Novel graphene-confined tin nanosheets (G/Sn/G) are constructed using an elaborately designed glucose-assisted chemical protocol. The as-synthesized G/Sn/G are featured with significantly enhanced lithium storage properties when compared with other graphene-based 0D/2D composite nanostructures, disclosing the merits of the 2D/2D composite featured with a surface-to-surface integration formula between graphene and the second 2D phase.

An acid-assisted ultrarapid thermal strategy is developed for constructing specifically functionalized graphene. The electrochemical performance of functionalized graphene can be boosted via elaborate coupling between the pseudocapacitance and the electronic double layer capacitance through rationally tailoring the structure of graphene sheets. This presents an opportunity for developing further high-performance graphene-based electrodes to bridge the performance gap between traditional capacitors and batteries.

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.A novel high performance electrode material for supercapacitor applications, terephthalonitrile-derived nitrogen-rich network (TNN), is developed successfully via temperature-dependent cross-linking of terephthalonitrile monomers. This work opens up a new window for seeing a versatile modular toolbox derived from various aromatic nitrile monomers for developing better electrode materials in the future

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Nanomaterials (NMs) have developed quickly and cover various fields, but research on nanotechnology and NMs largely relies on costly experiments or complex calculations (e.g., density functional theory). In contrast, machine learning (ML) methods can address the large amount of time needed and labor consumption in material testing and achieve big-data, high-throughput screening, boosting the design and application of NMs. ML is a powerful tool for NM research; however, large knowledge gaps and critical issues should be promptly addressed to promote NMs from the laboratory to industry. With a focus on the primary NM aspects, enhancements to the design of NM structures, properties, adsorption, and catalysis by ML are reviewed and discussed. Given the emergent challenges in nanobiology, ML predictions of interactions between NMs and biology are also analyzed. Subsequently, this perspective discusses how to improve the interpretability of ML algorithms, which has been a bottleneck of ML in recent years. ML has led to innovations in the development of NMs, but some problems remain, such as imperfect databases and the accuracy of algorithm determination and nanopattern image recognition, which are herein addressed. Overall, this perspective provides insights for the development of ML in NM research.