RESUMEN
We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the identification of peptide sample TOF-SIMS spectra by machine learning. More than 1000 time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of six peptide model samples (one of them was a test sample) were collected using 27 TOF-SIMS instruments from 25 institutes of six countries, the U. S., the U. K., Germany, China, South Korea, and Japan. Because peptides have systematic and simple chemical structures, they were selected as model samples. The intensity of peaks in every TOF-SIMS spectrum was extracted using the same peak list and normalized to the total ion count. The spectra of the test peptide sample were predicted by Random Forest with 20 amino acid labels. The accuracy of the prediction for the test spectra was 0.88. Although the prediction of an unknown peptide was not perfect, it was shown that all of the amino acids in an unknown peptide can be determined by Random Forest prediction and the TOF-SIMS spectra. Moreover, the prediction of peptides, which are included in the training spectra, was almost perfect. Random Forest also suggests specific fragment ions from an amino acid residue Q, whose fragment ions detected by TOF-SIMS have not been reported, in the important features. This study indicated that the analysis using Random Forest, which enables translation of the mathematical relationships to chemical relationships, and the multi labels representing monomer chemical structures, is useful to predict the TOF-SIMS spectra of an unknown peptide.
RESUMEN
This review summarizes the recent advances in three-dimensional (3D) imaging techniques and their application to polymer nanostructures, for example, microphase-separated structures of block copolymers. We place particular emphasis on the method of transmission electron microtomography (electron tomography for short; hereafter abbreviated as ET). As a result of recent developments in ET, truly quantitative 3D images of polymer nanostructures can now be obtained with subnanometer resolution. The introduction of scanning optics in ET has made it possible to obtain large amounts of 3D data from micrometer-thick polymer specimens by using conventional electron microscopes at a relatively low accelerating voltage, 200 kV. Thus, ET covers structures over a wide range of thicknesses, from a few nanometers to several hundred nanometers, which corresponds to quite an important spatial range for hierarchical polymer nanostructures. ET provides clear 3D images and a wide range of new structural information that cannot be obtained using other methods. Information traditionally derived from conventional microscopy or scattering methods can be directly acquired from 3D volume data. ET is a versatile technique that is not restricted to only polymer applications; it can also be used as a powerful characterization tool in energy applications such as fuel cells.
