Investigating Lipid Bilayer Structures 

with Neutron Reflectometry

By: Ryan Puthumana

Lab: NIST Center for Neutron Research (NCNR)

Mentors: David Hoogerheide and Frank Heinrich
● Introduction

○ What is Neutron Reflectometry?

● Project Workflow

● Results

● Conclusion/Future Directions

Presentation Contents
○ Neutrons are reflected off a sample of 

interest 

○ Measure intensity of neutron reflection 

relative to incident intensity (reflectivity)

○ Measure R (reflectivity) against Q 

(momentum transferred to neutron from 

surface).

○ Can be used to determine structure of 

lipid bilayers and membrane proteins.

Introduction: What is Neutron Reflectometry?

Eells, R., et al. "3. Structural investigations of membrane-associated proteins by neutron reflectometry". Characterization of Biological Membranes, 2019, pp. 87-130. 
● nSLD encodes the information about the 

structure of the interface and determines 

R(Q)

○ Each isotope has a unique 

scattering length

○ nSLD is material property 

dependent on the elemental 

composition and density

Introduction: Scattering Length Density

Eells, R., et al. "3. Structural investigations of membrane-associated proteins by neutron reflectometry". Characterization of Biological Membranes, 2019, pp. 87-130. 
● Usually uses solid substrate to support 

membrane 

■ Directly adsorb lipid bilayer 

to silicon wafer (ssBLM).

■ Or attach lipid bilayer to gold 

with tether molecules 

(tBLM).

Introduction: Neutron Reflectometry Experiment

Tethered bilayer lipid membrane (tBLM)
● Shows how constituent chemical 

groups of lipid bilayer are arranged in 

space.

○ Any empty space filled with 

water (D2O or H2O)

○ Used to calculate nSLD profiles 

for optimization of R(Q)

Introduction: Composition-Space Model
Composition

Space

Model (plugin)

nSLD(z) Rcalc(Q)

Adjust

parameters

Molgroups

Refl1D

Compare to

R(Q)

Jupyter notebook (developed over summer)

Initiate fit

Collect results

Project Workflow

Optimized model that can be plotted 

(reflectivity, profile, area plots)

1

2
Results: Jupyter Notebook Output (ssBLM) 

● Successfully transcribed C++ code into Python for molgroups module. 

● Additionally, created a concise shareable Jupyter notebook that is user-friendly and made 

for the purpose of plotting and fitting real-life reflectometry data (output below).

Fig. 1: Reflectivity vs. Q, solid-supported bilayer Fig. 2: Profile Data of a Lipid Bilayer (z = distance)
Results: Composition Space Model (ssBLM)

Fig. 3: Area composition space-model of lipid bilayer.
Results (tBLM)
Results: Final Jupyter Notebook

● 3 major sections in final Jupyter notebook:

○ 1) Set up the composition space model definition

○ 2) Run refl1d fit (need a previously existing run file). Creates a best fit of data

○ 3) Plot the graphs of the resulting refl1d fit (3 graphs output: reflectivity, 

profile, and space-composition graphs)
Conclusion and Future Directions

● Conclusion

○ NCNR now has a more userfriendly interface for processing 

neutron reflectometry data. 

○ Future steps could include 

producing models for proteins.

○ Also, the code could be further 

streamlined and sped up.
Mentors: David Hoogerheide and Frank Heinrich

Special Thanks to NCNR team and directors Joseph Dura, Julie Borchers, Susana Teixeira

Other acknowledgements to Alyssa Thomas for technical assistance on this project.

Funding courtesy of CHRNS through National Science Foundation. 

Acknowledgements
Questions?