Controlling the synthesis of atomic-thick nanosheets of nonlayered materials is extremely challenging because of the lack of an intrinsic driving force for anisotropic growth of two-dimensional (2D) structures. In that case, control of the anisotropy such as oriented attachment of small building blocks during the reaction process will be an effective way to achieve 2D nanosheets. Those atomic-thick nanosheets possess novel electronic structures and physical properties compared with the corresponding bulk samples. Here we report Co(9)Se(8) single-crystalline nanosheets with atomic thickness and unique lamellar stacking formed by 2D oriented attachment. The atomic-thick Co(9)Se(8) nanosheets were found to exhibit intrinsic half-metallic ferromagnetism, as supported by both our experimental measurements and theoretical calculations. This work will not only open a new door in the search for new half-metallic ferromagnetic systems but also pave a practical way to design ultrathin, transparent, and flexible paperlike spintronic devices.

Spontaneous exfoliation of MoS2 is achieved in H2O2-NMP mixed solvents with a yield of over 60 wt%, operated under mild conditions. H2O2-prompted sheet-tailoring effect induces a size evolution of MoS2 nanosheets from micro-scale to nano-scale. Furthermore, the concurrent dissolution also affords an approach to introduce structural defects in the nanosheets.

3D thick electrode design is a promising strategy to increase the energy density of lithium-ion batteries but faces challenges such as poor rate and limited cycle life. Herein, a coassembly method is employed to construct low-tortuosity, mechanically robust 3D thick electrodes. LiFe 0.7 Mn 0.3 PO 4 nanoplates (LFMP NPs) and graphene are aligned along the growth direction of ice crystals during freezing and assembled into sandwich frameworks with vertical channels, which prompts fast ion transfer within the entire electrode and reveals a 2.5-fold increase in ion transfer performance as opposed to that of random structured electrodes. In the sandwich framework, LFMP NPs are entrapped in the graphene wall in a "plate-on-sheet" contact mode, which avoids the detachment of NPs during cycling and also constitutes electron transfer highways for the thick electrode. Such vertical-channel sandwich electrodes with mass loading of 21.2 mg cm −2 exhibit a superior rate capability (0.2C-20C) and ultralong cycle life (1000 cycles). Even under an ultrahigh mass loading of 72 mg cm −2 , the electrode still delivers an areal capacity up to 9.4 mAh cm −2 , ≈2.4 times higher than that of conventional electrodes. This study provides a novel strategy for designing thick electrodes toward high performance batteries.

Purpose To summarize current non-exercise prediction models to estimate cardiorespiratory fitness (CRF), cross-validate these models, and apply them to predict health outcomes. Methods PubMed search was up to August 2018 for eligible publications. The current review was comprised of three steps. The first step was to search the literature on non-exercise prediction models. The key words combined non-exercise, CRF and one among prediction, prediction model, equation, prediction equation and measurement. The second step was to search the literature about cross-validation of non-exercise equations. The key words included non-exercise, CRF and one among validation, cross-validation and validity. The last step was to search for application of CRF assessed from non-exercise equations. The key words were non-exercise, CRF, mortality, all-cause mortality, cardiovascular disease (CVD) mortality and cancer mortality. Results Sixty non-exercise equations were identified. Age, gender, percent body fat, body mass index, weight, height and physical activity status were commonly used in the equations. Several researchers cross-validated non-exercise equations and proved their validity. In addition, non-exercise estimated CRF was significantly associated with all-cause mortality and fatal and nonfatal CVD. Conclusions Measurement of CRF from non-exercise models is practical and viable when exercise testing is not feasible. Despite the limitations of equations, application of CRF from non-exercise methods showed accuracy and predictive ability.