Ultivo Triple Quad LC/MS with the 1260 Infinity II Prime LC, and Captiva Enhanced Matrix Removal—Lipid make it to the top 15 innovations
Agilent Technologies’ Ultivo Triple Quadrupole LC/MS system together with the Agilent 1260 Infinity II Prime LC and Agilent Captiva Enhanced Matrix Removal—Lipid technology have made it to The Analytical Scientist’s list of this year’s top 15 innovations. The awards highlight innovations delivered by transformative technology. The winners were chosen by a panel of experts, including members of the magazine’s editorial advisory board and editorial staff.
“Now in year five, the Innovation Awards continue to shine a light on the instruments and technologies that are having a big impact across the analytical sciences,” said Charlotte Barker, the editor of the magazine. “We had a record number of entries this year, and the final 15 reflect the full spectrum of analytical advances.”
“At Agilent, we continually strive to help laboratory scientists be more successful by delivering innovation that matters in the form of leading-edge products across a variety of uses,” said Monty Benefiel, Agilent vice president and general manager of Agilent´s Mass Spectrometry Division.
The Agilent Ultivo Triple Quadrupole LC/MS marks the next generation of LC/TQ instruments. It offers performance equivalent to or better than larger LC/TQ systems, with the added benefits of being smaller, easier to use and costing less to operate. Laboratories benefit from its space-saving and stackable design, providing optimal use of lab bench space, plus early maintenance feedback, enhanced serviceability, intuitive operational design, and robust/reliable performance in difficult matrices.
The 1260 Infinity II Prime LC completes the system, offering the highest sample capacity per bench space for any laboratory. Agilent’s Captiva EMR—Lipid pass-through SPE format simplifies workflows and reduces sample preparation steps. With cleaner samples (removing >99 per cent of phospholipids), the method sensitivity and analyte recovery is improved, which results in faster data analysis, better reproducibility and higher data confidence. By avoiding the introduction of a heavy-laden matrix into the system, unscheduled downtime is reduced.
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