Slice-selective NMR: a non-invasive method for the analysis of separated pyrolysis fuel samples

Robert Evans*, Aran Sandhu, Anthony V. Bridgwater, Katie J. Chong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Pyrolysis oil has been identified as a possible alternative fuel source, however widespread use is hindered by high acidity and water content. These negative characteristics can be mitigated by blending with, for example, biodiesel, marine gas oil and butanol. These blended samples can be unstable and often separate into two distinct phases. NMR spectroscopy is a well-established spectroscopic technique that is finding increasing application in the analysis of pyrolysis oil and blended fuels derived from it. Here, slice-selective NMR, where the NMR spectrum of only a thin slice of the total sample is acquired, is used to study, non-invasively, how the constituent components of blended biofuel samples are partitioned between the two layers. Understanding the outcome of the phase separation is an important step towards understanding why the blended oil samples separate, and may provide answers to mitigating and eventually solving the problem. The NMR method was successfully used to analyse a number of separated biofuel samples - typically separated into an oil layer, containing marine gas oil and biodiesel, above a bio-oil layer with a high water and butanol content.
Original languageEnglish
Pages (from-to)4135-4142
Number of pages8
JournalEnergy and Fuels
Issue number4
Early online date6 Mar 2017
Publication statusPublished - 20 Apr 2017

Bibliographical note

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Energy Fuels, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see


Dive into the research topics of 'Slice-selective NMR: a non-invasive method for the analysis of separated pyrolysis fuel samples'. Together they form a unique fingerprint.

Cite this