An epigraphene platform for coherent 1D nanoelectronics

Exceptional edge state ballistic transport, first observed in graphene nanoribbons grown on the sidewalls of trenches etched in electronics grade silicon carbide even at room temperature, is shown here to manifest in micron scale epigraphene structures that are conventionally patterned on single crystal silicon carbide substrates. Electronic transport is dominated by a single electronic mode, in which electrons travel large distances without scattering, much like photons in an optical fiber. In addition, robust quantum coherence, non-local transport, and a ground state with half a conductance quantum are also observed. These properties are explained in terms of a ballistic edge state that is pinned at zero energy. The epigraphene platform allows interconnected nanostructures to be patterned, using standard microelectronics methods, to produce phase coherent 1D ballistic networks. This discovery is unique, providing the first feasible route to large scale quantum coherent graphene nanoelectronics, and a possible inroad towards quantum computing

Protected transport in the epigraphene edge state

The graphene edge state has long been predicted to be a zero energy, one-dimensional electronic waveguide mode that dominates transport in neutral graphene nanostructures, with potential application to graphene devices. However, its exceptional properties have been observed in only a few cases, each employing novel fabrication methods without a clear path to large-scale integration. We show here that interconnected edge-state networks can be produced using non-conventional facets of electronics grade silicon carbide wafers and scalable lithography, which cuts the epitaxial graphene and apparently fuses its edge atoms to the silicon carbide substrate. Measured epigraphene edge state (EGES) conduction is ballistic with mean free paths exceeding tens of microns, thousands of times greater than for the diffusive 2D bulk. It is essentially independent of temperature, decoupled from the bulk and substantially immune to disorder. Remarkably, EGES transport involves a non-degenerate conductance channel that is pinned at zero energy, yet it does not generate a Hall voltage, implying balanced electron and hole components. These properties, observed at all tested temperatures, magnetic fields, and charge densities, are not predicted by present theories, and point to a zero-energy spin one-half quasiparticle, composed of half an electron and a half a hole moving in opposite directions

Graphene‐Like Conjugated Molecule as Hole‐Selective Contact for Operationally Stable Inverted Perovskite Solar Cells and Modules

Further enhancing the operational lifetime of inverted-structure perovskite solar cells (PSCs) is crucial for their commercialization, and the design of hole-selective contacts at the illumination side plays a key role in operational stability. In this work, the self-anchoring benzo[rst]pentaphene (SA-BPP) is developed as a new type of hole-selective contact toward long-term operationally stable inverted PSCs. The SA-BPP molecule with a graphene-like conjugated structure shows a higher photostability and mobility than that of the frequently-used triphenylamine and carbazole-based hole-selective molecules. Besides, the anchoring groups of SA-BPP promote the formation of a large-scale uniform hole contact on ITO substrate and efficiently passivate the perovskite absorbers. Benefiting from these merits, the champion efficiencies of 22.03% for the small-sized cells and 17.08% for 5 × 5 cm2 solar modules on an aperture area of 22.4 cm2 are achieved based on this SA-BPP contact. Also, the SA-BPP-based device exhibits promising operational stability, with an efficiency retention of 87.4% after 2000 h continuous operation at the maximum power point under simulated 1-sun illumination, which indicates an estimated T80 lifetime of 3175 h. This novel design concept of hole-selective contacts provides a promising strategy for further improving the PSC stability.journal articl

Toward the Estimation of All-Weather Daytime Downward Longwave Radiation over the Tibetan Plateau

