"Steric forces related to the lipopolysaccharide (LPS) brush on bacterial surfaces is of great importance in biofilm research. However, the atomic force microscopy (AFM) data, or force curves, produced require extensive analysis to obtain any useful information about the sample. Normally, after several force curves have been measured, the individual curves would be fit to a model for analysis. This process is not only time-consuming, but it is also extremely subjective as it lends itself to user bias throughout the analysis. A Matlab program to analyze force curves from an AFM efficiently, accurately, and with minimal user bias has been developed and is presented here. The analysis is based on a modified version of the Alexander and de Gennes (AdG) polymer model, which is a function of equilibrium polymer brush length, probe radius, temperature, separation distance, and a density variable. The program runs efficiently by cropping curves to the region specified by the model and then fitting the data. Automating the procedure reduces the amount of time required to process 100 force curves from several days to less than two minutes. Accuracy is ensured by making the program highly adjustable. The user can specify experimental constants such as the temperature and cantilever tip geometry, as well as adjust many cropping and fitting parameters to better analyze the data. Additionally, as part of this program, researchers can compare data from related experiments by choosing to plot the calculated fit parameters using either error bars or box plots to quickly identify relationships or trends. The use of this program to crop and fit force curves to the AdG model will allow researchers to ensure proper processing of large amounts of experimental data and reduce the time required for analysis and comparison of data, thereby enabling higher quality results in a shorter period of time. "
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-theses-2026 |
| Date | 07 September 2014 |
| Creators | O'Connor, Samantha |
| Contributors | Qi Wen, Reader, Nancy A. Burnham, Advisor, , Terri Camesano |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses (All Theses, All Years) |
Page generated in 0.0017 seconds