Tag: macromolecules

Calculate the orientation of macromolecules in three-dimensional space

Calculate the orientation of macromolecules in three-dimensional space

Scientists from the CIRI beamline are developing a method to calculate the orientation of macromolecules in three-dimensional space based on microscopic measurements using mid-infrared light polarized linearly at different angles. Reconstructing the experimental shape of the absorption dependency on polarization by fitting a nonlinear function to the data points obtained from the measurements allows for determining the angles that define the orientation of macromolecules in three-dimensional space, as well as a parameter describing the degree of sample organization. The measurements were conducted using an FPA array detector available at the FT-IR microscopy end-station on the CIRI beamline.

This method is non-destructive and does not require labeling, making it particularly useful for imaging and quantitatively determining the order parameters in various types of anisotropic polymer and biological samples.

The study focuses on the structural characterization of poly(lactic acid) (PLLA), a biodegradable polymer known for its polymorphism and significant potential in various applications due to its environmentally friendly nature. Scientists prepared thin PLLA films and, through thermal treatment, obtained a sample with varied morphology, including an amorphous phase, isolated spherulites, and larger clusters of the semi-crystalline phase. The reconstruction of the three angles of macromolecule orientation revealed that the morphological organization in the amorphous phase is random, and the molecular orientation differs from the semi-crystalline phase also in the third dimension in thin films.

Read more on SOLARIS website

Image: Figure 1. Optical microscopic image of the PLA film (a). 4P-3D orientation results for a pair of 1088–1041 cm–1. Visualization of the primary transition moment (1088 cm–1): centers of nucleation (b), borders between spherulites and amorphous phase (c), and the <P2> image of the same region (d). 

Calculating the “fingerprints” of molecules with artificial intelligence

Calculating the “fingerprints” of molecules with artificial intelligence

With conventional methods, it is extremely time-consuming to calculate the spectral fingerprint of larger molecules. But this is a prerequisite for correctly interpreting experimentally obtained data. Now, a team at HZB has achieved very good results in significantly less time using self-learning graphical neural networks.

“Macromolecules but also quantum dots, which often consist of thousands of atoms, can hardly be calculated in advance using conventional methods such as DFT,” says PD Dr. Annika Bande at HZB. With her team she has now investigated how the computing time can be shortened by using methods from artificial intelligence.

The idea: a computer programme from the group of “graphical neural networks” or GNN receives small molecules as input with the task of determining their spectral responses. In the next step, the GNN programme compares the calculated spectra with the known target spectra (DFT or experimental) and corrects the calculation path accordingly. Round after round, the result becomes better. The GNN programme thus learns on its own how to calculate spectra reliably with the help of known spectra.

Read more on the HZB website

Image: The graphical neural network GNN receives small molecules as input with the task of determining their spectral responses. By matching them with the known spectra, the GNN programme learns to calculate spectra reliably.

Credit: © K. Singh, A. Bande/HZB