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.

Quantification of the composition of binary mixtures in secondary ion mass spectrometry (SIMS) is required in the analyses of technological materials from organic electronics to drug delivery systems. In some instances, it is found that there is a linear dependence between the composition, expressed as a ratio of component volumes, and the secondary ion intensities, expressed as a ratio of intensities of ions from each component. However, this ideal relationship fails in the presence of matrix effects and linearity is observed only over small compositional ranges, particularly in the dilute limits. In this paper, we assess an empirical method, which introduces a power law dependence between the intensity ratio and the volume fraction ratio. A previously published physical model of the organic matrix effect is employed to test the limits of the method and a mixed system of 3,3′‐bis(9‐carbazolyl) biphenyl and tris(2‐phenylpyridinato)iridium (III) is used to demonstrate the method. This paper introduces a two‐point calibration, which determines both the exponent in the power law and the sensitivity factor for the conversion of ion intensity ratio into volume fraction ratio. We demonstrate that this provides significantly improved accuracy, compared with a one‐point calibration, over a wide compositional range in SIMS quantification and with a weak dependence on matrix effects. Because the method enables the use of clearly identifiable secondary ions for quantitative purposes and mitigates commonly observed matrix effects in organic materials, the two‐point calibration method could be of significant benefit to SIMS analysts.

Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS), when used for the analysis of complex material samples, typically provides data that are complicated and challenging to understand. Therefore, additional data analysis techniques, such as multivariate analysis, are often required to facilitate the interpretation of TOF‐SIMS data. In this study, a new method based on the information entropy (Shannon entropy) is proposed as an indicator of the outline characteristics of an unknown sample, such as changes in the material within the sample and mixing conditions. The Shannon entropy values are calculated using the relative intensity of every secondary ion normalized to the total ion count and reflect the diversity of secondary ions in the spectrum. Mixed samples containing two organic electroluminescence materials of different ratios, multilayers of Irganox 1010, and other organic materials were employed to evaluate the utility of Shannon entropy in the analysis of TOF‐SIMS data. The findings demonstrate that the Shannon entropy of a spectrum indicates differences in materials and changes in the conditions of a material in a sample without the need for peak identification or the knowledge of specific peaks corresponding to the materials in the sample.

It is widely recognized that, in comparison with conventional Ar + sputter etching, Argon gas cluster ion beam (Ar-GCIB) sputtering provides mild etching of a sample surface because of its low energy per atom and lateral sputtering effect. X-ray photoelectron spectroscopy (XPS) combined with Ar-GCIB has become established as an in-depth analysis technique for organic materials. Considering such advantages, Ar-GCIB irradiation conditions were investigated for removal of organic contaminations on inorganic material surfaces. The bonding state of the native oxide on the Si-substrate surface was employed as an indicator of surface damage. It was found that the incident angle of Ar-GCIB irradiation strongly affects the roughness and damage to the surface. It was also found that the surface contamination layer can be removed without affecting the native oxide film on the Si-substrate when the incident angle is 85° for Ar 1000 + and 80° for Ar 2000 + , respectively.