Unsupervised cryo-EM data clustering through adaptively constrained K-means algorithm

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.Comment: 35 pages, 14 figure

Ultralarge Free-Standing Imine-Based Covalent Organic Framework Membranes Fabricated via Compression

Demand continues for processing methods to shape covalent organic frameworks (COFs) into macroscopic objects that are needed for their practical applications. Herein, a simple compression method to prepare large-scale, free-standing homogeneous and porous imine-based COF-membranes with dimensions in the centimeter range and excellent mechanical properties is reported. This method entails the compression of imine-based COF-aerogels, which undergo a morphological change from an elastic to plastic material. The COF-membranes fabricated upon compression show good performances for the separation of gas mixtures of industrial interest, N2/CO2 and CH4/CO2. It is believed that the new procedure paves the way to a broader range of COF-membranes

ADVANCED POROUS MATERIALS IN MIXED MATRIX MEMBRANES FOR CARBON DIOXIDE CAPTURE

Ph.DDOCTOR OF PHILOSOPH

Mixed Matrix Membranes for Natural Gas Upgrading: Current Status and Opportunities

10.1021/acs.iecr.7b04796INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH57124139-416

Mixed Matrix Membranes for Natural Gas Upgrading: Current Status and Opportunities

In the past few decades, natural gas has attracted worldwide attention as one of the most desired energy sources owing to its more efficient and cleaner combustion process compared to that of coal and crude oil. Due to the presence of impurities, raw natural gas needs to be upgraded to meet the pipeline specifications. Membrane-based separation is a promising alternative to conventional processes such as cryogenic distillation and pressure swing adsorption. Among the existing membranes for natural gas upgrading, polymeric membranes and inorganic membranes have been extensively explored, but each type has its own pros and cons. The development of mixed matrix membranes (MMMs) by incorporating organic/inorganic fillers into the polymer matrix provides a good strategy to combine the merits of each material and fabricate novel membranes with superior gas separation performance. In this review, we first discuss the recent advances in MMMs showing potentials in natural gas upgrading. Special attention is paid to a detailed evaluation on the polymer and filler choices for acidic gas removal. After that, we analyze factors that influence the membrane separation performance and summarize effective strategies reported in the open literature for the fabrication of high-performance MMMs. Finally, a perspective on future research directions in this field is presented

Isoreticular covalent organic frameworks for hydrocarbon uptake and separation: the important role of monomer planarity

10.1039/c7ce00344gCRYSTENGCOMM19334899-490

Mechanoassisted Synthesis of Sulfonated Covalent Organic Frameworks with High Intrinsic Proton Conductivity

10.1021/acsami.6b06189ACS APPLIED MATERIALS & INTERFACES82818505-1851

Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm - Fig 3

<p><b>2D class averages of GroEL using the traditional K-means (a), EQK-means (b) and ACK-means (c) in MRA approach from SPARX</b>. Class size is shown at the left bottom of each class average. ACK-means (b) is the best by having the most number of clear classes.</p

Hydrazone-based covalent organic frameworks for Lewis acid catalysis

10.1039/c8dt03005gDALTON TRANSACTIONS473913824-1382

Ultrathin mixed matrix membranes containing two-dimensional metal-organic framework nanosheets for efficient CO2/CH4 separation

10.1016/j.memsci.2017.06.011JOURNAL OF MEMBRANE SCIENCE539213-22