Hydrogen production by sorption-enhanced steam reforming of glycerol

Catalytic steam reforming of glycerol for H(2) production has been evaluated experimentally in a continuous flow fixed-bed reactor. The experiments were carried out under atmospheric pressure within a temperature range of 400-700 degrees C. A commercial Ni-based catalyst and a dolomite sorbent were used for the steam reforming reactions and in situ CO(2) removal. The product gases were measured by on-line gas analysers. The results show that H(2) productivity is greatly increased with increasing temperature and the formation of methane by-product becomes negligible above 500 degrees C. The results suggest an optimal temperature of approximately 500 degrees C for the glycerol steam reforming with in situ CO(2) removal using calcined dolomite as the sorbent, at which the CO(2) breakthrough time is longest and the H(2) purity is highest. The shrinking core model and the 1D-diffusion model describe well the CO(2) removal under the conditions of this work

Importance of electronic self-consistency in the TDDFT based treatment of nonadiabatic molecular dynamics

A mixed quantum-classical approach to simulate the coupled dynamics of electrons and nuclei in nanoscale molecular systems is presented. The method relies on a second order expansion of the Lagrangian in time-dependent density functional theory (TDDFT) around a suitable reference density. We show that the inclusion of the second order term renders the method a self-consistent scheme and improves the calculated optical spectra of molecules by a proper treatment of the coupled response. In the application to ion-fullerene collisions, the inclusion of self-consistency is found to be crucial for a correct description of the charge transfer between projectile and target. For a model of the photoreceptor in retinal proteins, nonadiabatic molecular dynamics simulations are performed and reveal problems of TDDFT in the prediction of intra-molecular charge transfer excitations.Comment: 9 pages, 8 figures. Minor changes in content wrt older versio

Transport critical current of Solenoidal MgB2/Cu Coils Fabricated Using a Wind-Reaction In-situ Technique

In this letter, we report the results of transport Jc of solenoid coils upto 100 turns fabricated with Cu-sheathed MgB2 wires using a wind-reaction in-situ technique. Despite the low density of single core and some reaction between Mg and Cu-sheath, our results demonstrate the decrease in transport Jc with increasing length of MgB2 wires is insignificant. Solenoid coils with diameter as small as 10 mm can be readily fabricated using a wind-reaction in-situ technique. The Jc of coils is essentially the same as in the form of straight wires. A Jc of 133,000 A/cm2 and 125,000 A/cm2 at 4 K and self field has been achieved for a small coil wound using Cu-sheathed tape and Cu-sheathed wire respectively. These results indicate that the MgB2 wires have a great potential for lage scale applicationsComment: 6 pages, 4 figures, 1 tabl

Temperature dependence of electron-spin relaxation in a single InAs quantum dot at zero applied magnetic field

The temperature-dependent electron spin relaxation of positively charged excitons in a single InAs quantum dot (QD) was measured by time-resolved photoluminescence spectroscopy at zero applied magnetic fields. The experimental results show that the electron-spin relaxation is clearly divided into two different temperature regimes: (i) T < 50 K, spin relaxation depends on the dynamical nuclear spin polarization (DNSP) and is approximately temperature-independent, as predicted by Merkulov et al. (ii) T > about 50 K, spin relaxation speeds up with increasing temperature. A model of two LO phonon scattering process coupled with hyperfine interaction is proposed to account for the accelerated electron spin relaxation at higher temperatures.Comment: 10 pages, 4 figure

Unpaired multi-modal segmentation via knowledge distillation

Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics. To effectively train such a highly compact model, we introduce a novel loss term inspired by knowledge distillation, by explicitly constraining the KL-divergence of our derived prediction distributions between modalities. We have extensively validated our approach on two multi-class segmentation problems: i) cardiac structure segmentation, and ii) abdominal organ segmentation. Different network settings, i.e., 2D dilated network and 3D U-net, are utilized to investigate our method's general efficacy. Experimental results on both tasks demonstrate that our novel multi-modal learning scheme consistently outperforms single-modal training and previous multi-modal approaches