ORCID Profile
0000-0001-8377-4703
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Biomaterials | Materials Engineering | Condensed Matter Physics—Electronic And Magnetic Properties; | Characterisation of Biological Macromolecules | Medicinal and Biomolecular Chemistry | Mathematical Physics | Colloid And Surface Chemistry | Macromolecular and Materials Chemistry | Biomedical Engineering | Chemical Sciences Not Elsewhere Classified | Physical Chemistry (Incl. Structural) | Biotechnology Not Elsewhere Classified | Polymers | Composite Materials | Synchrotrons; Accelerators; Instruments and Techniques | Materials Engineering not elsewhere classified | Biochemistry and Cell Biology not elsewhere classified | Biological Physics | Other Physical Sciences | Interdisciplinary Engineering Not Elsewhere Classified | Biosensor Technologies | Metals and Alloy Materials | Materials Engineering Not Elsewhere Classified | Condensed Matter Physics—Structural Properties
ANZSRC Socio-Economic Objective (SEO)
Physical sciences | Expanding Knowledge in the Chemical Sciences | Fabricated metal products not elsewhere classified | Expanding Knowledge in the Physical Sciences | Plastic products (incl. Construction materials) | Other | Environmentally Sustainable Manufacturing not elsewhere classified | Diagnostic methods | Integrated circuits and devices | Chemical sciences | Basic Metal Products (incl. Smelting, Rolling, Drawing and Extruding) not elsewhere classified | Scientific instrumentation | Polymeric materials (e.g. paints) | Expanding Knowledge in the Earth Sciences | Structural Metal Products | Expanding Knowledge in Engineering | Expanding Knowledge in the Biological Sciences |
Related Links
Publications
Ageing of surface treated thermoplastic polyolefins
Publisher: Elsevier BV
Date: 09-2013
High-Throughput Production of Chromium(III) Complexes for Antibody Immobilization
Publisher: American Chemical Society (ACS)
Date: 05-10-2016
DOI: 10.1021/ACS.ANALCHEM.6B02531
Abstract: A robot-assisted high-throughput methodology was employed to produce chromium(III) complexes suitable for the surface modification of the commercially available PerkinElmer Optiplate96 well plate for use in enzyme-linked immunosorbent assays (ELISAs). The complexes were immobilized to the native functionality of the well plate and first screened using a horseradish-peroxidase-tagged (HRP) mouse antibody to quantify binding. The top "hits" were further assessed for their ability to present the antibody in a functional state using an ELISA. "Hits" from the second screen yielded four complexes capable of improving the signal intensity of the ELISA by greater than 500%. The metal/base ratio of these complexes was also investigated, and we isolated the most stable and reproducible candidate, [Cr(OH)
Scaled-up production of multi-walled carbon nanotubes using catalytic chemical vapour deposition
Publisher: IEEE
Date: 07-2006
Encapsulation of Mitoxantrone within Cucurbit[8]uril Decreases Toxicity and Enhances Survival in a Mouse Model of Cancer
Publisher: American Chemical Society (ACS)
Date: 28-04-2017
The influence of hydroxyl group concentration on epoxy–aluminium bond durability
Publisher: Informa UK Limited
Date: 2004
Orientation and characterization of immobilized antibodies for improved immunoassays (Review)
Publisher: American Vacuum Society
Date: 16-03-2017
DOI: 10.1116/1.4978435
Abstract: Orientation of surface immobilized capture proteins, such as antibodies, plays a critical role in the performance of immunoassays. The sensitivity of immunodiagnostic procedures is dependent on presentation of the antibody, with optimum performance requiring the antigen binding sites be directed toward the solution phase. This review describes the most recent methods for oriented antibody immobilization and the characterization techniques employed for investigation of the antibody state. The introduction describes the importance of oriented antibodies for maximizing biosensor capabilities. Methods for improving antibody binding are discussed, including surface modification and design (with sections on surface treatments, three-dimensional substrates, self-assembled monolayers, and molecular imprinting), covalent attachment (including targeting amine, carboxyl, thiol and carbohydrates, as well as “click” chemistries), and (bio)affinity techniques (with sections on material binding peptides, biotin-streptavidin interaction, DNA directed immobilization, Protein A and G, Fc binding peptides, aptamers, and metal affinity). Characterization techniques for investigating antibody orientation are discussed, including x-ray photoelectron spectroscopy, spectroscopic ellipsometry, dual polarization interferometry, neutron reflectometry, atomic force microscopy, and time-of-flight secondary-ion mass spectrometry. Future perspectives and recommendations are offered in conclusion.
