Organic-inorganic hybrid materials enable the design and fabrication of new materials with enhanced properties. The interface between the organic and inorganic materials is often critical to the device's performance and therefore chemical characterization is of significant interest. Since the interfaces are often buried, milling by focused ion beams (FIB) to expose the interface is becoming increasingly popular. Chemical imaging can subsequently be obtained using secondary ion mass spectrometry.However, the FIB milling process damages the organic material. In this study, we make an organicinorganic test structure to develop a detailed understanding of the processes involved in FIB milling and SIMS imaging. We provide an analysis methodology that involves a "clean-up" process using sputtering with an Argon gas cluster ion source to remove the FIB induced damage. The methodology is evaluated for an additive manufactured encapsulated strain sensor containing silver tracks embedded in a polymeric material. We show a polymer-silver interface with a resolution of 440 nm and that the polymer contains a low level of silver particulates.

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.

The investigation of fragment ions from macromolecules is crucial for the interpretation of time-of-flight secondary ion mass spectrometry (TOF-SIMS) data, although it is often difficult because fragmentation mechanisms in secondary ion mass spectrometry have not been clarified. In this study, multivariate curve resolution (MCR) was applied to extract a pure component spectrum, and gentle SIMS (G-SIMS) was applied to the investigation of fragment ions from polymers. Several types of polyethylene glycol were diluted in toluene solutions, and then thin films of them were formed on Si wafers using a spin coater. The samples were measured with TOF-SIMS using Mn + , Bi + , and Bi 3 + and then the TOF-SIMS spectra data were analyzed with G-SIMS and MCR. As a result, relationships between fragment ions were clarified using G-SIMS, and a pure sample spectrum of each polyethylene glycol sample was extracted by MCR.

The g-ogram method effectively separates out mass peaks relating to the substrate, contamination and protein without any a priori information or subjective decisions about which peaks to include in the analysis (so called 'peak picking'). This is a great help to analysts. We find two possible peaks from plural amino acids but no evidence of pluralities is found for peaks above 240 Da that are generated from when using Bi or Mn primary ions.