Neutral water splitting catalysis with a high FF triple junction polymer cell

This document is the Accepted Manuscript version of a Published Work that appeared in final form in CS catalysis, copyright © American Chemical Society, after peer review and technical editing by the publisher and may be found at http://dx.doi.org/10.1021/acscatal.6b01036We report a photovoltaics-electrochemical (PV-EC) assembly based on a compact and easily processable triple homojunction polymer cell with high fill factor (76%), optimized conversion efficiencies up to 8.7%, and enough potential for the energetically demanding water splitting reaction (V-oc = 2.1 V). A platinum-free cathode made of abundant materials is coupled to a ruthenium oxide on glassy carbon anode (GC-RuO2) to perform the reaction at optimum potential (Delta E = 1.70-1.78 V, overpotential = 470-550 mV). The GC-RuO2 anode contains a single monolayer of catalyst corresponding to a superficial concentration (Gamma) of 0.15 nmol cm(-2) and is highly active at pH 7. The PV-EC cell achieves solar to hydrogen conversion efficiencies (STH) ranging from 5.6 to 6.0%. As a result of the solar cell's high fill factor, the optimal photovoltaic response is found at 1.70 V, the minimum potential at which the electrodes used perform the water splitting reaction. This allows generating hydrogen at efficiencies that would be very similar (96%) to those obtained as if the system were to be operating at 1.23 V, the thermodynamic potential threshold for the water splitting reaction.Peer ReviewedPostprint (author's final draft

Video based object representation and classification using multiple covariance matrices

<div><p>Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.</p></div

Average accuracies of different methods on four datasets.

<p>Average accuracies of different methods on four datasets.</p

The general flow of our MCDL method.

<p>The general flow of our MCDL method.</p

Use of Ball Drop Casting and Surface Modification for the Development of Amine-Functionalized Silica Aerogel Globules for Dynamic and Efficient Direct Air Capture

Amine-functionalized silica aerogel globules (AFSAGs) were first synthesized via a simple ball drop casting method followed by amine grafting. The effect of grafting time on the structure and CO2 adsorption performance of the AFSAGs was investigated. The CO2 adsorption performance was comprehensively studied by breakthrough curves, adsorption capacity and rates, surface amine loading and density, amine efficiency, adsorption halftime, and cyclic stability. The results demonstrate that prolonging the grafting time does not lead to a significant increase in surface amine content owing to pore space blockage by superabundant amine groups. The CO2 adsorption performance shows obvious dependence on surface amine density, determined by both the surface amine content and specific surface area, and working temperature. AFSAGs with a grafting time of 24 h (AFSAG24) with a moderate surface amine density have optimal CO2 adsorption capacities, which are 1.78 and 2.14 mmol/g at 25 °C with dry and humid 400 ppm CO2, respectively. The amine efficiency of AFSAG24 with low CO2 concentrations, 0.38–0.63 with dry 400 ppm−1% CO2, is the highest among the reported amine-functionalized adsorbents. After estimation with different diffusion models, the CO2 adsorption process of AFSAG24 is governed by film diffusion and intraparticle diffusion. In the range of 1–4 mm, the ball size does not affect the CO2 adsorption capacity of AFSAG24 obviously. AFSAG24 offers significant advantages for practical direct air capture compared with its state-of-the-art counterparts, such as high dynamic adsorption capacity and amine efficiency, excellent stability, and outstanding adaptation to the environment

Variables used as inputs.

<p>Variables used as inputs.</p

Auto ICA regression model (AICA-SVR).

<p>Auto ICA regression model (AICA-SVR).</p

Macro averaging evaluation rating results for 30 test EM images from ISBI 2012 using the proposed approach with boundary amendment and the proposed approach without boundary amendment, as well as the Canny, Kirsch, LoG, Prewitt, Roberts Cross, and Sobel operators.

<p>Macro averaging evaluation rating results for 30 test EM images from ISBI 2012 using the proposed approach with boundary amendment and the proposed approach without boundary amendment, as well as the Canny, Kirsch, LoG, Prewitt, Roberts Cross, and Sobel operators.</p

Thirty test EM images for segmentation from ISIB 2012.

<p>Thirty test EM images for segmentation from ISIB 2012.</p

Segmentation results for neuronal structures using the Sobel operator.

<p>Segmentation results for neuronal structures using the Sobel operator.</p