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Concept: Secondary ion mass spectrometry


Biological materials with hierarchically laminated structures usually exhibit a good synergy between strength and fracture toughness. Here, we show that a bio-inspired (polyelectrolyte (PE)/TiO2)4 nanolayered composite with a thickness ratio of TiO2 and amorphous PE layers of about 1.1 has been prepared successfully on Si substrates by layer-by-layer self-assembly and chemical bath deposition methods. Microstructures of the nanolayered composite were investigated by scanning electron microscopy, secondary ion mass spectroscopy, and high-resolution transmission microscopy. Mechanical performance of the composite was characterized by instrumented indentation. The composite consisting of 17.9-nm-thick nanocrystalline TiO2 and 16.4-nm-thick amorphous PE layers has a strength of about 245 MPa, which is close to that of shells, while the fracture toughness of the composite, KIC = 1.62 +/- 0.30 MPa . m1/2, is evidently higher than that of the bulk TiO2. A possible strategy to build the composite at nanoscale for high mechanical performance was addressed.

Concepts: Electron, Electron microscope, Fracture mechanics, Materials science, Transmission electron microscopy, Scanning electron microscope, Secondary ion mass spectrometry, Fracture toughness


Photoluminescence (PL) was used to estimate the concentration of point defects in GaN. The results are compared with data from positron annihilation spectroscopy (PAS), secondary ion mass spectrometry (SIMS), and deep level transient spectroscopy (DLTS). Defect-related PL intensity in undoped GaN grown by hydride vapor phase epitaxy increases linearly with the concentration of related defects only up to 10(16) cm(-3). At higher concentrations, the PL intensity associated with individual defects tends to saturate, and accordingly, does not directly correlate with the concentration of defects. For this reason, SIMS analysis, with relatively high detection limits, may not be helpful for classifying unidentified point defects in GaN. Additionally, we highlight challenges in correlating defects identified by PL with those by PAS and DLTS methods.

Concepts: Electron, Spectroscopy, Positron, Analytical chemistry, Epitaxy, Phase diagram, Secondary ion mass spectrometry, Deep-level transient spectroscopy


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.

Concepts: Cell, Enzyme, Mass spectrometry, Peptide, In-gel digestion, Ion source, Peptide sequence, Secondary ion mass spectrometry


The uneven cholesterol distribution among organelles and within the plasma membrane is postulated to be critical for proper cellular function. To study how interactions between cholesterol and specific lipid species contribute to the uneven cholesterol distribution between and within cellular membranes, model lipid membranes are frequently employed. Although the cholesterol distributions within membranes can be directly imaged without labels by using time-of-flight secondary ion mass spectrometry (TOF-SIMS), quantifying the cholesterol abundance at specific membrane locations in a label-free manner remains a challenge. Here, partial least-squares regression (PLSR) of TOF-SIMS data is used to quantitatively measure the local molar percentage (mol%) of cholesterol within supported lipid membranes. Using TOF-SIMS data from lipid membranes of known composition, a PLSR model was constructed that correlated the spectral variation to the mol% cholesterol in the membrane. The PLSR model was then used to measure the mol% cholesterol in test membranes, and to measure cholesterol exchange between vesicles and supported lipid membranes. The accuracy of these measurements was assessed by comparison to the mol% cholesterol measured with conventional assays. By using this TOF-SIMS/PLSR approach to quantify the mol% cholesterol with location specificity, a better understanding of how the regional lipid composition influences cholesterol abundance and exchange in membranes may be obtained.

Concepts: Mass spectrometry, Cell membrane, Measurement, Concentration, Least squares, Lipid bilayer, Systems of measurement, Secondary ion mass spectrometry


The use of multivariate analysis (MVA) methods in the processing of time-of-flight secondary ion mass spectrometry (ToF-SIMS) data has become increasingly more common. MVA presents a powerful set of tools to aid the user in processing data from complex, multicomponent surfaces such as biological materials and biosensors. When properly used, MVA can help the user identify the major sources of differences within a sample or between samples, determine where certain compounds exist on a sample, or verify the presence of compounds that have been engineered into the surface. Of all the MVA methods, principal component analysis (PCA) is the most commonly used and forms an excellent starting point for the application of many of the other methods employed to process ToF-SIMS data. Herein we discuss the application of PCA and other MVA methods to multicomponent ToF-SIMS data and provide guidelines on their application and use.

Concepts: Spectroscopy, Mass spectrometry, Multivariate statistics, Principal component analysis, Computer program, Linear discriminant analysis, Secondary ion mass spectrometry, The Unscrambler


Cardiovascular diseases are the world’s number one cause of death, accounting for 17.1 million deaths a year. New high-resolution molecular and structural imaging strategies are needed to understand underlying pathophysiological mechanism. The aim of our study is (1) to provide a molecular basis of the heart animal model through the local identification of biomolecules by mass spectrometry imaging (MSI) (three-dimensional (3D) molecular reconstruction), (2) to perform a cross-species validation of secondary ion mass spectrometry (SIMS)-based cardiovascular molecular imaging, and (3) to demonstrate potential clinical relevance by the application of this innovative methodology to human heart specimens. We investigated a MSI approach using SIMS on the major areas of a rat and mouse heart: the pericardium, the myocardium, the endocardium, valves, and the great vessels. While several structures of the heart can be observed in individual two-dimensional sections analyzed by metal-assisted SIMS imaging, a full view of these structures in the total heart volume can be achieved only through the construction of the 3D heart model. The images of 3D reconstruction of the rat heart show a highly complementary localization between Na(+), K(+), and two ions at m/z 145 and 667. Principal component analysis of the MSI data clearly identified different morphology of the heart by their distinct correlated molecular signatures. The results reported here represent the first 3D molecular reconstruction of rat heart by SIMS imaging. Figure Workflow of the 3D reconstruction. A Tissue section, B gold deposition is done by sputter coating, C, C1 SIMS-ToF mass analyzer, C, C2 mass spectral peaks, C, C3 datacube images; D, E Reconstruction of the heart showing 3D-spatial distributions of three different ions 145 m/z (red), 23 m/z (green), and 39 m/z (blue); F coregistration of 40 individual MS imaging.

