Deconvolution Protocols for Melt Crystallization Peaks in Recyclate Blends
Deconvolution of DSC melt crystallization peaks isolates PE and PP fractions in recyclates to verify composition before compounding.
Fraser-Suzuki Model
Meaning
Mathematical deconvolution is applied within polymer characterization to resolve overlapping molecular weight distribution peaks derived from size exclusion chromatography data. Peak broadening, caused by axial dispersion inside chromatographic columns, distorts the true polymer dispersity. Resolution algorithms decompose these composite curves into distinct individual distribution components.
Deconvolution accuracy deteriorates when baseline separation fails or when low molecular weight fractions overlap excessively.
Peak Shape
Mathematical functions represent individual polymer fractions within the chromatogram. Gaussian distributions fail to account for the tailing behaviour observed in polymer molecular weight fractions. Asymmetrical mathematical functions resolve this difficulty by incorporating parameters that govern skewness.
Mathematical optimisation algorithms adjust these parameters until the calculated composite curve matches the experimental chromatogram.
Resin Fractionation
Molecular heterogeneity influences the mechanical performance of moulded thermoplastic components. Complex crystallisation kinetics and melt flow properties depend upon the proportions of low and high molecular weight chains present in the resin. Preparative fractionation separates physical samples, whereas mathematical deconvolution extracts equivalent compositional insights directly from chromatograms.
Moulders use these resolved fractions to predict how virgin and regrind material blending alters final part warpage.
Processing Limit
Uncontrolled variation in polymer molecular weight distribution alters the processing window during injection moulding. Melt viscosity shifts during production when the proportion of low molecular weight chains fluctuates beyond acceptable tolerances. Datasheet values represent average properties, which mask the underlying distribution details revealed by deconvolution.
Part shrinkage and dimensional instability emerge in moulded components when suppliers deliver batches with shifted fraction profiles.