284 research outputs found
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron
microscopic (EM) brain images are critical for the field of connectomics. An
important computation for reconstruction is the detection of neuronal
boundaries. Images acquired by serial section EM, a leading 3D EM technique,
are highly anisotropic, with inferior quality along the third dimension. For
such images, the 2D max-pooling convolutional network has set the standard for
performance at boundary detection. Here we achieve a substantial gain in
accuracy through three innovations. Following the trend towards deeper networks
for object recognition, we use a much deeper network than previously employed
for boundary detection. Second, we incorporate 3D as well as 2D filters, to
enable computations that use 3D context. Finally, we adopt a recursively
trained architecture in which a first network generates a preliminary boundary
map that is provided as input along with the original image to a second network
that generates a final boundary map. Backpropagation training is accelerated by
ZNN, a new implementation of 3D convolutional networks that uses multicore CPU
parallelism for speed. Our hybrid 2D-3D architecture could be more generally
applicable to other types of anisotropic 3D images, including video, and our
recursive framework for any image labeling problem
Synaptic Partner Assignment Using Attentional Voxel Association Networks
Connectomics aims to recover a complete set of synaptic connections within a
dataset imaged by volume electron microscopy. Many systems have been proposed
for locating synapses, and recent research has included a way to identify the
synaptic partners that communicate at a synaptic cleft. We re-frame the problem
of identifying synaptic partners as directly generating the mask of the
synaptic partners from a given cleft. We train a convolutional network to
perform this task. The network takes the local image context and a binary mask
representing a single cleft as input. It is trained to produce two binary
output masks: one which labels the voxels of the presynaptic partner within the
input image, and another similar labeling for the postsynaptic partner. The
cleft mask acts as an attentional gating signal for the network. We find that
an implementation of this approach performs well on a dataset of mouse
somatosensory cortex, and evaluate it as part of a combined system to predict
both clefts and connections
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
Neural circuits can be reconstructed from brain images acquired by serial
section electron microscopy. Image analysis has been performed by manual labor
for half a century, and efforts at automation date back almost as far.
Convolutional nets were first applied to neuronal boundary detection a dozen
years ago, and have now achieved impressive accuracy on clean images. Robust
handling of image defects is a major outstanding challenge. Convolutional nets
are also being employed for other tasks in neural circuit reconstruction:
finding synapses and identifying synaptic partners, extending or pruning
neuronal reconstructions, and aligning serial section images to create a 3D
image stack. Computational systems are being engineered to handle petavoxel
images of cubic millimeter brain volumes
Synergistic multi-doping effects on the Li7La3Zr2O12 solid electrolyte for fast lithium ion conduction.
Here, we investigate the doping effects on the lithium ion transport behavior in garnet Li7La3Zr2O12 (LLZO) from the combined experimental and theoretical approach. The concentration of Li ion vacancy generated by the inclusion of aliovalent dopants such as Al(3+) plays a key role in stabilizing the cubic LLZO. However, it is found that the site preference of Al in 24d position hinders the three dimensionally connected Li ion movement when heavily doped according to the structural refinement and the DFT calculations. In this report, we demonstrate that the multi-doping using additional Ta dopants into the Al-doped LLZO shifts the most energetically favorable sites of Al in the crystal structure from 24d to 96 h Li site, thereby providing more open space for Li ion transport. As a result of these synergistic effects, the multi-doped LLZO shows about three times higher ionic conductivity of 6.14 × 10(-4) S cm(-1) than that of the singly-doped LLZO with a much less efforts in stabilizing cubic phases in the synthetic condition
Real-time visualization of Zn metal plating/stripping in aqueous batteries with high areal capacities
Zinc aqueous batteries have attracted great attention due to the earth abundance and the low redox potential of Zn metal. Utilizing Zn metal as an anode, however, causes low coulombic efficiency stemming from a dendritic Zn plating and formation of byproducts such as hydrogen gas, solid zinc hydroxide and salt-related compounds. One effective way of mitigating the issues is to modify the solvation structure of the electrolyte to increase the energy barrier of the water molecules for hydrolysis and electrolysis. Nevertheless, Zn aqueous batteries still indiscriminately utilize several types of electrolytes without elucidating the correlation between electrolyte composition and the electrochemistry of Zn metal. Here, we use operando optical microscopy to visualize the microstructural evolution of Zn metal, which strongly affects the electrochemical reversibility. In ZnSO4 electrolyte, large Zn platelets grow and form loose agglomerates vulnerable to unexpected delamination from the electrodes. In Zn(OTf)(2) electrolyte, Zn platelets nucleate more homogeneously and grow smaller, which forms denser agglomerates enabling more stable cycling. We further reveal that the formation of a stable solidelectrolyte interphase layer holds the key to the excellent performance of acetonitrile-hybrid water-in-salt electrolytes. Our results show the necessity of designing proper electrolytes to develop long-life Zn aqueous batteries.
