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.

Versailles Project on Advanced Materials and Standards interlaboratory study on intensity calibration for x-ray photoelectron spectroscopy instruments using low-density polyethylene.

We report the results of a Versailles Project on Advanced Materials and Standards interlaboratory study on the intensity scale calibration of x-ray photoelectron spectrometers using low-density polyethylene (LDPE) as an alternative material to gold, silver, and copper. An improved set of LDPE reference spectra, corrected for different instrument geometries using a quartz-monochromated Al Kα x-ray source, was developed using data provided by participants in this study. Using these new reference spectra, a transmission function was calculated for each dataset that participants provided. When compared to a similar calibration procedure using the NPL reference spectra for gold, the LDPE intensity calibration method achieves an absolute offset of ∼3.0% and a systematic deviation of ±6.5% on average across all participants. For spectra recorded at high pass energies (≥90 eV), values of absolute offset and systematic deviation are ∼5.8% and ±5.7%, respectively, whereas for spectra collected at lower pass energies (<90 eV), values of absolute offset and systematic deviation are ∼4.9% and ±8.8%, respectively; low pass energy spectra perform worse than the global average, in terms of systematic deviations, due to diminished count rates and signal-to-noise ratio. Differences in absolute offset are attributed to the surface roughness of the LDPE induced by sample preparation. We further assess the usability of LDPE as a secondary reference material and comment on its performance in the presence of issues such as variable dark noise, x-ray warm up times, inaccuracy at low count rates, and underlying spectrometer problems. In response to participant feedback and the results of the study, we provide an updated LDPE intensity calibration protocol to address the issues highlighted in the interlaboratory study. We also comment on the lack of implementation of a consistent and traceable intensity calibration method across the community of x-ray photoelectron spectroscopy (XPS) users and, therefore, propose a route to achieving this with the assistance of instrument manufacturers, metrology laboratories, and experts leading to an international standard for XPS intensity scale calibration.

Application of focused ion-beam sampling for sidewall-roughness measurement of free-standing sub-µm objects by atomic force microscopy.

In the present study, a free-standing object-sampling technique for microelectromechanical systems (MEMS) is developed to measure their sidewall surface roughnesses by atomic force microscopy (AFM). For this purpose, a conventional focused ion beam (FIB) sampling technique widely used for cross-sectional transmission electron microscope specimen preparation was applied. The sub-nm-order roughness parameters were quantitatively measured for sidewalls of Si-bridge test samples. The roughness parameters were compared before and after H2 annealing treatment, which induced smoothing of the surface by migration of the Si atoms. The reduction in the surface roughness by a factor of approximately one-third with 60-s H2 annealing was quantitatively evaluated by AFM. The present study confirms that the developed FIB-AFM technique is one potential approach for quantitatively evaluating the surface-roughness parameters on the oblique faces of free-standing objects in MEMS devices.

Hard x-ray phase contrast imaging using a tabletop Talbot-Lau interferometer with multiline embedded x-ray targets.

We demonstrate hard x-ray phase contrast imaging (XPCI) using a tabletop Talbot-Lau interferometer in which the x-ray source and source grating are replaced with an x-ray source with multiline metal targets embedded in a diamond substrate. This source realizes an array of linear x-ray sources of a few micrometers width without fabrication difficulty because of the shallow penetration depth of electrons irradiated to the metal targets. This enhances the coherence of x rays from each linear source and allows XPCI within 45 cm source-detector distance under 1.2 W input power for 8 keV x rays.

Catalysis of nickel ferrite for photocatalytic water oxidation using [Ru(bpy)3]2+ and S2O8(2-).

Single or mixed oxides of iron and nickel have been examined as catalysts in photocatalytic water oxidation using [Ru(bpy)(3)](2+) as a photosensitizer and S(2)O(8)(2-) as a sacrificial oxidant. The catalytic activity of nickel ferrite (NiFe(2)O(4)) is comparable to that of a catalyst containing Ir, Ru, or Co in terms of O(2) yield and O(2) evolution rate under ambient reaction conditions. NiFe(2)O(4) also possesses robustness and ferromagnetic properties, which are beneficial for easy recovery from the solution after reaction. Water oxidation catalysis achieved by a composite of earth-abundant elements will contribute to a new approach to the design of catalysts for artificial photosynthesis.

Surface excitations in surface electron spectroscopies studied by reflection electron energy-loss spectroscopy and elastic peak electron spectroscopy.

Surface excitations, in addition to bulk excitation, undergone by signal electrons in surface electron spectroscopies, such as Auger electron spectroscopy, and X-ray photoelectron spectroscopy, play an important role in the formation of electron spectra. Those inelastic scattering processes not only induce decay in the peak intensity, but also form background appearing in the lower kinetic energy side of relevant peaks. Information on surface excitation is essential in addition to bulk excitations for the quantification of material surfaces by surface electron spectroscopies, and extensive studies have been devoted to it. In this report, we introduce the basics of the study of surface excitations by reflection electron energy loss spectroscopy (REELS) and elastic peak electron spectroscopy (EPES). The application of several approaches within the schemes of EPES analysis and REELS analysis to the experimental determination of inelastic scattering parameters, such as the surface excitation parameter (SEP), differential SEP (DSEP), inelastic mean free path (IMFP), and dielectric function, are also introduced. Information useful to calculate the values of the IMFP and SEP using predictive equations is provided in Supporting Information as well.