Metallic nanostructure-based surface plasmon sensors are capable of real-time, label-free, and multiplexed detections for chemical and biomedical applications. Recently, the studies of aluminum-based biosensors have attracted a large attention because aluminum is a more cost-effective metal and relatively stable. However, the intrinsic properties of aluminum, having a large imaginary part of the dielectric function and a longer evanescent length, limit its sensing capability. Here we show that capped aluminum nanoslits fabricated on plastic films using hot embossing lithography can provide tailorable Fano resonances. Changing height of nanostructures and deposited metal film thickness modulated the transmission spectrum, which varied from Wood’s anomaly-dominant resonance, asymmetric Fano profile to surface plasmon-dominant resonance. For biolayer detections, the maximum surface sensitivity occurred at the dip of asymmetric Fano profile. The optimal Fano factor was close to −1.3. The wavelength and intensity sensitivities for surface thickness were up to 2.58 nm/nm and 90%/nm, respectively. The limit of detection (LOD) of thickness reached 0.018 nm. We attributed the enhanced surface sensitivity for capped aluminum nanoslits to a reduced evanescent length and sharp slope of the asymmetric Fano profile. The protein-protein interaction experiments verified the high sensitivity of capped nanostructures. The LOD was down to 236 fg/mL.

A series of ternary Zintl phases, Ca(2)CdP(2), Ca(2)CdAs(2), Sr(2)CdAs(2), Ba(2)CdAs(2), and Eu(2)CdAs(2), have been synthesized through high temperature metal flux reactions, and their structures have been characterized by single-crystal X-ray diffraction. They belong to the Yb(2)CdSb(2) structure type and crystallize in the orthorhombic space group Cmc2(1) (No. 36, Z = 4) with cell dimensions of a = 4.2066(5), 4.3163(5), 4.4459(7), 4.5922(5), 4.4418(9) Å; b = 16.120(2), 16.5063(19), 16.904(3), 17.4047(18), 16.847(4) Å; c = 7.0639(9), 7.1418(8), 7.5885(11), 8.0526(8), 7.4985(16) Å for Ca(2)CdP(2) (R1 = 0.0152, wR2 = 0.0278), Ca(2)CdAs(2) (R1 = 0.0165, wR2 = 0.0290), Sr(2)CdAs(2) (R1 = 0.0238, wR2 = 0.0404), Ba(2)CdAs(2) (R1 = 0.0184, wR2 = 0.0361), and Eu(2)CdAs(2) (R1 = 0.0203, wR2 = 0.0404), respectively. Among these, Ca(2)CdAs(2) was found to form with another closely related structure, depending on the experimental conditions--monoclinic space group Cm (No. 8, Z = 10) with lattice constants a = 21.5152(3) Å, b = 4.30050(10) Å, c = 14.3761(2) Å and β = 110.0170(10)° (R1 = 0.0461, wR2 = 0.0747). UV/vis optical absorption spectra for both forms of Ca(2)CdAs(2) show band gaps on the order of 1.0 eV, suggesting semiconducting properties, which have also been confirmed through electronic band structure calculations based on the density-functional theory. Results from differential scanning calorimetry measurements probing the thermal stability and phase transitions in the two Ca(2)CdAs(2) polymorphs are discussed. Magnetic susceptibility measurements for Eu(2)CdAs(2), indicating divalent Eu(2+) cations, are presented as well.

Massive amount of water level data has been collected by using Internet of Things (IoT) techniques in the Yangtze River and other rivers. In this paper, utilizing these data to construct deep neural network models for water level prediction is focused. To achieve higher accuracy, both the factors of time and locations of data collection sensors are considered to perform prediction. And the network structures of gated recurrent unit (GRU) and convolutional neural network (CNN) are combined to build a CNN-GRU model in which the GRU part learns the changing trend of water level, and the CNN part learns the spatial correlation among water level data observed from adjacent water stations. The CNN-GRU model that using data from multiple locations to predict the water level of the middle location has higher accuracy than the model only based on GRU and other state-of-the-art methods including autoregressive integrated moving average model (ARIMA), wavelet-based artificial neural network (WANN) and long-short term memory model (LSTM), because of its ability to decrease the affections of abnormal value and data randomness of a single water station to some extent. The results are verified on an experiment dataset that including 30-year observed data of water level at several collection stations in the Yangtze River. For forecasting the 8-o'clock water levels of future 5 days, accuracy of the CNN-GRU model is better than that of ARIMA, WANN and LSTM models with three evaluation factors including Nash-Sutcliffe efficiency coefficient (NSE), average relative error (MRE) and root mean square error (RMSE).

Bright red emission has been obtained at room temperature from Eu-doped GaN films pumped by 325 nm HeCd laser. The luminescent films were grown by metalorganic chemical vapor deposition on GaN/Al2O3 substrates. Trimethylgallium (TMGa), ammonia (NH3), and europium 2,2,4,4-tetramethyl-3,5-heptanedionate were used as sources for Ga, N, and Eu dopant, respectively. The influence of the V/III ratio during growth on the photoluminescence (PL) intensity has been studied using a fixed TMGa flow rate of 92 μmol/min and varying the NH3 flow rate. The film growth rate (∼2 μm/h) is nearly constant with V/III ratio over the range from ∼30 to ∼1000. The Eu incorporation in GaN films was found to decrease with increasing V/III ratio. The Eu PL intensity (normalized to the Eu concentration) exhibited a maximum at a V/III ratio of ∼100.