Downward longwave radiation (DLR) is a critical parameter for radiation balance, energy budget, and water cycle studies at regional and global scales. Accurate estimation of the all-weather DLR with a high temporal resolution is important for the estimation of the surface net radiation and evapotranspiration. However, most DLR products involve instantaneous DLR estimates based on polar orbiting satellite data under clear-sky conditions. To obtain an in-depth understanding of the performances of different models in the estimation of DLR over the Tibetan Plateau, which is a focus area of climate change study, this study tests eight methods for clear-sky conditions and six methods for cloudy conditions based on ground-measured data. It is found that the Dilley and O’Brien model and the Lhomme model are most suitable for clear-sky conditions and cloudy conditions, respectively. For the Dilley and O’Brien model, the average root mean square error (RMSE) of DLR under clear-sky conditions is approximately 22.5 W/m2 for nine ground sites; for the Lhomme model, the average RMSE is approximately 23.2 W/m2. Based on the estimated cloud fraction and meteorological data provided by the China Land Surface Data Assimilation System (CLDAS), hourly all-weather daytime DLR with a 0.0625° resolution over the Tibetan Plateau is estimated. Results demonstrate that the average RMSE of the estimated hourly all-weather DLR is approximately 26.4 W/m2. With the combined all-weather DLR model, the hourly all-weather daytime DLR dataset with a 0.0625° resolution from 2008 to 2016 over the Tibetan Plateau is generated. This dataset can contribute to studies associated with the radiation balance and energy budget, water cycle, and climate change over the Tibetan Plateau

Speech emotion recognition using wavelet packet reconstruction with attention-based deep recurrent neutral networks

Speech emotion recognition (SER) is a complicated and challenging task in the human-computer interaction because it is difficult to find the best feature set to discriminate the emotional state entirely. We always used the FFT to handle the raw signal in the process of extracting the low-level description features, such as short-time energy, fundamental frequency, formant, MFCC (mel frequency cepstral coefficient) and so on. However, these features are built on the domain of frequency and ignore the information from temporal domain. In this paper, we propose a novel framework that utilizes multi-layers wavelet sequence set from wavelet packet reconstruction (WPR) and conventional feature set to constitute mixed feature set for achieving the emotional recognition with recurrent neural networks (RNN) based on the attention mechanism. In addition, the silent frames have a disadvantageous effect on SER, so we adopt voice activity detection of autocorrelation function to eliminate the emotional irrelevant frames. We show that the application of proposed algorithm significantly outperforms traditional features set in the prediction of spontaneous emotional states on the IEMOCAP corpus and EMODB database respectively, and we achieve better classification for both speaker-independent and speaker-dependent experiment. It is noteworthy that we acquire 62.52% and 77.57% accuracy results with speaker-independent (SI) performance, 66.90% and 82.26% accuracy results with speaker-dependent (SD) experiment in final

Toward the Estimation of All-Weather Daytime Downward Longwave Radiation over the Tibetan Plateau

Downward longwave radiation (DLR) is a critical parameter for radiation balance, energy budget, and water cycle studies at regional and global scales. Accurate estimation of the all-weather DLR with a high temporal resolution is important for the estimation of the surface net radiation and evapotranspiration. However, most DLR products involve instantaneous DLR estimates based on polar orbiting satellite data under clear-sky conditions. To obtain an in-depth understanding of the performances of different models in the estimation of DLR over the Tibetan Plateau, which is a focus area of climate change study, this study tests eight methods for clear-sky conditions and six methods for cloudy conditions based on ground-measured data. It is found that the Dilley and O’Brien model and the Lhomme model are most suitable for clear-sky conditions and cloudy conditions, respectively. For the Dilley and O’Brien model, the average root mean square error (RMSE) of DLR under clear-sky conditions is approximately 22.5 W/m2 for nine ground sites; for the Lhomme model, the average RMSE is approximately 23.2 W/m2. Based on the estimated cloud fraction and meteorological data provided by the China Land Surface Data Assimilation System (CLDAS), hourly all-weather daytime DLR with a 0.0625° resolution over the Tibetan Plateau is estimated. Results demonstrate that the average RMSE of the estimated hourly all-weather DLR is approximately 26.4 W/m2. With the combined all-weather DLR model, the hourly all-weather daytime DLR dataset with a 0.0625° resolution from 2008 to 2016 over the Tibetan Plateau is generated. This dataset can contribute to studies associated with the radiation balance and energy budget, water cycle, and climate change over the Tibetan Plateau