Nanosheets with highly regulated nanopores are ultimately thin functional materials for diverse applications including molecular separation and detection, catalysis, and energy conversion and storage. However, their availability has hitherto been restricted to layered parent materials, covalently bonded sheets, which are layered via relatively weak electrostatic interactions. Here, we report a rational bottom-up methodology that enables nanosheet creation beyond the layered systems. We employ the air/liquid interface to assemble a triphenylbenzene derivative into perfectly oriented highly crystalline noncovalent-bonded organic nanosheets under ambient conditions. Each molecular building unit connects laterally by hydrogen bonding, endowing the nanosheets with size- and position-regulated permanent nanoporosity, as established by in situ synchrotron X-ray surface crystallography and gas sorption measurements. Notably, the nanosheets are constructed specifically by interfacial synthesis, which suppresses the intrinsic complex interpenetrated structure of the bulk crystal. Moreover, they possess exceptional long-term and thermal stability and are easily transferrable to numerous substrates without loss of structural integrity. Our work shows the power of interfacial synthesis using a suitably chosen molecular component to create two-dimensional (2D) nanoassemblies not accessible by conventional bulk crystal exfoliation techniques.
Surface modification of inorganic objects with metal-organic frameworks (MOFs) - organic-inorganic hybrid framework materials with infinite networks - opens wide windows for potential applications. In order to derive a target property, the key is the ability to fine tune the degree of modification. Solution-based step-by-step growth techniques provide excellent control of layer thickness which can be varied with the number of deposition cycles. Such techniques with MOFs have been mainly applied to flat substrates, but not to particle surfaces before. Here, we present the facile surface modification of inorganic particles with a framework compound under operationally simple ambient conditions. A solution-based sequential technique involving the alternate immersion of LiCoO2 (LCO) - a positive electrode material for a lithium ion battery - into FeCl2·4H2O and K3[Fe(CN)6] solutions results in the formation of Prussian blue (PB) nanolayers on the surface of the LCO particles (PBNL@LCO). The PB growth is finely controlled by the number of immersion cycles. An electrochemical cell with PBNL@LCO as a positive electrode material exhibits a discharge capacity close to the specific capacity of LCO. The results open a new direction for creating suitable interfacial conditions between electrode materials and electrolytes in secondary battery materials.
Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification. Furthermore, novel possibilities have been demonstrated, such as inferring the existence of previously unseen attractors. Sampling, in contrast, has a strong influence on such functions. Sampling is indispensable in a physical reservoir computer that uses an existing physical system as a reservoir because the use of an external digital system for the data input is usually inevitable. This study analyzes the effect of sampling on the ability of reservoir computing to autonomously regenerate chaotic time series. We found, as expected, that excessively coarse sampling degrades the system performance, but also that excessively dense sampling is unsuitable. Based on quantitative indicators that capture the local and global characteristics of attractors, we identify a suitable window of the sampling frequency and discuss its underlying mechanisms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.