Solution processable fullerene and copolymer bulk heterojunctions are widely used as the active layers of solar cells. In this work, scanning time-of-flight secondary ion mass spectrometry (ToF-SIMS) is used to examine the distribution of [6,6]phenyl-C61-butyric acid methyl ester (PCBM) and regio-regular poly(3-hexylthiophene) (rrP3HT) that forms the bulk heterojunction. The planar phase separation of P3HT:PCBM is observed by ToF-SIMS imaging. The depth profile of the fragment distribution that reflects the molecular distribution is achieved by low energy Cs(+) ion sputtering. The depth profile clearly shows a vertical phase separation of P3HT:PCBM before annealing, and hence, the inverted device architecture is beneficial. After annealing, the phase segregation is suppressed, and the device efficiency is dramatically enhanced with a normal device structure. The 3D image is obtained by stacking the 2D ToF-SIMS images acquired at different sputtering times, and 50 nm features are clearly differentiated. The whole imaging process requires less than 2 h, making it both rapid and versatile.

No abstract

No abstract

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.

In order to overcome the limitations of sputter depth profiling, the authors have introduced focused ion beam-time-of-flight secondary ion mass spectrometry (FIB-TOF-SIMS). In this article, the authors summarize our investigation into the capability of Ar-gas cluster ion beam (GCIB) to remove FIB-induced molecular damage. The analysis of organic-inorganic hybrid mixture samples is applied and discussed. The authors demonstrate a method whereby the accurate and reproducible chemical depth distributions of atomic and molecular moieties in hybrid materials are successfully acquired. Our results reveal the approach of using Ar-GCIB for molecular recovery of FIB straggle to be highly reproducible and amenable to three-dimensional materials characterization.

In recent years, all-solid-state batteries (ASSBs) have been attracting attention as the next generation batteries for electric vehicles, energy storage systems, etc. Despite the growing interest, there are still many challenges faced in the commercial use of ASSBs. One of the biggest issues is the internal resistance, especially generated at the interface between solid electrolyte and electrode. The internal resistance at the interface limits the charge-discharge cycling performances. In order to solve this issue, it is necessary to examine the chemical and physical interactions at the interface. In this study, we have performed a detailed characterization of a LiPON/LiCoO2 interface using time-of-flight secondary ion mass spectrometry, x-ray photoelectron spectroscopy, ultraviolet photoelectron spectroscopy, and low-energy inverse photoelectron spectroscopy to obtain information on chemical species, chemical compositions, chemical states, and energy band diagrams. These powerful techniques have revealed that an interlayer between LiPON and LiCoO2 was formed due to the temperature rise during the manufacturing process. The temperature rise caused a change of the LiPON network structure and stimulated Co reduction in the LiCoO2 layer near the interface. Energy band diagram analysis suggests that the electron diffusion from LiPON to LiCoO2 may have triggered the reduction of Co. We concluded that the chemical changes that occur at the interface caused an increase in interfacial impedance. Preventing the chemical reduction of Co would be a key to minimize the internal resistance. In this article, the detailed chemical interactions between the LiPON and LiCoO2 layers will be discussed.

No abstract

No abstract