GPC is used to easily and reliably analyze the molecular mass distribution of industrially produced polyacrylnitrile in this application note. Polyacrylnitrile (PAN) is an important component of many industrial products including heavy-duty fibres, synthetic ropes, flame resistant plastics, and the versatile impact resistant ABS plastics. PAN is also commonly used to produce fabric for clothing manufacture since its addition creates a material that is warming, soft and crease-resistant. This application demonstrates a separation based on molecular size using GPC with organic eluents, along with a simple sample preparation procedure. Since this GPC application uses DMF and a small amount of lithium bromide as mobile phase, it will work on a standard HPLC system without any modifications.
How feasible is it to integrate this Bayesian graph-theory methodology into existing commercial chromatography software workflows for semi-automated data curation in routine analytical laboratories?
Gabriel Vivó-Truyols outlines the benefits and challenges of combining graph theory with Bayesian statistics to automate peak assignment and alignment.
This week, Chromatography Online’s coverage spanned lipidomics-based early cancer detection, career paths in separation science, machine learning and uncertainty-driven approaches to LC method development, new EU wastewater rules for pharmaceutical micropollutants, automated Separation Quality Factor (SQF) evaluation in chromatography data systems, and solutions to GLP-1 peptide analysis challenges.
Gabriel Vivó-Truyols from the Spanish Council for Scientific Research, Madrid, Spain. discusses his work on using graph theory and Bayesian statistics for peak assignment and chromatographic alignment.
Gas chromatography–mass spectrometry (GC–MS) and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC–qTOF–MS) reveal metabolic shifts in tricuspid regurgitation.