Improving polarized neutron reflectometry experiments on soft-matter samples: optimization of the solid substrate structure.

In this study we use Fisher information within the software HOGBEN to optimize the structure of magnetic reference layers to maximize the information gained from polarized neutron reflectometry (PNR) measurements on soft-matter and biological samples. Our approach is based on simulating the reflectivity from an experimentally determined dataset of a supported lipid bilayer. A set of reflectivity curves is calculated by varying parameters associated with the substrate assembly containing a magnetic reference layer. For each of the calculated reflectivity curves, we quantified the sensitivity of the simulated data to the sample structure. Our Fisher information analysis predicts that decreasing the thickness of the capping layer combined with increasing the magnetic scattering length density of the magnetic reference layer will reduce measurement time of similar PNR experiments by up to a factor of 5.6 or, equivalently, reduce parameter uncertainties at fixed beam time by the same factor relative to the reference experiment. Within a practically realizable SiO2 capping layer window of 200-400 Å, the predicted gain remains substantial (G ≈ 2.7). Because the parameters determining reflectivity curves are often correlated, such information is difficult to obtain in any other way. Experimental validation of these predictions on substrate assemblies synthesized according to the proposed designs is the natural next step. We established a theoretical method that can also be implemented within the context of other neutron reflectometry experiment. The approach allows the optimization of PNR experiments and will enable more straightforward and robust extraction of relevant parameters as well as more efficient use of neutron beam time.