Concepts: Mass spectrometry, Heart, Principal component analysis, Ion source, MALDI imaging, Mass spectrometry imaging, Secondary ion mass spectrometry, Mass-to-charge ratio


When organic matter is mixed on a nanometer scale with clay minerals, the individual D/H ratios of the two H-bearing phases cannot be directly measured even with the nominal spatial resolution of nanoscale secondary ion mass spectrometry (NanoSIMS, 50-100 nm). To overcome this limitation, a new analytical protocol is proposed based on the deconvolution of the D(-)/H(-) and (16)OD(-)/(16)OH(-) ionic ratios measured by NanoSIMS. Indeed, since the yields of H(-) and (16)OH(-) are different for organics and clays, it should be theoretically possible to determine the mixing ratio of these two components in the area analyzed by the ion probe. Using organics with different D/H ratios, the interdependence of the D(-)/H(-) and (16)OD(-)/(16)OH(-) ionic ratios was determined in pure samples. Then using the H(-) and (16)OH(-) yields and the isotopic ratios measured on pure organic matter and clays, the expected D(-)/H(-) and (16)OD(-)/(16)OH(-) variations as a function of the mixing proportions were determined. These numerical predictions are consistent with measurements on laboratory prepared mixtures of D-rich organic matter and D-poor phyllosilicates, validating both the proposed experimental protocol and its use for meteorites. With an improvement of the precision of the ionic ratios by a factor of 10, it should possible to expend this protocol to samples having natural terrestrial D/H variations. Such an improvement could be attainable with the development of synthetic deuterated reference samples.

Concepts: Mass spectrometry, Measurement, Chemistry, Analytical chemistry, Isotope, Secondary ion mass spectrometry, Clay, Mix


Abstract: Digital staining for the automated annotation of Mass Spectrometry Imaging (MSI) data has previously been achieved using state-of-the-art classifiers such as random forests or support vector machines (SVMs). However, the training of such classifiers requires an expert to label exemplary data in advance. This process is time-consuming and hence costly, especially if the tissue is heterogeneous. In theory, it may be sufficient to only label few highly representative pixels of an MS image, but it is not known a priori which pixels to select. This motivates active learning strategies in which the algorithm itself queries the expert by automatically suggesting promising candidate pixels of an MS image for labeling. Given a suitable querying strategy, the number of required training labels can be significantly reduced while maintaining classification accuracy. In this work, we propose active learning for convenient annotation of MSI data. We generalize a recently proposed active learning method to the multi-class case and combine it with the random forest classifier. Its superior performance over random sampling is demonstrated on Secondary Ion Mass Spectrometry data, making it an interesting approach for the classification of mass spectrometry images.

Concepts: Mass spectrometry, Machine learning, IMAGE, Statistical classification, Support vector machine, Label, Secondary ion mass spectrometry, Active learning


A minimally destructive technique for the determination of dyes in finished fibers provides an important tool for crime scene and other forensic investigations. The analytical power and the minimal sample consumption of time-of-flight-secondary ion mass spectrometric (TOF-SIMS) analysis provides the ability to obtain definitive molecular and elemental information relevant to fiber identification, including identification of dyes, from a very small volume of sample. For both fiber surface analysis and, with the aid of cryomicrotomy, fiber cross-section analysis, TOF-SIMS was used to identify various dyes in finished textile fibers. The analysis of C.I. Acid Blue 25 in nylon is presented as a representative example. The molecular ion of C.I. Acid Blue 25 with lower than 3% on weight-of-fiber (owf) dye loading cannot be identified on dyed nylon surfaces by TOF-SIMS using a Bi(3)(+) primary ion beam. Sputtering with C(60)(+) provided the ability to remove surface contamination as well as at least partially remove Bi-induced damage, resulting in a greatly improved signal-to-noise ratio for the Acid Blue 25 molecular ion. The use of C(60)(+) for damage removal in a cyclic manner along with Bi for data acquisition provided the ability to unambiguously identify Acid Blue 25 via its molecular ion at a concentration of 0.1% owf from both fiber surfaces and cross sections.

Concepts: Mass spectrometry, Molecule, Chemistry, Ion, Nylon, Fiber, Dye, Secondary ion mass spectrometry


Precise surface nanopatterning is a promising route for predictable control of cellular behavior on biomedical materials. There is currently a gap in taking such precision engineered surfaces from the laboratory to clinically relevant implant materials such as titanium (Ti). In this work, anodization of Ti surfaces was performed in combination with block copolymer templates to create highly ordered and tunable oxide nanopatterns. Secondary ion mass spectroscopy (SIMS) and X-ray photoelectron spectroscopy (XPS) analyses showed that the composition of the anodized structures was mainly titania with small amounts of nitrogen left from the block copolymer. It was further demonstrated that these nanopatterns can be superimposed on more complex shaped Ti surfaces such as microbeads, using the same technique. Human mesenchymal stem cells were cultured on Ti microbead surfaces, with and without nanopatterns, in vitro to study the effect of nanotopography on Ti surfaces. The results presented in this work demonstrate a promising method of producing highly defined and well-arranged surface nanopatterns on Ti implant surfaces.

Concepts: X-ray, Stem cell, Mesenchymal stem cell, Bone marrow, Stem cells, X-ray photoelectron spectroscopy, Dental implant, Secondary ion mass spectrometry