Materials with an ultralow density and ultrahigh electromagnetic-interference (EMI)-shielding performance are highly desirable in fields of aerospace, portable electronics, and so on. Theoretical work predicts that 3D carbon nanotube (CNT)/graphene hybrids are one of the most promising lightweight EMI shielding materials, owing to their unique nanostructures and extraordinary electronic properties. Herein, for the first time, a lightweight, flexible, and conductive CNT-multilayered graphene edge plane (MLGEP) core-shell hybrid foam is fabricated using chemical vapor deposition. MLGEPs are seamlessly grown on the CNTs, and the hybrid foam exhibits excellent EMI shielding effectiveness which exceeds 38.4 or 47.5 dB in X-band at 1.6 mm, while the density is merely 0.0058 or 0.0089 g cm , respectively, which far surpasses the best values of reported carbon-based composite materials. The grafted MLGEPs on CNTs can obviously enhance the penetration losses of microwaves in foams, leading to a greatly improved EMI shielding performance. In addition, the CNT-MLGEP hybrids also exhibit a great potential as nano-reinforcements for fabricating high-strength polymer-based composites. The results provide an alternative approach to fully explore the potentials of CNT and graphene, for developing advanced multifunctional materials.

No abstract

Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE = 18.87±2.25 [mg/dL] over a 60minute horizon) and real patient cases (RMSE = 21.07±2.35 [mg/dL] for 30-minute, RMSE = 33.27±4.79% for 60-minute).In addition, the model provides competitive performance in providing effective prediction horizon (P H ef f ) with minimal time lag both in a simulated patient dataset (P H ef f = 29.0±0.7 for 30-min and P H ef f = 49.8±2.9 for 60-min) and in a real patient dataset (P H ef f = 19.3±3.1 for 30-min and P H ef f = 29.3±9.4 for 60-min). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 10 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6ms on a phone compared to an execution time of 780ms on a laptop.

No abstract

A Cu/SSZ-13 catalyst containing predominately Z2Cu sites is prepared. Using FTIR spectroscopy, two nonsteady-state measurements, (1) continuous NO titration of an NH3 saturated catalyst and (2) intermittent NO on/off cycles during quasi-steady state (i.e., NO perturbation), are conducted to shed light on active sites, reaction intermediates, and possible reaction mechanisms. During continuous NO titration, a strong NH3 inhibition effect is found, where Cu active sites containing NH3 ligands are less active than Cu sites depleted of NH3 ligands. In the NO perturbation experiments, it is demonstrated that NO+NH3 interactions lead to formation of Brønsted acid sites, which further interact with NH3 to form NH4 +. Perturbation measurements, using ND3 to replace NH3 in order to distinguish ammonia and nitrate vibrations, further show that NH4NO3 and other surface nitrates are not involved in the SCR process under typical low-temperature quasi-steady-state conditions.

A novel γ-MnO2 catalyst obtained by selective removal and other traditional MnO2 catalysts (α-, β-, δ-MnO2) were studied for their catalytic performance in toluene combustion. The new γ-MnO2 catalyst showed the best catalytic activity owing to its three-dimensional macroporous and mesoporous morphology and the γ-MnO2-like structure.

No abstract

For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in-silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28±2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83±3.49 mg/dL) with time lag (17.78±8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm.