Secondary Ion Mass Spectrometry Spectra Research Articles

Exploring the Performance of Linear and Nonlinear Models of Time-of-Flight Secondary Ion Mass Spectrometry Spectra.

Multivariate statistical tools and machine learning (ML) techniques can deconvolute hyperspectral data and control the disparity between the number of samples and features in materials science. Nevertheless, the importance of generating sufficient high-quality sample replicates in training data cannot be overlooked, as it fundamentally affects the performance of ML models. Here, we present a quantitative analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra of a simple microarray system of two food dyes using partial least-squares (PLS, linear) and random forest (RF, nonlinear) algorithms. This microarray was generated by a high-throughput sample preparation and analysis workflow for fast and efficient acquisition of quality and reproducible spectra via ToF-SIMS. We drew insights from the bias-variance trade-off, investigated the performances of PLS and RF regression models as a function of training data size, and inferred the amount of data needed to construct accurate and reliable regression models. In addition, we found that the spectral concatenation of positive and negative ToF-SIMS spectra improved the model performances. This study provides an empirical basis for future design of high-throughput microarrays and multicomponent systems, for the purpose of analysis with ToF-SIMS and ML.

Determination of impurity distribution in IG-11/110 nuclear graphite using TOF-SIMS

Graphite serves as moderator and structural material in various type of graphite-based reactors. The presence of impurities within the graphite matrix can lead to adverse consequences, including diminished fuel efficiency, heightened radionuclide production, and catalytic oxidation. High-purity graphite materials also find essential applications in diverse industries, such as semiconductor manufacturing and chemical analysis. In this study, we conducted an investigation into IG-11 graphite, which has an ash content of 347 ppm, and its purified version, IG-110 nuclear grade graphite, which has an ash content of 12.7 ppm, to understand the purification mechanism of polycrystalline graphite. The analysis of Time-of-flight secondary ion mass spectrometry spectra revealed that metallic impurities in IG-11 were primarily segregated within graphite porosities or appeared as discrete point inclusions, rather than uniformly distributed throughout the graphite matrix. IG-110 demonstrated a significant reduction in impurities such as Na, K, Ca, and Al compared to IG-11, but Ti was still present within its porosity. Notably, impurities residing on the walls of these porosities can accelerate graphite oxidation. The establishment of the theory on impurity segregation in porosities help to develop innovative graphite purification techniques that produce graphite with even greater purity and stronger oxidation resistance.

Molecular identification of wines using in situ liquid SIMS and PCA analysis.

Composition analysis in wine is gaining increasing attention because it can provide information about the wine quality, source, and nutrition. In this work, in situ liquid secondary ion mass spectrometry (SIMS) was applied to 14 representative wines, including six wines manufactured by a manufacturer in Washington State, United States, four Cabernet Sauvignon wines, and four Chardonnay wines from other different manufacturers and locations. In situ liquid SIMS has the unique advantage of simultaneously examining both organic and inorganic compositions from liquid samples. Principal component analysis (PCA) of SIMS spectra showed that red and white wines can be clearly differentiated according to their aromatic and oxygen-contained organic species. Furthermore, the identities of different wines, especially the same variety of wines, can be enforced with a combination of both organic and inorganic species. Meanwhile, in situ liquid SIMS is sample-friendly, so liquid samples can be directly analyzed without any prior sample dilution or separation. Taken together, we demonstrate the great potential of in situ liquid SIMS in applications related to the molecular investigation of various liquid samples in food science.

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.

Investigation of mechanism of smear formation from diamond-like carbon films on heating

The formation of head smears during the heat-assisted magnetic recording is a significant problem that must be addressed to ensure the reliability of the head-disk interface. In this study, the formation of a head smear by diamond-like carbon (DLC) films on magnetic disks was analyzed through simulated heating experiments. DLC films with a thickness of 100 nm were prepared and heated on a hot plate in an environmental gas chamber. A smear was observed on a silicon wafer placed 3 mm above the DLC surface film after the heating of the film. The smear amount was quantified based on the intensity of the carbon peak of the smear, which was measured by X-ray photoelectron spectroscopy. In air, the carbon concentration decreased with the heating temperature, while in nitrogen, it increased. Further, the carbon concentration of the smear formed in air was lower than that in nitrogen. A comparison of the smears formed by DLC films with different hydrogen concentrations showed that a greater amount of the decomposition gases was generated from the DLC films with a higher hydrogen concentration and that this value increased at lower temperatures. This result suggests that DLC films with low hydrogen concentrations generate smaller amounts of the decomposition gases during heating than those with high hydrogen concentrations. In addition, the results of Raman spectroscopy suggested that the DLC films get degraded during heating, resulting in the generation of hydrocarbon gases because the films contain carbon and hydrogen. Moreover, since peaks related to fragments with atomic masses of 250–500 atomic mass units were observed in the time-of-flight secondary ion mass spectrometry spectra of the smears, it can be concluded that the smears consist of hydrocarbons with relatively higher molecular weights.

TOF-SIMS Analysis Using Bi3 + as Primary Ions on Au Nanoparticles Supported by SiO2/Si: Providing Insight into Metal-Support Interactions.

Au nanoparticles with a mean diameter of 20 nm with a coverage of ∼20% of the surface were distributed on a Si wafer surface and studied both before and after being annealed (at 100 and 300 °C). The two types of samples were analyzed using secondary ion mass spectroscopy (SIMS) with Bi3+ clusters as the primary ions combined with surface etching using Ar1000+ clusters. We observed a substantial difference in the SIMS spectra combined with a relatively short sputtering time of Ar1000+. In the nonannealed samples, bare Au cluster cations and Si+ were observed in the SIMS spectra; AuSi+ clusters were also observed in the annealed samples. These results indicate Au-silicide formation at a part of the periphery of the Au nanoparticles upon annealing. We suggest that SIMS experiments using cluster ions such as Bi3+ can not only be used for surface elemental analyses but also provide information on local chemical environments of elements on the surface. This is an important issue in heterogeneous catalysis (e.g., strong metal–support interactions). We also advise that one should be careful interpreting the SIMS data combined with a longer Ar1000+ sputtering time because this can deteriorate the surfaces from their original structures.

In Situ Liquid Secondary Ion Mass Spectrometry: A Surprisingly Soft Ionization Process for Investigation of Halide Ion Hydration.

The understanding of ion solvation phenomena is of significance due to their influences on many important chemical, biological, and environmental processes. Mass spectrometry (MS)-based methods have been used to investigate this topic with molecular insights. As ion-solvent interactions are weak, ionization processes should be as soft as possible in order to retain solvation structures. An in situ liquid secondary ion MS (SIMS) approach developed in our group has been recently utilized in investigations of Li ion solvation in nonaqueous solution, and it detected a series of solvated Li ions. As traditionally SIMS has long been recognized as a hard ionization process with strong damage occurring at the sputtering interface, it is very interesting to study further how soft in situ liquid SIMS can be. In this work, we used halide ion hydration as an example to compare the ionization performance of the in situ liquid SIMS approach with regular electrospray ionization MS (ESI-MS). Results show that, although ESI has been recognized as a soft ionization method, nearly no solvated halide ions were detected by ESI-MS analysis, which acquired only strong signals of salt ion clusters. In contrast, in liquid SIMS spectra, a series of obvious hydrated halide ion compositions could be observed. We further evaluated the hydration numbers of halide ions and revealed the effects of the ion size, charge density, and polarizability on the hydration phenomenon. Our findings demonstrated that the in situ liquid SIMS approach is surprisingly soft, and it is expected to have very broad applications on investigation of various ion-solvent interactions and many other interesting chemical processes (e.g., the initial nucleation of nanoparticle formation) in liquid environment.

Secondary ion mass spectrometry (SIMS) with Bi3+ primary ions as a sensitive probe of surface structures of heterogeneous catalysts

We prepared mesoporous Al2O3- or SiO2-supported Fe-oxide nanoparticles and their geometric and chemical properties were studied. For Fe-oxide/SiO2, transmission electron microscopy (TEM) images and X-ray diffraction (XRD) patterns clearly show structure of Fe-oxide with particle sizes of 7–15 nm. However, TEM and XRD did not show clear evidence for the structure of Fe-oxide on Al2O3, even though a higher dispersity of Fe-oxide on Al2O3 was suggested by elemental mapping techniques. Catalytic activity of Al2O3-supported Fe-oxide was much higher than that of the SiO2-based catalyst for CO oxidation and NO reduction by CO, implying a higher effective surface area of Fe-oxide on Al2O3, also in good agreement with Fe-oxide being more highly dispersed on Al2O3. SIMS shed more light on the catalyst structure. The Fe+/Al+ ratio from Fe-oxide/Al2O3 was much higher than the Fe+/Si+ ratio from Fe-oxide/SiO2, in line with our observations from TEM and catalytic activity that Fe-oxide is more highly dispersed on Al2O3 than on SiO2. Moreover, not only Al+ and Fe+ but also mixed cluster ions of Fe+ and Al+ such as FeAlO+, Al2FeOH2+ and Al2FeO3+ were observed from the surface of Fe-oxide on Al2O3. The presence of these ions comments on chemical bond formation between Fe-oxide and Al2O3 at the interface of nanostructured overlayers and substrates. The Fe-oxide/SiO2 surface produced much less pronounced SIMS emission of FeSiO+ compared to the Fe-oxide/Al2O3 surface, suggesting lower dispersion and much larger size of the Fe-oxide particles on SiO2. Detailed analyses of SIMS spectra can provide deep insight into complex nanostructures.

Soft X‐ray characterization of ion beam sputtered magnesium oxide (MgO) thin film

In the present study, surface and interface characterization of magnesium oxide (MgO) thin film is carried out by using non‐destructive soft X‐ray reflectivity and absorption technique. To get a further insight about the in‐depth and surface composition, secondary ion mass spectroscopy measurement is also carried out. The analysis of the reflectivity data indicates the presence of Mg‐Si‐O layer between the principal layer (MgO) and Si substrate interface. The secondary ion mass spectroscopy spectra corroborate well with the model assumed in the analysis of the reflectivity data. Combined soft X‐ray reflectivity‐total electron yield result confirms the presence of low‐density MgO on top of principal MgO layer. Total electron yield result confirms the rocksalt structure of the film and provides a glimpse of the electronic structure near the O‐K absorption edge.

Automated data mining of secondary ion mass spectrometry spectra

AbstractTime of flight secondary ion mass spectrometry (ToF‐SIMS) allows the reliable analytical determination of organic and polymeric materials. Since a typical raw data may contain thousands of peaks, the amount of information to deal with is accordingly large, so that data reduction techniques become indispensable for extracting the most significant information from the given dataset. Here, the use of the wavelet‐principal component analysis–based signal processing of giant raw data acquired during ToF‐SIMS experiments is presented. The proposed procedure provides a straightforwardly “manageable” dataset without any binning procedure neither detailed integration. By studying the principal component analysis results, detailed and reliable information about the chemical composition of polymeric samples have been gathered.

Thermally annealed iron thin film on an alumina barrier layer by ToF-SIMS

The authors report the positive and negative ion time-of-flight secondary ion mass spectrometry spectra obtained with Bi32+ primary ions at 50 keV of an Fe film annealed in hydrogen at 750 °C to form Fe nanoparticles. This surface had been exposed to the air; it had previously been shown by x-ray photoemission spectroscopy to be entirely oxidized. The strongest signal in the positive ion spectrum corresponded to Fe+, with FexOyHz+ species also present. A substantial Al+ signal was observed due to exposure of the underlying alumina substrate. The negative ion spectrum showed O−, OH−, and AlO−.

Ambient analysis of liquid materials with Wet-SIMS

Secondary ion mass spectrometry (SIMS) is a method with high surface sensitivity that allows both elemental and molecular analysis. However, volatile liquid (wet) samples are difficult to measure using conventional SIMS, because samples must be dried and introduced into a high vacuum chamber. The mean free path of ions with energy in the keV range is very short in low vacuum and these ions cannot penetrate the surface. In contrast, ions in the MeV-energy range have high transmission capability in low vacuum and wet samples can be measured using heavy ions without dry sample preparation. Ion beams in the MeV-energy range also excite electrons near the surface and enhance the ionization of high-mass molecules and thus fragment-suppressed SIMS spectra of ionized molecules can be obtained. We have developed an ambient analysis system with secondary ion mass spectrometry for wet samples (Wet-SIMS) that operates from low vacuum to 30kPa using MeV-energy heavy ion beams. The system is equipped with fine apertures that avoid vacuum degradation at both the primary beam incidence and the secondary ion measurement sides, even when the target chamber is filled with He gas at 30kPa. Water evaporation was suppressed in a He atmosphere of 16.5kPa and a solution of benzoic acid could be measured using MeV-energy heavy ions.

Thermally evaporated (oxide) iron on an alumina barrier layer by ToF-SIMS

The authors report the positive and negative ion time-of-flight secondary ion mass spectrometry spectra using Bi32+ primary ions at 50 keV of an Fe film (6 nm) that had been thermally evaporated on a thin film of alumina (ca. 35 nm) on a silicon wafer. This surface had been exposed to the air; it had previously been shown by x-ray photoemission spectroscopy (XPS) to be entirely oxidized. The positive ion secondary ion mass spectrometry spectrum shows Fe+, FeH+, and FexOyHz+ species. The negative ion spectrum shows FeOH−, FexOyHz−, and OH− species.

Diffusion barrier property of MnSixOy layer formed by chemical vapor deposition for Cu advanced interconnect application

An amorphous manganese oxide layers formed by chemical vapor deposition have been studied as a copper diffusion barrier. The thermal stability of the barrier layer was assessed by annealing Cu/MnSixOy/SiO2/Si samples at 400°C for various times up to 10h. Transmission electron microscopy, energy-dispersive X-ray spectroscopy (EDX), secondary ion mass spectroscopy (SIMS), capacitance-voltage and current–voltage measurements were performed. Failure of the barrier property is marked by observing the copper peak appearing in EDX and SIMS spectra data from the SiO2 region. Amorphous MnSixOy barrier with a thickness of 1.2 nm has failed in preventing Cu diffusion into SiO2 substrate after anneal at 400°C in vacuum for 1h, as proven by the presence of Cu in the dielectric (SiO2) layer. However, the amorphous MnSixOy with the thickness of 2.0nm barrier was thermally stable and could prevent Cu from inter-diffusion to the SiO2 substrate after annealing at 400°C even up to 10h.

Biomaterial imaging with MeV-energy heavy ion beams

The spatial distribution of several chemical compounds in biological tissues and cells can be obtained with mass spectrometry imaging (MSI). In conventional secondary ion mass spectrometry (SIMS) with keV-energy ion beams, elastic collisions occur between projectiles and atoms of constituent molecules. The collisions produce fragments, making the acquisition of molecular information difficult. In contrast, ion beams with MeV-energy excite near-surface electrons and enhance the ionization of high-mass molecules; hence, SIMS spectra of fragment-suppressed ionized molecules can be obtained with MeV-SIMS. To compare between MeV and conventional SIMS, we used the two methods based on MeV and Bi3-keV ions, respectively, to obtain molecular images of rat cerebellum. Conventional SIMS images of m/z 184 were clearly observed, but with the Bi3 ion, the distribution of the molecule with m/z 772.5 could be observed with much difficulty. This effect was attributed to the low secondary ion yields and we could not get many signal counts with keV-energy beam. On the other hand, intact molecular ion distributions of lipids were clearly observed with MeV-SIMS, although the mass of all lipid molecules was higher than 500Da. The peaks of intact molecular ions in MeV-SIMS spectra allowed us to assign the mass. The high secondary ion sensitivity with MeV-energy heavy ions is very useful in biomaterial analysis.

