Gardner, Wil published the artcileToF-SIMS and Machine Learning for Single-Pixel Molecular Discrimination of an Acrylate Polymer Microarray, HPLC of Formula: 142-90-5, the main research area is ToF MS machine learning mol discrimination acrylate polymer microarray.
Combinatorial approaches to materials discovery offer promising potential for the rapid development of novel polymer systems. Polymer microarrays enable the high-throughput comparison of material phys. and chem. properties-such as surface chem. and properties like cell attachment or protein adsorption-in order to identify correlations that can progress materials development. A challenge for this approach is to accurately discriminate between highly similar polymer chemistries or identify heterogeneities within individual polymer spots. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers unique potential in this regard, capable of describing the chem. associated with the outermost layer of a sample with high spatial resolution and chem. sensitivity. However, this comes at the cost of generating large scale, complex hyperspectral imaging data sets. We have demonstrated previously that machine learning is a powerful tool for interpreting ToF-SIMS images, describing a method for color-tagging the output of a self-organizing map (SOM). This reduces the entire hyperspectral data set to a single reconstructed color similarity map, in which the spectral similarity between pixels is represented by color similarity in the map. Here, we apply the same methodol. to a ToF-SIMS image of a printed polymer microarray for the first time. We report complete, single-pixel mol. discrimination of the 70 unique homopolymer spots on the array while also identifying intraspot heterogeneities thought to be related to intermixing of the polymer and the pHEMA coating. In this way, we show that the SOM can identify layers of similarity and clusters in the data, both with respect to polymer backbone structures and their individual side groups. Finally, we relate the output of the SOM anal. with fluorescence data from polymer-protein adsorption studies, highlighting how polymer performance can be visualized within the context of the global topol. of the data set.
Analytical Chemistry (Washington, DC, United States) published new progress about Machine learning. 142-90-5 belongs to class esters-buliding-blocks, name is Dodecyl 2-methylacrylate, and the molecular formula is C16H30O2, HPLC of Formula: 142-90-5.
Referemce:
Ester – Wikipedia,
Ester – an overview | ScienceDirect Topics