Structure of surfactant and phospholipid monolayers at the air/water interface modeled from neutron reflectivity data

Richard A. Campbell, Yussif Saaka, Yanan Shao, Yuri Gerelli, Robert Cubitt, Ewa Nazaruk, Dorota Matyszewska, M. Jayne Lawrence

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)
394 Downloads (Pure)

Abstract

Specular neutron reflectometry is a powerful technique to resolve interfacial compositions and structures in soft matter. Surprisingly however, even after several decades, a universal modeling approach for the treatment of data of surfactant and phospholipid monolayers at the air/water interface has not yet been established. To address this shortcoming, first a systematic evaluation of the suitability of different models is presented. The result is a comprehensive validation of an optimum model, which is evidently much needed in the field, and which we recommend as a starting point for future data treatment. While its limitations are openly discussed, consequences of failing to take into account various key aspects are critically examined and the systematic errors quantified. On the basis of this physical framework, we go on to show for the first time that neutron reflectometry can be used to quantify directly in situ at the air/water interface the extent of acyl chain compaction of phospholipid monolayers with respect to their phase. The achieved precision of this novel quantification is ∼10%. These advances together enhance significantly the potential for exploitation in future studies data from a broad range of systems including those involving synthetic polymers, proteins, DNA, nanoparticles and drugs.
Original languageEnglish
Pages (from-to)98-108
JournalJOURNAL OF COLLOID AND INTERFACE SCIENCE
Volume531
Early online date7 Jul 2018
DOIs
Publication statusPublished - 1 Dec 2018

Fingerprint

Dive into the research topics of 'Structure of surfactant and phospholipid monolayers at the air/water interface modeled from neutron reflectivity data'. Together they form a unique fingerprint.

Cite this