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu–Fe sulphides by principal component analysis and artificial neural networks

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples.

Identification and separation of protein, contaminant and substrate peaks using gentle‐secondary ion mass spectrometry and the g‐ogram

Secondary ion mass spectrometry (SIMS) is an important technique for the characterization of proteins at surfaces. However, interpretation of the mass spectra is complicated owing to confusion with peaks from contaminants and the substrate which is further compounded by complex fragmentation mechanisms. We test a new development of the G-SIMS method called the g-ogram to separate out spectral components without a priori information about which peaks to include in the analysis and which peaks relate to each component. The effectiveness of the g-ogram method is investigated using a model system of lysozyme adsorbed onto a silicon wafer and indium tin oxide substrates. In the method, two SIMS spectra are acquired using Bi(+) and Mn(+) primary ions which create lower and higher fragmentation in the spectra, respectively. The g-ogram separates out components using a separation parameter that is related to the fragmentation energy. The g-ogram separates the spectrum of lysozyme adsorbed onto a silicon wafer into three components: (i) the substrate and PDMS contamination; (ii) a second, but unexpected, contaminant; and (iii) peaks from the protein amino acids. Similar results are achieved for the indium tin oxide substrate. In addition, evidence of fragments from plural amino acids with two candidate peaks at 140.12 Da and 185.08 Da is observed. 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.

Highly sensitive analysis of surface contaminants by Ar gas cluster SIMS

A size‐selected Ar gas cluster ion beam (GCIB) is applied to secondary ion mass spectrometry (SIMS) of a polystyrene (PS) thin film, a 1,4‐didodecylbenzene (DDB) thin film, and an ITO glass sample. All three samples are contaminated by poly(dimethylsiloxane) (PDMS) on the surface. Compared with the Ar+ SIMS spectra, the fragments in the PS and DDB SIMS spectra for Ar1550+, including siloxane, are enhanced more than ~100‐fold, whereas the hydrocarbon fragments are enhanced 10‐ to 20‐fold. The shallow volume sputtering by GCIB is responsible for the preferential enhancement of the surface contaminants. The intensities of the fragment PDMS ions and the intact DDB ion increase, but the hydrocarbon fragment ions from PS and DDB decrease as the energy per atom (Eatom) of the Ar cluster projectile decreases from ~8 to ~4 eV. These results indicate that tuning Eatom can maximize the surface sensitivity and the sensitivity for intact ions. Copyright © 2012 John Wiley & Sons, Ltd.

SIMS studies of titanium biomaterial hydrogenation after magnetoelectropolishing

Hydrogenation of the commercial purity (99%) CP Titanium Grade 2 biomaterial was investigated using the secondary ion mass spectrometry (SIMS) after two finishing electrochemical operations: standard electropolishing (EP), and magnetoelectropolishing (MEP). The SIMS depth profile analyses were carried out right after electropolishing, and again one month later. The mass spectrograms of secondary positive and negative ions were recorded. SIMS spectra reveal higher emission of positive O+, Ti+ and TiO+ secondary ions which corresponds to higher oxidation of MEP sample. Negative spectra show higher concentration of compounds containing carbon on MEP sample than those of EP sample. Lower content of hydrogen in the near-surface layers in the MEP sample right after the process was revealed. It was found that the concentrations of oxygen and hydrogen alter in time. One month of storage in air causes the contents of hydrogen in EP and MEP samples to be equalized. The SIMS studies performed after a one month period show that the differences in hydrogen contents between EP and MEP samples are not significant.

Chemometric and Multivariate Statistical Analysis of Time-of-Flight Secondary Ion Mass Spectrometry Spectra from Complex Cu–Fe Sulfides

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.