By employing graphene quantum dots (GQDs), we have achieved a high efficiency of 16.55% in n-type Si heterojunction solar cells. The efficiency enhancement is based on the photon downconversion phenomenon of GQDs to make more photons absorbed in the depletion region for effective carrier separation, leading to the enhanced photovoltaic effect. The short circuit current and the fill factor are increased from 35.31 to 37.47 mA/cm2 and 70.29% to 72.51%, respectively. The work demonstrated here holds the promise for incorporating graphene-based materials in commercially available solar devices for developing ultrahigh efficiency photovoltaic cells in the future.

In this study, we have successfully demonstrated that a GaN nanowire (GaNNW) based extended-gate field-effect-transistor (EGFET) biosensor is capable of specific DNA sequence identification under label-free in situ conditions. Our approach shows excellent integration of the wide bandgap semiconducting nature of GaN, surface-sensitivity of the NW-structure, and high transducing performance of the EGFET-design. The simple sensor-architecture, by direct assembly of as-synthesized GaNNWs with a commercial FET device, can achieve an ultrahigh detection limit below attomolar level concentrations: about 3 orders of magnitude higher in resolution than that of other FET-based DNA-sensors. Comparative in situ studies on mismatches ("hotspot" mutations related to human p53 tumor-suppressor gene) and complementary targets reveal excellent selectivity and specificity of the sensor, even in the presence of noncomplementary DNA strands, suggesting the potential pragmatic application in complex clinical samples. In comparison with GaN thin film, NW-based EGFET exhibits excellent performance with about 2 orders higher sensitivity, over a wide detection range, 10(-19)-10(-6) M, reaching about a 6-orders lower detection limit. Investigations illustrate the unique and distinguished feature of nanomaterials. Detailed studies indicate a positive effect of energy band alignment at the biomaterials-semiconductor hybrid interface influencing the effective capacitance and carrier-mobility of the system.

We demonstrated that hierarchical structures combining different scales (i.e., pyramids from 1.5 to 7.5 μm in width on grooves from 40 to 50 μm in diameter) exhibit excellent broadband and omnidirectional light-trapping characteristics. These microscaled hierarchical structures could not only improve light absorption but prevent poor electrical properties typically observed from nanostructures (e.g., ultra-high-density surface defects and nonconformal deposition of following layers, causing low open-circuit voltages and fill factors). The microscaled hierarchical Si heterojunction solar cells fabricated with hydrogenated amorphous Si layers on as-cut Czochralski n-type substrates show a high short-circuit current density of 36.4 mA/cm(2), an open-circuit voltage of 607 mV, and a conversion efficiency of 15.2% due to excellent antireflection and light-scattering characteristics without sacrificing minority carrier lifetimes. Compared to cells with grooved structures, hierarchical heterojunction solar cells exhibit a daily power density enhancement (69%) much higher than the power density enhancement at normal angle of incidence (49%), demonstrating omnidirectional photovoltaic characteristics of hierarchical structures. Such a concept of hierarchical structures simultaneously improving light absorption and photocarrier collection efficiency opens avenues for developing large-area and cost-effective solar energy devices in the industry.

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Understanding polysulfide electrochemistry in high temperature sodium–sulfur (HT–Na–S) batteries is crucial for their practical applications. Currently the discharge capacity of commercial HT–Na–S battery achieves only one third of its theoretical capacity due to polysulfides formation, understanding of which is limited due to technical difficulty in direct imaging polysulfides. Herein, in situ transmission electron microscopy implemented with a microelectromechanical systems (MEMS) heating device is used to investigate the electrochemical reactions of HT–Na–S batteries. The formation and evolution of transient polysulfides during cycling are revealed in real‐time. Upon discharge, sulfur transforms to long‐chain polysulfides, short‐chain polysulfides, and finally Na2S or its mixture with polysulfides, and the process is reversible during charge at high temperatures. Surprisingly, by introducing nanovoids into the sulfur cathode to buffer the large volume change thus preserving the integrity of the electronic/ionic pathways and reducing the diffusion distance of Na+ ions, the sulfur cathode is fully discharged to Na2S rather than the conventionally observed Na2S2 at 300 °C. Moreover, the electrochemical reaction is swift and highly reversible. The in situ studies provide not only new understanding to the polysulfide electrochemistry, but also critical strategies to boost the capacity and cyclability of HT–Na–S batteries for large‐scale energy storage applications.

The classification performance of a brain-computer interface (BCI) depends largely on the methods of data recording and feature extraction. The electrocorticogram (ECoG)-based BCIs are a BCI modality that has the potential to achieve high classification accuracy. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery. The optimal channel subsets are first selected by genetic algorithms from multi-channel ECoG recordings, then the power features are extracted by common spatial pattern (CSP), and finally Fisher discriminant analysis (FDA) is used for classification. The algorithm is applied to Data set I of BCI Competition III and the classification accuracy of 90% is achieved on test set by using only seven channels.

Pansharpening is the process of fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. In the process of pansharpening, the LRMS image is often directly upsampled by a scale of 4, which may result in the loss of high-frequency details in the fused high-resolution multispectral (HRMS) image. To solve this problem, we put forward a novel progressive cascade deep residual network (PCDRN) with two residual subnetworks for pansharpening. The network adjusts the size of an MS image to the size of a PAN image twice and gradually fuses the LRMS image with the PAN image in a coarse-to-fine manner. To prevent an overly-smooth phenomenon and achieve high-quality fusion results, a multitask loss function is defined to train our network. Furthermore, to eliminate checkerboard artifacts in the fusion results, we employ a resize-convolution approach instead of transposed convolution for upsampling LRMS images. Experimental results on the Pléiades and WorldView-3 datasets prove that PCDRN exhibits superior performance compared to other popular pansharpening methods in terms of quantitative and visual assessments.

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