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Authors: Zhang, Xin; Co-Author: 2006 (Advances in chromatography have led to two recent active areas of study: the reduction of particle size for column packing and development of monolithic materials. Reducing the size of the packing material leads to an increase in chromatographic efficiency and a decrease in the analysis time. The monolithic column is composed of one single piece of material with through-pores that offers very high permeability, thereby, allowing the column can offer a variable external porosity and operation at high linear velocity with low pressure requirements. The performance of commercially available columns, a monolith and a sub-2 gm particle packed column was examined. Higher efficiencies were obtained with the sub-2 gm particle packed column. Very high pressures 14,000 psi were required to operated the sub-2 gm packed column to obtain a linear velocity of 6.4 mm/sec, while a pressure of 1,600 psi was required to achieve a linear velocity of 12 mm/sec with the monolithic column. Examination by the van Deemter model shows that the monolithic column behaves like a 3 gm packed column. A new approach to synthesize monolithic columns for capillary LC and capillary electrochromatography (CEC) was also examined. A silica based hybrid monolithic material was synthesized and characterized. The material fabricated inside capillary columns consisted of an allyl-functionalized monolith, which was prepared in a one-pot reaction. The material was characterized by SEM, nitrogen absorption (BET method), and chromatographically by CEC and CLC. The chromatographically tested column with best performance had a surface area of about 100 m 2/g with through pores 7 gm and 6 nm mesopores. A stationary phase for CEC was synthesized by polymerizing n¬isopropylacrylamide on aminated silica particles. The poly-n-isopropylacrylamide was characterized by IR spectroscopy and thermogravimetric analysis. Both IR spectroscopy and thermogravimetric thermograms showed the grafting of the poly-n¬isopropylacrylamide on the aminated silica. Tested under CEC conditions, using a series of neutral and acidic compounds, the stationary phase showed a mixed mode separation mechanism with both hydrophobicity and hydrophilicity contributing to the separation.)
Authors: Zhang, Xin; Co-Author: 2006 (Events occur in every aspect of our lives. An unexpectedly large number of events occurring within some certain measurement (e.g. within some time du-ration or a spatial region) is called a burst, suggesting unusual behaviors or activities. Bursts come up in many natural and social processes. It is a challenging task to monitor the occurrence of bursts whose lasting duration is unknown in a fast data stream environment. This work describes efficient data structures and algorithms for high performance burst detection under different settings. Our view is that bursts, as unusual phenomena, constitute a useful preliminary primitive in a knowledge discovery hierarchy. Our intent is to build a high performance primitive detection algorithm to support high-level data mining tasks. The work starts with an algorithmic framework including a family of data structures and a heuristic optimization algorithm to choose an efficient data structure given the inputs. The advantage of this framework is that it is adaptive to different inputs. Experiments on both synthetic data and real world data show the new framework significantly outperforms existing techniques over a variety of inputs. Furthermore, we present a greedy dynamic detection algorithm which handles the changing data. It evolves the structure to adapt to the incoming data. It achieves better performance on both synthetic and real data streams than a static algorithm in most cases. We have applied this framework to several real world applications in physics, stock trading and website traffic monitoring. All the case studies show that our framework has real time response. We extend this framework to multi-dimensional data and use it in an epidemiology simulation to detect infectious disease outbreak and spread.)