Used APPLIED BIOSYSTEMS / ABI / MDS SCIEX 4500 #293620202 for sale

APPLIED BIOSYSTEMS / ABI / MDS SCIEX 4500 is an advanced triple quadrupole mass spectrometry equipment designed to provide powerful, precise, and sensitive quantitation for highly specific analytes. It features a compact, automated platform that can be tailored to a variety of sample preparation protocols. This system offers a range of accessories for sample preparation, such as ultrasonic homogenizers, pneumatic sample injectors, and refrigerated sample traps. ABI 4500 features an optimized triple quadrupole design that provides an unsurpassed level of sensitivity and selectivity. The unit demonstrates an impressive 1/1000,000th sensitivity for performing a wide range of quantitative assays. It also offers high-speed scan rates, with a maximum of 1000 scans per second, for maximum throughput and efficiency. This machine also allows the user to choose between two types of ion source systems: the 13-ended ABI Autosampler ion source, or the 10-ended Selex TOF ion source. By affixing different probes, the user can perform a range of analyses including universal, selective, and polarized fragmentation. This can be used to identify and quantify molecules with high accuracy and reproducibility. Another useful feature of MDS SCIEX 4500 is its customizable user interface. This provides the user with support and flexibility when setting up both small and large scale workflows. In addition, the tool includes a range of software capabilities for data analysis and reporting. These include comprehensive library searching, integration of chromatographic and spectrometric information, and the ability to analyze and compare data from multiple locations. 4500 is fast becoming a valuable tool to pharmaceutical and biotechnology organizations, as it provides an unprecedented level of performance and flexibility. As such, this asset has become a staple in the laboratory setting, offering researchers the precision and accuracy they need to accurately quantify, identify and characterize their data.