Quantification and Visualization of Single Macromolecules with AFM Software

Commercial AFMs often come equipped with software that is focused on the collection of data but off-line data treatment capabilities and quantitative examination are typically limited.

Therefore, it is important that AFM users work with specialized software packages, for example those based on Digital Surf’s Mountains® platform. Dr Sergei Magonov, member of SPM Labs LLC and experienced AFM user, has worked with Mountains® for a number of years and in the following article demonstrates the value of Mountains® and presents a few examples of AFM image processing and analysis taken from studies of single macromolecules and heterogenous materials.

Visualization and Quantification of Single Macromolecules

Researchers have been fascinated by high-resolution profiling of nanoscale objects with an AFM probe since the early 90s when images of single DNA strands and their double helix structure were first observed. Synthetic polymer chains have also been regularly studied since 1996.

Being able to directly visualize the architecture of synthesized polymer chains deposited on an atomically-flat substrate from their dilute solutions is beneficial for the chemists who create these macromolecules. However, in order for this to be possible the data needs to be prepared using software like Mountains®.

Preparing AFM Data for Analysis

Often it is necessary to subject the raw height images of single macromolecules to processing. Examples include leveling with the exclusion of raised structures and form removal which eliminates occasional sample tilt and tube-scanner bulging. Mountains® software performs these common procedures in a very time-efficient and user-friendly way.

The software offers a wide choice of color palettes which are user-defined. The resulting images are well suited to quantitative analysis and provide statistics including macromolecule length (an important characteristic of polymers, related to molecular weight distribution).

It is also important to examine the chain conformation and changes caused by different factors: environment, temperature, etc.

The height image below illustrates polymer macromolecules absorbed on a mica surface. These images can be further analyzed using the Motifs analysis tool for example.

AFM height & phase images of brush macro-molecule. Top right?: sketch of the brush macromolecule in “spoke-wheel” configuration.

AFM height & phase images of brush macro-molecule. Top right : sketch of the brush macromolecule in “spoke-wheel” configuration.

1. Characterizing Membrane Morphology

Membranes are important functional components for various applications ranging from batteries to biochemistry. Pore distribution, morphology and size of pores are valuable characteristics for defining the overall performance of a membrane.

Figure A below shows the surface morphology of a Celgard microporous polymer film containing multiple nano-sized voids originated in fibrillar regions and separated by densely packed lamellar regions.

The Mountains® Slices tool (Figure B) can be used to quantify morphology and provides volumes, projected areas, and mean thickness of the voids and surrounding material. The procedure is user-controlled and has a choice of one or two color-coded threshold levels separating features of interest.

Figures C & D show a similar analysis routine applied to an industrial nitrocellulose membrane and has features much larger than those of the Celgard film. The pore size varies from tens of nanometers to several microns.

2. Examination of Bitumen Composition

Bitumen is widely used as a roofing material and for road pavements. The technological properties of the material depend upon its composition and morphology and this can be examined with AFM phase imaging and mapping of local dielectric response.

Bee-like structures resulting from surface stress during cooling from high temperature can typically be seen in height images of bitumen surface regions. As the profiles are corrugated, the leveling of these images is facilitated by automatically excluding features above and below the average level (Figures A & B below).

Their heterogeneous morphology and different domains can only faintly be seen here. However, phase images are more sensitive not only to topographical features but also to differences in mechanical and adhesive local properties (figure C). Here, the color-coded contrast differentiates bee-like structures and two kinds of surrounding surface domains.

Slices analysis was then applied to both height and phase images to qualify the composition. Comparing these allows for the identification of the bee-like features with surrounding domains as wax and the other regions as polar asphaltene material, which are two of the multiple chemical constituents of bitumen.

This information has been sourced, reviewed and adapted from materials provided by Digital Surf.

For more information on this source, please visit Digital Surf.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Digital Surf. (2023, March 30). Quantification and Visualization of Single Macromolecules with AFM Software. AZoNano. Retrieved on October 13, 2024 from https://www.azonano.com/article.aspx?ArticleID=5080.

  • MLA

    Digital Surf. "Quantification and Visualization of Single Macromolecules with AFM Software". AZoNano. 13 October 2024. <https://www.azonano.com/article.aspx?ArticleID=5080>.

  • Chicago

    Digital Surf. "Quantification and Visualization of Single Macromolecules with AFM Software". AZoNano. https://www.azonano.com/article.aspx?ArticleID=5080. (accessed October 13, 2024).

  • Harvard

    Digital Surf. 2023. Quantification and Visualization of Single Macromolecules with AFM Software. AZoNano, viewed 13 October 2024, https://www.azonano.com/article.aspx?ArticleID=5080.

Ask A Question

Do you have a question you'd like to ask regarding this article?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.