Thermal radiation can be substantially enhanced in the near-field scenario due to the tunneling of evanescent waves. Monolayer graphene could play a vital role in this process owing to its strong infrared plasmonic response, however, which still lacks an experimental verification due to the technical challenges. Here, we manage to make a direct measurement about plasmon-mediated thermal radiation between two macroscopic graphene sheets using a custom-made setup. Super-Planckian radiation with efficiency 4.5 times larger than the blackbody limit is observed at a 430-nm vacuum gap on insulating silicon hosting substrates. The positive role of graphene plasmons is further confirmed on conductive silicon substrates which have strong infrared loss and thermal emittance. Based on these, a thermophotovoltaic cell made of the graphene–silicon heterostructure is lastly discussed. The current work validates the classic thermodynamical theory in treating graphene and also paves a way to pursue the application of near-field thermal management.

Received XX Month XXXX; accepted XX Month XXXX; published online XX Month XXXX)We report a daytime passive radiative cooler using chemically fabricated porous anodic aluminum oxide (AAO) membranes. Effective medium theory (EMT) has been applied to analyzing the optical properties of the air-doped porous medium. The composite structure is specifically optimized so that it has a high absorbance (emittance) in the far-infrared atmospheric window and nearly no loss in the solar spectrum. The calculated emittance is well reproduced in the experiment by our AAO sample. The fabricated porous membrane shows a potential cooling power density of 64 W/m 2 at ambient (humidity = 75%) under direct sunlight irradiance (AM1.5). Experimentally, the sample is cooled by a 2.6 °C temperature reduction below the ambient air temperature in the sunlight. This performance shows little variance at night. The AAO approach proposed here may provide a promising way to produce low-cost and efficient radiative cooler in large scales for feasible energy conservation. Published by AIP Publishing. http://doi.org/10.1063/x.xxxxxxx

ZnO nanowire (NW) array was conformally coated with an ultrathin SiO(2) shell by a bioinspired layer-by-layer deposition in order to obtain ultraviolet (UV)-durable superhydrophobic property. Uniform SiO(2) shell was prepared on ZnO NW by alternative reactive deposition of polyethylenimine and silicic acid. Despite the highly curved morphology of ZnO NW array, the thickness of SiO(2) shell increased linearly with the number of deposition cycles, with a thickness increment being of approximately 4.17 nm per deposition cycle. The SiO(2) shell only had a slight influence on the superhydrophobic property of ZnO NW array after modification with a monolayer of octadecyltrimethoxysilane (OTS). However, it greatly improved the UV durability of the superhydrophobic property of ZnO NW array due to the confinement effect of insulating SiO(2) layer on the photogenerated electron-hole pairs in ZnO NW.

No abstract

Emotion recognition using electroencephalogram (EEG) signals has attracted significant research attention. However, it is difficult to improve the emotional recognition effect across subjects. In response to this difficulty, in this study, multiple features were extracted for the formation of high-dimensional features. Based on the high-dimensional features, an effective method for cross-subject emotion recognition was then developed, which integrated the significance test/sequential backward selection and the support vector machine (ST-SBSSVM). The effectiveness of the ST-SBSSVM was validated on a dataset for emotion analysis using physiological signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). With respect to high-dimensional features, the ST-SBSSVM average improved the accuracy of cross-subject emotion recognition by 12.4% on the DEAP and 26.5% on the SEED when compared with common emotion recognition methods. The recognition accuracy obtained using ST-SBSSVM was as high as that obtained using sequential backward selection (SBS) on the DEAP dataset. However, on the SEED dataset, the recognition accuracy increased by ~6% using ST-SBSSVM from that using the SBS. Using the ST-SBSSVM, ~97% (DEAP) and 91% (SEED) of the program runtime was eliminated when compared with the SBS. Compared with recent similar works, the method developed in this study for emotion recognition across all subjects was found to be effective, and its accuracy was 72% (DEAP) and 89% (SEED).

We report core@satellite Janus mesoporous silica‐Pt@Au (JMPA) nanomotors with pH‐responsive multi‐phoretic propulsion. The JMPA nanomotors first undergo self‐diffusiophoretic propulsion in 3.0 % H2O2 due to the isolation of the Au nanoparticles (AuNPs) from the PtNPs layer. Then the weak acidity of H2O2 can trigger the disassembly and reassembly of the AuNPs, resulting in the Janus distribution of large AuNPs aggregates. Such reconstruction of JMPA leads to the contact between PtNPs and AuNPs aggregates, thus changing the propulsion mechanism to self‐electrophoresis. The asymmetric and aggregated AuNPs also enable the generation of a thermal gradient under laser irradiation, which propels the JMPA nanomotors by self‐thermophoresis. Such multi‐phoretic propulsion offers considerable promise for developing advanced nanomachines with a stimuli‐responsive switch of propulsion modes in biomedical applications.

Measurement of coal carbon content using laser-induced breakdown spectroscopy (LIBS) is limited by its low precision and accuracy. A modified spectrum standardization method was proposed to achieve both reproducible and accurate results for the quantitative analysis of carbon content in coal using LIBS. The proposed method used the molecular emissions of diatomic carbon (C2) and cyanide (CN) to compensate for the diminution of atomic carbon emissions in high volatile content coal samples caused by matrix effect. The compensated carbon line intensities were further converted into an assumed standard state with standard plasma temperature, electron number density, and total number density of carbon, under which the carbon line intensity is proportional to its concentration in the coal samples. To obtain better compensation for fluctuations of total carbon number density, the segmental spectral area was used and an iterative algorithm was applied that is different from our previous spectrum standardization calculations. The modified spectrum standardization model was applied to the measurement of carbon content in 24 bituminous coal samples. The results demonstrate that the proposed method has superior performance over the generally applied normalization methods. The average relative standard deviation was 3.21%, the coefficient of determination was 0.90, the root mean square error of prediction was 2.24%, and the average maximum relative error for the modified model was 12.18%, showing an overall improvement over the corresponding values for the normalization with segmental spectrum area, 6.00%, 0.75, 3.77%, and 15.40%, respectively.

No abstract