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Assembling 2D materials such as MXenes into functional 3D aerogels using 3D printing technologies gains attention due to simplicity of fabrication, customized geometry and physical properties, and improved performance. Also, the establishment of straightforward electrode fabrication methods with the aim to hinder the restack and/or aggregation of electrode materials, which limits the performance of the electrode, is of great significant. In this study, unidirectional freeze casting and inkjet‐based 3D printing are combined to fabricate macroscopic porous aerogels with vertically aligned Ti₃C₂T_xsheets. The fabrication method is developed to easily control the aerogel microstructure and alignment of the MXene sheets. The aerogels show excellent electromechanical performance so that they can withstand almost 50% compression before recovering to the original shape and maintain their electrical conductivities during continuous compression cycles. To enhance the electrochemical performance, an inkjet‐printed MXene current collector layer is added with horizontally aligned MXene sheets. This combines the superior electrical conductivity of the current collector layer with the improved ionic diffusion provided by the porous electrode. The cells fabricated with horizontal MXene sheets alignment as current collector with subsequent vertical MXene sheets alignment layers show the best electrochemical performance with thickness‐independent capacitive behavior.

 

Aerogels are highly porous structures produced by replacing the liquid solvent of a gel with air without causing a collapse in the solid network. Unlike conventional fabrication methods, additive manufacturing (AM) has been applied to fabricate 3D aerogels with customized geometries specific to their applications, designed pore morphologies, multimaterial structures, etc. To date, three major AM technologies (extrusion, inkjet, and stereolithography) followed by a drying process have been proposed to additively manufacture 3D functional aerogels. 3D‐printed aerogels and porous scaffolds showed great promise for a variety of applications, including tissue engineering, electrochemical energy storage, controlled drug delivery, sensing, and soft robotics. In this review, the details of steps included in the AM of aerogels and porous scaffolds are discussed, and a general frame is provided for AM of those. Then, the different postprinting processes are addressed to achieve the porosity (after drying); and mechanical strength, functionality, or both (after postdrying thermal or chemical treatments) are provided. Furthermore, the applications of the 3D‐printed aerogels/porous scaffolds made from a variety of materials are also highlighted. The review is concluded with the current challenges and an outlook for the next generation of 3D‐printed aerogels and porous scaffolds.

 

The comprehensive properties of high-entropy alloys (HEAs) are highly-dependent on their phases. Although a large number of machine learning (ML) algorithms has been successfully applied to the phase prediction of HEAs, the accuracies among different ML algorithms based on the same dataset vary significantly. Therefore, selection of an efficient ML algorithm would significantly reduce the number and cost of the experiments. In this work, phase prediction of HEAs (PPH) is proposed by integrating criterion and machine learning recommendation method (MLRM). First, a meta-knowledge table based on characteristics of HEAs and performance of candidate algorithms is established, and meta-learning based on the meta-knowledge table is adopted to recommend an algorithm with desirable accuracy. Secondly, an MLRM based on improved meta-learning is engineered to recommend a more desirable algorithm for phase prediction. Finally, considering poor interpretability and generalization of single ML algorithms, a PPH combining the advantages of MLRM and criterion is proposed to improve the accuracy of phase prediction. The PPH is validated by 902 samples from 12 datasets, including 405 quinary HEAs, 359 senary HEAs, and 138 septenary HEAs. The experimental results shows that the PPH achieves performance than the traditional meta-learning method. The average prediction accuracy of PPH in all, quinary, senary, and septenary HEAs is 91.6%, 94.3%, 93.1%, and 95.8%, respectively. 

Magnetic materials have brought innovations in the field of advanced materials. Their incorporation in aerogels has certainly broadened their application area. Magnetic aerogels can be used for various purposes from adsorbents to developing electromagnetic interference shielding and microwave absorbing materials, high-level diagnostic tools, therapeutic systems, and so on. Considering the final use and cost, these can be fabricated from a variety of materials using different approaches. To date, several studies have been published reporting the fabrication and uses of magnetic aerogels. However, to our knowledge, there is no review that specifically focuses only on magnetic aerogels, so we attempted to overview the main developments in this field and ended our study with the conclusion that magnetic aerogels are one of the emerging and futuristic advanced materials with the potential to offer multiple applications of high value.