Fundamenal Studies of Biorecognition Elements Sequestered Within Xerogels

干凝胶内生物识别元件的基础研究

• 批准号: 0078101
• 负责人: Frank Bright
• 金额: $ 37.5万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0078101&HistoricalAwards=false
• 关键词: Fundamenal; Studies; Biorecognition; Elements; Sequestered

This project, sponsored by the Analytical and Surface Chemistry Program, continues research on utilization of sol-gel derived xerogels for chemical analysis. The research, conducted by Dr. Frank V. Bright and his students at SUNY-Buffalo, focuses on the behavior and performance of enzymes, antibodies, and signaling proteins that are sequestered in xerogels. These protein doped xerogels are used as platforms for developing discrete and distributed biosensors. The experimental aproach first develops a fundamental understanding of how the analytical performance of xerogel entrapped proteins is influenced by the the choice of xerogel host matrix chemistry, co-dopants, and preparation protocol. The next step determines precisely how the xerogel host matrix chemistry, co-dopants, and preparation protocol affect 1) the biorecognition element's dynamics and 2) the environment around the sites within the biorecognition element that are responsible for producing fluorescence in the presence of the target analyte. Finally, the project uses results from the steps above to produce biosensors and distributed biosensor arrays that are more robust, stable, and reliable than those that are currently available. Use of the sensors developed by this project could result in faster, more reliable, and less costly clinical analyses.

该项目由分析和表面化学计划赞助，继续研究利用溶胶-凝胶衍生的干凝胶进行化学分析。 这项研究是由纽约州立大学布法罗分校的Frank V. Bright博士和他的学生进行的，重点是酶、抗体和信号蛋白在干凝胶中的行为和性能。 这些蛋白质掺杂的干凝胶被用作开发离散和分布式生物传感器的平台。 实验aproach首先发展的干凝胶截留蛋白质的分析性能是如何影响的干凝胶主机基质化学，共掺杂剂的选择，和制备协议的基本理解。 下一步精确地确定干凝胶主体基质化学、共掺杂剂和制备方案如何影响1）生物识别元件的动力学和2）生物识别元件内负责在目标分析物存在下产生荧光的位点周围的环境。 最后，该项目使用上述步骤的结果来生产生物传感器和分布式生物传感器阵列，这些传感器和阵列比目前可用的传感器和阵列更强大，更稳定，更可靠。 使用该项目开发的传感器可以实现更快，更可靠，成本更低的临床分析。