Co Nanoparticles Encapsulated in Carbon Nanotubes Decorated Carbon Aerogels Toward Excellent Microwave Absorption

Broadband electromagnetic wave absorption using pure carbon aerogel by synergistically modulating propagation path and carbonization degree

Carbon materials were widely used as electromagnetic (EM) wave absorption due to their advantages of light weight, environmental resistance and high electrical conductivity. However, conventional means were typically available by combining carbon and other materials to achieve effective absorption. Herein, a novel strategy using pure carbon aerogel with oriented structure was reported to enhance the EM wave absorption by synergistically modulating the wave propagation path and carbonization degree. The aerogel contained proposed modified carbon nanofibers (MCNF) derived from bacterial cellulose (BC), and core-shell carbon nanofibers @ reduced oxide graphene (CNF@RGO). The oriented structure was induced by the temperature field, which manifests anisotropic EM constitutive parameters (εx ≠ εz) at different directions of incident wave. The carbonization degree was adjusted by varying the carbonization temperature. At the carbonization temperature of 700 °C, the maximum reflection loss and effective absorption bandwidth reached -53.94 dB and 7.14 GHz, respectively, enabling the aerogel to outperform its previous counterparts. To clarify the EM wave mode-of-action in conjunction, physical models of the aerogel were established in addition to finite element simulation and theoretical analysis. Notably, the aerogel with a density of 3.6 mg/cm3 featured ultra-light weight, superhydrophobicity, superior compressibility, and thermal insulation. Our work offers an efficient strategy for designing broadband and multifunctional EM wave absorption materials (EWAMs), promising great potentials in complex stealth equipment.

QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery

Structural elucidation of chemical compounds is challenging experimentally, and theoretical chemistry methods have added important insight into molecules, nanoparticles, alloys, and materials geometries and properties. However, finding the optimum structures is a bottleneck due to the huge search space, and global search algorithms have been used successfully for this purpose. In this work, we present the quantum machine learning software/agent for materials design and discovery (QMLMaterial), intended for automatic structural determination in silico for several chemical systems: atomic clusters, atomic clusters and the spin multiplicity together, doping in clusters or solids, vacancies in clusters or solids, adsorption of molecules or adsorbents on surfaces, and finally atomic clusters on solid surfaces/materials or encapsulated in porous materials. QMLMaterial is an artificial intelligence (AI) software based on the active learning method, which uses machine learning regression algorithms and their uncertainties for decision making on the next unexplored structures to be computed, increasing the probability of finding the global minimum with few calculations as more data is obtained. The software has different acquisition functions for decision making (e.g., expected improvement and lower confidence bound). Also, the Gaussian process is available in the AI framework for regression, where the uncertainty is obtained analytically from Bayesian statistics. For the artificial neural network and support vector regressor algorithms, the uncertainty can be obtained by K-fold cross-validation or nonparametric bootstrap resampling methods. The software is interfaced with several quantum chemistry codes and atomic descriptors, such as the many-body tensor representation. QMLMaterial's capabilities are highlighted in the current work by its applications in the following systems: Na20, Mo6C3 (where the spin multiplicity was considered), H2O@CeNi3O5, Mg8@graphene, Na3Mg3@CNT (carbon nanotube).