Most two-dimensional (2D) covalent organic frameworks (COFs) are non-fluorescent in the solid state even when they are constructed from emissive building blocks. The fluorescence quenching is usually attributed to non-irradiative rotation-related or π–π stacking-caused thermal energy dissipation process. Currently there is a lack of guiding principle on how to design fluorescent, solid-state material made of COF. Herein, we demonstrate that the eclipsed stacking structure of 2D COFs can be used to turn on, and tune, the solid-state photoluminescence from non-emissive building blocks by the restriction of intramolecular bond rotation via intralayer and interlayer hydrogen bonds among highly organized layers in the eclipse-stacked COFs. Our COFs serve as a platform whereby the size of the conjugated linkers and side-chain functionalities can be varied, rendering the emission colour-tuneable from blue to yellow and even white. This work provides a guide to design new solid-state emitters using COFs.
A crucial ingredient in lithium (Li) and sodium (Na)-ion batteries (LIBs and NIBs) is the electrolytes. The use of Li-metal (Na-metal) as anode in liquid electrolyte LIBs (NIBs) is constrained...
Surface functionalization with organic electron donors (OEDs) is an effective doping strategy for 2D materials, which can achieve doping levels beyond those possible with conventional electric field gating. While the effectiveness of surface functionalization has been demonstrated in many 2D systems, the doping efficiencies of OEDs have largely been unmeasured, which is in stark contrast to their precision syntheses and tailored redox potentials. Here, using monolayer MoS2 as a model system and an organic reductant based on 4,4′‐bipyridine (DMAP‐OED) as a strong organic dopant, it is established that the doping efficiency of DMAP‐OED to MoS2 is in the range of 0.63 to 1.26 electrons per molecule. The highest doping levels to date are also achieved in monolayer MoS2 by surface functionalization and demonstrate that DMAP‐OED is a stronger dopant than benzyl viologen, which is the previous best OED dopant. The measured range of the doping efficiency is in good agreement with the values predicted from first‐principles calculations. This work provides a basis for the rational design of OEDs for high‐level doping of 2D materials.
The identification of alternatives to the lithium-ion battery architecture remains a crucial priority in the diversification of energy storage technologies. Accompanied by the low reduction potential of Ca2+/Ca, −2.87 V vs standard hydrogen electrode, metal-anode-based rechargeable calcium (Ca) batteries appear competitive in terms of energy densities. However, the development of Ca batteries lacks high-energy-density intercalation cathode materials. Using first-principles methodologies, we screen a large chemical space for potential Ca-based cathode chemistries, with composition of Ca i TM j Z k , where TM is a first- or second-row transition metal and Z is oxygen, sulfur, selenium, or tellurium. Ten materials are selected, and their Ca intercalation properties are investigated. We identify two previously unreported promising electrode compositions: the post-spinel CaV2O4 and the layered CaNb2O4, with Ca migration barriers of ∼654 and ∼785 meV, respectively. We analyze the geometrical features of the Ca migration pathways across the 10 materials studied and provide an updated set of design rules for the identification of good ionic conductors, especially with large mobile cations.
Ion transport in materials is routinely probed through several experimental techniques, which introduce variability in reported ionic diffusivities and conductivities. The computational prediction of ionic diffusivities and conductivities helps in identifying good ionic conductors, and suitable solid electrolytes, thus establishing firm structure-property relationships. Machine-learned potentials are an attractive strategy extending the capabilities of accurate \emph{ab initio} molecular dynamics to longer and larger simulations, enabling simulations of ion transport at low-temperature. However, being machine-learned potentials in their infancy, critical assessments of their predicting capabilities are seldom. Here, we identified the main factors controlling the quality of machine-learning potentials based on the moment tensor potential when applied to the properties of ion transport in solid electrolytes. Our results underline the importance of high-quality training sets in fitting moment tensor potentials, diverse training sets, and the importance of intrinsic defects which may occur in solid electrolytes. We demonstrate the limitations posed by short-time scale and high-temperature AIMD simulations to predict the room-temperature properties of materials.
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