QuBIC: Electromagnetic and Informational Processes in Biomolecular Polymers

QuBIC：生物分子聚合物中的电磁和信息过程

• 批准号: 0218595
• 负责人: Dimitri Nanopoulos
• 金额: --
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0218595&HistoricalAwards=false
• 关键词: QuBIC; Electromagnetic; Informational; Processes; Biomolecular

EIA-0218595Dimitri V. NanopoulosTexas A&M Research FoundationElectromagnetic and informational processes in biomolecular polymersIn this project the electric dipole moment and quantum properties of tubulin, microtubules (MTs), microtubule-associated proteins (MAPs) and cytoskeletal networks immobilized on thin metal films utilizing surface plasmon resonance and femtosecond laser pulses are being studied. The goal is to understand the electrical and conjectured quantum properties of the cytoskeleton and determine whether biomolecular polymers and specifically tubulin can be used as bits or qubits in the protein-based devices such as logic gates, nanowires and memory registers in the biological and quantum computers of the future. Measurements of the electric dipole moment of tubulin and its dynamics in polymerized and free tubulin, investigate the formation and propagation of the predicted kink-solitonic electric dipole moment flip waves and monitor the polymerization and dynamics of MT-MAP networks under different conditions are being measured. In addition, this research is ideally suited to test MT-based 'quantum brain' and 'quantum neuron' models predicting such phenomena as large-scale neuronal quantum coherence and teleportation of dipole quanta.

EIA-0218595 Dimitri V. NanopoulosTexas A M Research Foundation生物分子聚合物中的电磁和信息过程在该项目中，利用表面等离子体共振和飞秒激光脉冲研究固定在薄金属膜上的微管蛋白、微管（MT）、微管相关蛋白（MAP）和细胞骨架网络的电偶极矩和量子特性。目标是了解细胞骨架的电学和量子特性，并确定生物分子聚合物，特别是微管蛋白是否可以用作基于蛋白质的设备中的位或量子位，例如未来生物和量子计算机中的逻辑门，纳米线和存储寄存器。测量微管蛋白的电偶极矩及其在聚合和自由微管蛋白中的动力学，研究预测的扭结-孤子电偶极矩翻转波的形成和传播，并监测不同条件下MT-MAP网络的聚合和动力学。此外，这项研究非常适合测试基于MT的“量子脑”和“量子神经元”模型，预测大规模神经元量子相干性和偶极量子隐形传态等现象。