Utilizing Latent Multi-Redox Activity of p-Type Organic Cathode Materials toward High Energy Density Lithium-Organic Batteries
Organic electrode materials hold great potential due to their cost-efficiency, eco-friendliness, and possibly high theoretical capacity. Nevertheless, most organic cathode materials exhibit a trade-off relationship between the specific capacity and the voltage, failing to deliver high energy density. Herein, it is shown that the trade-off can be mitigated by utilizing the multi-redox capability of p-type electrodes, which can significantly increase the specific capacity within a high-voltage region. The molecular structure of 5,10-dihydro-5,10-dimethylphenazine is modified to yield a series of phenoxazine and phenothiazine derivatives with elevated redox potentials by substitutions. Subsequently, the feasibility of the multi-redox capability is scrutinized for these high-voltage p-type organic cathodes, achieving one of the highest energy densities. It is revealed that the seemingly impractical second redox reaction is indeed dependent on the choice of the electrolyte and can be reversibly realized by tailoring the donor number and the salt concentration of the electrolyte, which places the voltage of the multi-redox reaction within the electrochemical stability window. The results demonstrate that high-energy-density organic cathodes can be practically achieved by rational design of multi-redox p-type organic electrode materials and the compatibility consideration of the electrolyte, opening up a new avenue toward advanced organic rechargeable batteries.
Nb-doped TiO2 air-electrode for advanced Li-air batteries
As new substrate materials to replace a conventional carbon substrate, TiO2 and Nb-doped TiO2 air-electrodes for Li-air batteries were investigated. Through a simple two-step process, we successfully synthesized anatase Nb-doped TiO2 nanoparticles and demonstrated the potential applicability of TiO2-based materials for use in Li-air battery electrode. An air-electrode with Nb-doped TiO2 nanoparticles could deliver a higher discharge capacity than a bare TiO2 electrode due to the enhanced conductivity, which implies the importance of facile electron transport during the discharge process. © 2014 The Ceramic Society of Japan and the Korean Ceramic Society.
Bio-inspired Molecular Redesign of a Multi-redox Catholyte for High-Energy Non-aqueous Organic Redox Flow Batteries
Redox-active organic materials (ROMs) have recently attracted significant attention for redox flow batteries (RFBs) to achieve green and cost-efficient energy storage. In particular, multi-redox ROMs have shown great promise, and further tailoring of these ROMs would yield RFB technologies with the highest possible energy density. Here, we present a phenazine-based catholyte material, 5,10-bis(2-methoxyethyl)-5,10-dihydrophenazine (BMEPZ), that undergoes two single-electron redox reactions at high redox potentials (-0.29 and 0.50 V versus Fc/Fc(+)) with enhanced solubility (0.5 M in acetonitrile), remarkable chemical stability, and fast kinetics. Moreover, an all-organic flow battery exhibits cell voltages of 1.2 and 2.0 V when coupled with 9-fluorenone (FL) as an anolyte. It shows capacity retention of 99.94% per cycle over 200 cycles and 99.3% per cycle with 0.1 M and 0.4 M BMEPZ catholyte, respectively. Notably, the BMEPZ/FL couple results in the highest energy density (similar to 17 Wh L-1) among the non-aqueous all- organic RFBs reported to date
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