Design Molecular Recognition Materials for Chiral Sensors, Separtations and Catalytic Materials
Author(s) -
Shaojin Jia,
Tina M. Nenoff,
Paula P. Provencio,
Yan Qiu,
John A. Shelnutt,
Steven G. Thoma,
J. Zhang
Publication year - 1998
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/2055
Subject(s) - molecular recognition , mesoporous material , selectivity , molecular model , catalysis , molecule , materials science , characterization (materials science) , nanotechnology , combinatorial chemistry , membrane , chemistry , organic chemistry , biochemistry
The goal is the development of materials that are highly sensitive and selective for chid chemicals and biochemical (such as insecticides, herbicides, proteins, and nerve agents) to be used as sensors, catalysts and separations membranes. Molecular modeling methods are being used to tailor chiral molecular recognition sites with high affinity and selectivity for specified agents. The work focuses on both silicate and non-silicate materials modified with chirally-pure fictional groups for the catalysis or separations of enantiomerically-pure molecules. Surfactant and quaternary amine templating is being used to synthesize porous frameworks, containing mesopores of 30 to 100 angstroms. Computer molecukw modeling methods are being used in the design of these materials, especially in the chid surface- modi~ing agents. Molecular modeling is also being used to predict the catalytic and separations selectivities of the modified mesoporous materials. The ability to design and synthesize tailored asymmetric molecular recognition sites for sensor coatings allows a broader range of chemicals to be sensed with the desired high sensitivity and selectivity. Initial experiments target the selective sensing of small molecule gases and non-toxic model neural compounds. Further efforts will address designing sensors that greatly extend the variety of resolvable chemical species and forming a predictive, model-based method for developing advanced sensors
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