness in earthquake responses prediction, even only known displacements of 9 stories. However, the TF-LSTM network fails. In order to evaluate the extensive practicability of the OR-LSTM network, the famous Van der Pol dynamic system and Lorenz dynamic system with strong nonlinearity are also modeled by the proposed method. The results show that OR-LSTM network is competent in modeling most of nonlinear dynamic systems, while has limited in fully chaotic systems, but the predictable length of time sequence and prediction accuracy are improved. 

Intelligent modeling of nonlinear dynamical systems by machine learning

Machine learning modeling for the near-wake mean velocity deficit profiles behind a rough circular cylinder

This paper describes an experimental study in which a novel nonequilibrium self-similarity/self-preservation region is found to exist in the wakes of both a rotor and a fractal plate. In this novel nonequilibrium self-similarity region, the ratio of the mean flow length scale to the turbulence length scale and the ratio of the mean flow velocity scale to the turbulence velocity scale are not constant in the streamwise direction. However, in this region, the ratio of the mean flow time scale to the turbulence time scale is approximately constant in the streamwise direction. Using the simplified Reynolds-averaged equation of motion, new scaling laws are derived for this novel nonequilibrium self-similarity region, and these are confirmed by experimental measurements. Significant differences are observed between the rotor wake and fractal plate wake. The rotor wake reaches a self-similarity state much earlier than the fractal plate wake. A transition in the self-similarity state and scaling laws is found to occur in the rotor wake within the measurement region considered in the present study (3 D-20 D, where D is the effective diameter of the wake generator).

Shu Jin Laima

Papers

Hypocrystalline ceramic aerogels for thermal insulation at extreme conditions

Intelligent modeling of nonlinear dynamical systems by machine learning

Machine learning modeling for the near-wake mean velocity deficit profiles behind a rough circular cylinder

Novel scaling laws in the nonequilibrium turbulent wake of a rotor and a fractal plate

Three-dimensional effects for flow past a wavy cylinder at low Reynolds numbers