6,646 research outputs found
Synthesis method of asymmetric gold particles.
Asymmetric particles can exhibit unique properties. However, reported synthesis methods for asymmetric particles hinder their application because these methods have a limited scale and lack the ability to afford particles of varied shapes. Herein, we report a novel synthetic method which has the potential to produce large quantities of asymmetric particles. Asymmetric rose-shaped gold particles were fabricated as a proof of concept experiment. First, silica nanoparticles (NPs) were bound to a hydrophobic micro-sized polymer containing 2-chlorotritylchloride linkers (2-CTC resin). Then, half-planar gold particles with rose-shaped and polyhedral structures were prepared on the silica particles on the 2-CTC resin. Particle size was controlled by the concentration of the gold source. The asymmetric particles were easily cleaved from the resin without aggregation. We confirmed that gold was grown on the silica NPs. This facile method for synthesizing asymmetric particles has great potential for materials science
Continuity of the Explosive Percolation Transition
The explosive percolation problem on the complete graph is investigated via
extensive numerical simulations. We obtain the cluster-size distribution at the
moment when the cluster size heterogeneity becomes maximum. The distribution is
found to be well described by the power-law form with the decay exponent , followed by a hump. We then use the finite-size scaling method to
make all the distributions at various system sizes up to collapse
perfectly onto a scaling curve characterized solely by the single exponent
. We also observe that the instant of that collapse converges to a
well-defined percolation threshold from below as . Based on
these observations, we show that the explosive percolation transition in the
model should be continuous, contrary to the widely-spread belief of its
discontinuity.Comment: Some corrections during the revie
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics
6.4 GHz Acoustic Sensor for In-situ Monitoring of AFM Tip Wear
This paper demonstrates an acoustic sensor that can resolve atomic force microscopy (AFM) tip blunting with a frequency sensitivity of 0.007%. The AFM tip is fabricated on a thin film piezoelectric aluminum nitride (AlN) membrane that is excited as a film bulk acoustic resonator (FBAR). We demonstrate that cutting 0.98 μm off of the tip apex results in a resonance frequency change of 0.4MHz at 6.387GHz. This work demonstrates the potential for in-situ monitoring of AFM tip wear
A Suspended Nanogap Formed by Field-Induced Atomically Sharp Tips
A sub-nanometer scale suspended gap (nanogap) defined by electric field-induced atomically sharp metallic tips is presented. A strong local electric field (\u3e109 V=m) across micro/nanomachined tips facing each other causes the metal ion migration in the form of dendrite-like growth at the cathode. The nanogap is fully isolated from the substrate eliminating growth mechanisms that involve substrate interactions. The proposed mechanism of ion transportation is verified using real-time imaging of the metal ion transportation using an in situ biasing in transmission electron microscope (TEM). The configuration of the micro/nanomachined suspended tips allows nanostructure growth of a wide variety of materials including metals, metal-oxides, and polymers. VC 2012 American Institute of Physics
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