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Graphene has been regarded as a promising candidate in microwave absorption field but still faces some major challenges, including the limitation of mass production and poor impedance matching. Here, we demonstrate a simple approach to fabricate multilayer graphene in a kilo-mass/hour (≥2.5 kg/h) scale through an oxidation-thermal expansion-air convection shearing process. The subsequent incorporation of hexagonal boron nitride nanoparticles (h-BNNPs) can effectively tailor the dielectric and magnetic properties of the as-obtained multilayer graphene, which can significantly boost its microwave absorption performance. The as-obtained multilayer graphene/h-BNNP hybrid with 40 wt.% of h-BNNPs, exhibits extremely low reflection loss value of −67.35 dB at 8.04 GHz when the absorber thickness is 3.29 mm, ranking it as one of the most attractive absorbers reported to date. Moreover, the multilayer graphene/h-BNNP hybrids possess low densities less than 0.45 g/cm3, making them very attractive for practical microwave absorption application.

Hexagonal boron nitride nanosheets (h-BNNSs), with a crystal lattice structure similar to graphene by over 98%, exhibit good lubrication properties as lubricant additives. However, the poor dispersibility in solvents has limited their wide practical applications as lubricant additives. In the present report, water dispersible Pebax functionalized h-BNNSs (Pebax-BNNSs) have been prepared through a one-step solvent-free mechanical exfoliation process which relies on a simple exfoliation of h-BN layers by shearing force in molten Pebax at 200 °C. In this process, Pebax molecules can synchronously react with the dangling bonds formed during the exfoliation process to achieve in situ functionalization of h-BNNSs. The reciprocating friction tests demonstrate that the as-obtained Pebax-BNNSs possess excellent antifriction and antiwear performance as water-based lubricant additive with a low concentration of 0.3 mg/mL under atmospheric condition. The friction coefficients can be h-BNNSs can achieve superlubrication as water-based lubricant additives via facile surface modification, making them very promising candidates as lubricant additives in practical applications.

Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts), JMA (Japan Meteorological Agency), NCEP (National Centers for Environmental Prediction), and UKMO (United Kingdom Met Office) in THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) datasets, for the Northern Hemisphere (10°–87.5°N, 0°–360°) from 1 June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean (BREM), the multiple linear regression based superensemble (LRSUP), and the neural network based superensemble (NNSUP) techniques for the forecast period from 8 to 31 August 2007. The forecast skills are verified by using the root-mean-square errors (RMSEs). Comparative analysis of forecast results by using the BREM, LRSUP, and NNSUP shows that the multimodel ensemble forecasts have higher skills than the best single model for the forecast lead time of 24–168 h. A roughly 16% improvement in RMSE of the 500-hPa geopotential height is possible for the superensemble techniques (LRSUP and NNSUP) over the best single model for the 24–120-h forecasts, while it is only 8% for BREM. The NNSUP is more skillful than the LRSUP and BREM for the 24–120-h forecasts. But for 144–168-h forecasts, BREM, LRSUP, and NNSUP forecast errors are approximately equal. In addition, it appears that the BREM forecasting without the UKMO model is more skillful than that including the UKMO model, while the LRSUP forecasting in both cases performs approximately the same. A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.