Evaluation of Time-of-Flight Secondary Ion Mass Spectrometry Spectra of Peptides by Random Forest with Amino Acid Labels: Results from a Versailles Project on Advanced Materials and Standards Interlaboratory Study.

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.

Wire-tracking of bent electric cable using X-ray CT and deep active learning.

We have demonstrated a quantification of all component wires in a bent electric cable, which is necessary for discussion of cable products in actual use cases. Quantification became possible for the first time because of our new technologies for image analysis of bent cables. In this paper, various image analysis techniques to detect all wire tracks in a bent cable are demonstrated. Unique cross-sectional image construction and deep active learning schemes are the most important items in this study. These methods allow us to know the actual state of cables under external loads, which makes it possible to elucidate the mechanisms of various phenomena related to cables in the field and further improve the quality of cable products.

Detrimental Effect of Sintering Additives on Conducting Ceramics: Yttrium-Doped Barium Zirconate.

Y-doped BaZrO3 (BZY) is currently the most promising proton-conductive ceramic-type electrolyte for application in electrochemical devices, including fuel cells and electrolyzer cells. However, owing to its refractory nature, sintering additives, such as NiO, CuO, or ZnO are commonly added to reduce its high sintering temperature from 1600 °C to approximately 1400 °C. Even without deliberately adding a sintering additive, the NiO anode substrate provides another source of the sintering additive; during the co-sintering process, NiO diffuses from the anode into the BZY electrolyte layer. In this work, a systematic study of the effect of NiO, CuO, and ZnO on the electroconductive properties of BaZr0.8 Y0.2 O3-δ (BZY20) is conducted. The results revealed that the addition of NiO, CuO, or ZnO into BZY20 not only degraded the electrical conductivity but also resulted in enhancement of the hole conduction. Removal of these sintering additives can be realized by post-annealing in hydrogen at a mild temperature of 700 °C, but it is kinetically very slow. Therefore, the addition of NiO, CuO, and ZnO is detrimental to the electroconductive properties of BZY20, and significantly restrict its application as an electrolyte. The development of new sintering additives, new anode catalysts, or new methods for preparing BZY electrolyte-based cells is urgently needed.