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自动喷针定位优化纳喷雾关键参数

发布时间: 2015-05-05 16:25 来源: New Objective China
样品:蛋白质项目:蛋白质质谱分析

下载地址: 自动喷针定位优化纳喷雾关键参数


  Establishing optimal spray conditions for nanoLC-MS is a key part of method development. Nano-ESI is dependent on a number of factors, including the effective voltage, specific dimensions of a given spray tip, effective flow rate, and mobile phase composition. All of these parameters can be tightly controlled through instrumentation software and commercially available nano-ESI emitters. The position of the emitter relative to the MS orifice plays an important role in spray optimization but is a highly subjective variable lacking in the control enabled by LC and MS instrument vendors for flow rate, applied voltage, and gas parameters. The implementation of a feedback-controlled nano-electrospray source where either the spray voltage or nanospray emitter position is under feedback control has been previously reported1. The utility of a digital-control system to map the spray current of the ESI plume under a variety of experimental conditions has been demonstrated on an LCQ DECA mass spectrometer2. Here we investigate the utility of a digital-control system for spray optimization on a 4000 Q TRAP instrument.

  The effects of a laminar flow of heated nitrogen gas from the inlet and a coaxial sheath gas on spray stability and analyte signal were evaluated using commercially available peptides via syringe infusion at 300 nL/min. flow rates. Proprietary software controlling an automated XYZ translation stage was modified with a custom program written to initiate an MS acquisition. Each MS acquisition was triggered by movement to a defined set of XYZ coordinates. The custom program was used to generate a raster scan pattern of the nanospray emitter XYZ coordinates relative to the MS orifice, enabling the ability to correlate analyte signal with emitter position for a specific set of nano-ESI parameters. Using this information, a parameter and position dependent map can be generated and used for reproducible and robust nano-ESI, minimizing the need for manual optimization.


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