journal article·2008-06Open access

Neutron reflectometry to investigate the delivery of lipids and DNA to interfaces (Review)

Tommy Nylander, Richard A. Campbell, Pauline Vandoolaeghe, Marité Cárdenas, Per Linse, Adrian R. Rennie

Biointerphases· Volume 3· Issue 2

Abstract

The application of scattering methods in the study of biological and biomedical problems is a field of research that is currently experiencing fast growth. In particular, neutron reflectometry (NR) is a technique that is becoming progressively more widespread, as indicated by the current commissioning of several new reflectometers worldwide. NR is valuable for the characterization of biomolecules at interfaces due to its capability to provide quantitative structural and compositional information on relevant molecular length scales. Recent years have seen an increasing number of applications of NR to problems related to drug and gene delivery. We start our review by summarizing the experimental methodology of the technique with reference to the description of biological liquid interfaces. Various methods for the interpretation of data are then discussed, including a new approach based on the lattice mean-field theory to help characterize stimulus-responsive surfaces relevant to drug delivery function. Recent progress in the subject area is reviewed in terms of NR studies relevant to the delivery of lipids and DNA to surfaces. Lastly, we discuss two case studies to exemplify practical features of NR that are exploited in combination with complementary techniques. The first case concerns the interactions of lipid-based cubic phase nanoparticles with model membranes (a drug delivery application), and the second case concerns DNA compaction at surfaces and in the bulk solution (a gene delivery application).
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Lipid Membrane Structure and Behavior · Force Microscopy Techniques and Applications · Nanopore and Nanochannel Transport Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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