Dongil Lee | Searcharxiv

arXiv2019

Ultrafast Electron Dynamics in Thiolate-Protected Plasmonic Gold Clusters: Size and Ligand Effect

The influence of passivating ligand on electron-phonon relaxation dynamics of the smallest sized gold clusters was studied using ultrafast transient absorption spectroscopy and theoretical modeling. The electron dynamics in Au279, Au329, and Au329 passivated with TBBT, SC2Ph and SC6, respectively, were investigated. Ultrafast transient absorption measurements were also carried out on Au~1400 (SC6) and Au~2000 (SC6) to understand the influence of the size on electron-phonon relaxation with the same passivating ligand. The study has revealed interesting aspects on the role of ligand on electron-phonon relaxation dynamics wherein the aromatic passivating ligands, SC2Ph and TBBT, have shown smaller power dependence and higher plasmon bleach indicating dampened plasmon resonance while the cluster with aliphatic passivating ligand has behaved similarly to regular plasmonic gold nanoparticles. To model the effect of the ligand on the plasmonic properties of the investigated samples , free electron density correction factor of each one was calculated using three-layered Mie theory, and the results show that SC6 inter-acts least with core-gold while TBBT and SC2Ph have a greater effect on the surface electronic conductivity that is attributed to pi-interaction of the ligand with gold. The results also shed light on unusual electron-phonon relaxation and smaller slope observed for Au329 (SC2Ph) that is ascribed to surface gold-pi interaction creating a hybrid state. In contrast, extended pi-interaction is probably the reason for plasmonic nature observed in Au279 (TBBT) even though its size is smaller when compared to Au329. In addition, the results also have shown that the electron-phonon coupling has increased with an increase in the size of the cluster and theoretical modeling has shown higher electron conductivity for larger plasmonic gold clusters.

cond-mat.mes-hall↗

arXiv2016

RhoanaNet Pipeline: Dense Automatic Neural Annotation

Reconstructing a synaptic wiring diagram, or connectome, from electron microscopy (EM) images of brain tissue currently requires many hours of manual annotation or proofreading (Kasthuri and Lichtman, 2010; Lichtman and Sanes, 2008; Seung, 2009). The desire to reconstruct ever larger and more complex networks has pushed the collection of ever larger EM datasets. A cubic millimeter of raw imaging data would take up 1 PB of storage and present an annotation project that would be impractical without relying heavily on automatic segmentation methods. The RhoanaNet image processing pipeline was developed to automatically segment large volumes of EM data and ease the burden of manual proofreading and annotation. Based on (Kaynig et al., 2015), we updated every stage of the software pipeline to provide better throughput performance and higher quality segmentation results. We used state of the art deep learning techniques to generate improved membrane probability maps, and Gala (Nunez-Iglesias et al., 2014) was used to agglomerate 2D segments into 3D objects. We applied the RhoanaNet pipeline to four densely annotated EM datasets, two from mouse cortex, one from cerebellum and one from mouse lateral geniculate nucleus (LGN). All training and test data is made available for benchmark comparisons. The best segmentation results obtained gave $V^\text{Info}_\text{F-score}$ scores of 0.9054 and 09182 for the cortex datasets, 0.9438 for LGN, and 0.9150 for Cerebellum. The RhoanaNet pipeline is open source software. All source code, training data, test data, and annotations for all four benchmark datasets are available at www.rhoana.org.

q-bio.NC↗