Resonant Acoustic Mixing Method to Produce Lipid-Based Liquid-Crystal Nanoparticles
Publisher: American Chemical Society (ACS)
Date: 06-05-2021
Polypropylene microtitre plates modified with [Cr(OH) 6 ] 3− for enhanced ELISA sensitivity
Publisher: Elsevier BV
Date: 07-2017
DOI: 10.1016/J.JIM.2017.03.015
Abstract: Chromium solutions have been used as wet chemical modifiers for polymer microtitre plates used in improving immunoassay performance. However, polypropylene has been excluded from the list of potentially modifiable substrates (AnteoTechnologies, 2015). Here we show that untreated polypropylene microtitre plates can indeed be modified using a [Cr(OH)
Chromium functionalized diglyme plasma polymer coating enhances enzyme-linked immunosorbent assay performance
Publisher: American Vacuum Society
Date: 10-11-2016
DOI: 10.1116/1.4967442
Abstract: Ensuring the optimum orientation, conformation, and density of substrate-bound antibodies is critical for the success of sandwich enzyme-linked immunosorbent assays (ELISAs). In this work, the authors utilize a diethylene glycol dimethyl ether plasma polymer (DGpp) coating, functionalized with chromium within a 96 well plate for the enhanced immobilization of a capture antibody. For an equivalent amount of bound antibody, a tenfold improvement in the ELISA signal intensity is obtained on the DGpp after incubation with chromium, indicative of improved orientation on this surface. Time-of-flight secondary-ion-mass-spectrometry (ToF-SIMS) and principal component analysis were used to probe the molecular species at the surface and showed ion fragments related to lysine, methionine, histidine, and arginine coupled to chromium indicating candidate antibody binding sites. A combined x-ray photoelectron spectroscopy and ToF-SIMS analysis provided a surface molecular characterization that demonstrates antibody binding via the chromium complex. The DGpp+Cr surface treatment holds great promise for improving the efficacy of ELISAs.
Addition of carboxylic acids modifies phosphate sorption on soil and boehmite surfaces: A solution chemistry and XANES spectroscopy study
Publisher: Elsevier BV
Date: 02-2009
DOI: 10.1016/J.JCIS.2008.10.044
Abstract: Soil acidification is a globally significant agricultural issue, as the plant availability of phosphorus (P) is decreased through increased P sorption onto aluminium (Al) hydroxides and other solid phase binding sites. X-ray absorption near edge structure (XANES) spectroscopy generated new information on the speciation of Al and P in the presence of carboxylic acids on soil and boehmite (gamma-AlOOH) surfaces. XANES spectra were acquired in the soft X-ray regime at the P and Al L(2,3)-edges, and the Al K-edge, respectively. Adding oxalic acid to soil enhanced Al dissolution and exposed previously occluded soil P, while hydroxybenzoic and coumaric acids did not compete with P for surface binding sites. Boehmite strongly adsorbed carboxylic acids in the absence of applied phosphorus. However, when P was applied with carboxylic acids, the carboxylics were unable to compete with P for binding, especially hydroxybenzoic and coumaric acids. Using XANES in both total electron yield and fluorescence yield modes provided valuable information on both surface and near-surface processes of P and Al due to different information depths. The Al K-edge XANES provided baseline information on the solid-phase matrix. XANES in total electron yield mode and at the P L-edge shows promise for speciation of elements on soil surfaces due to enhanced sensitivity for speciation of surface-adsorbed species compared to the commonly used P K-edge XANES.
Multi-Walled Carbon Nanotubes Grown from Chemical Vapor: Links between Atomic near Range Order and Growth Parameters
Publisher: American Chemical Society (ACS)
Date: 23-02-2009
DOI: 10.1021/JP8092868
Tailoring the surface chemistry of carbon fiber and E-glass composites for improved adhesion
Publisher: Wiley
Date: 05-04-2011
DOI: 10.1002/SIA.3645
Adhesion of polymers
Publisher: Elsevier BV
Date: 09-2009
Comparative analysis of Ti3SiC2 and associated compounds using x-ray diffraction and x-ray photoelectron spectroscopy
Publisher: IOP Publishing
Date: 18-06-2002
Multi-Dimensional Machine Learning Analysis of Polyaniline Films Using Stitched Hyperspectral ToF-SIMS Data
Publisher: American Chemical Society (ACS)
Date: 12-05-2023
A chemiluminescent sandwich ELISA enhancement method using a chromium (III) coordination complex
Publisher: Elsevier BV
Date: 11-2016
DOI: 10.1016/J.JIM.2016.09.003
Abstract: Enzyme linked immunosorbent assays (ELISAs) are employed for the detection and quantification of antigens from biological sources such as serum and cell culture media. A sandwich ELISA is dependent on the immobilization of a capture antibody, or antibody fragment, and the effective presentation of its antigen binding sites. Immobilization to common microtitre plates relies on non-specific interactions of the capture protein with a surface that may result in unfavourable orientation and conformation, compromising ELISA signal strength and performance. We have developed a wet chemical surface activation method that utilizes a chromium (III) solution to immobilize native, non-tagged, capture antibodies on commercially available microtitre plates. Antibodies captured by this method had increased antigen binding, particularly from dilute antibody solutions, relative to antibodies adsorbed directly to the plate surface. A variety of monoclonal antibodies with complementary antigen systems were used to demonstrate improvements in ELISA signal and reproducibility. The simplicity and versatility of this method should enable ELISA enhancement in assays where chemiluminescence is used as the detection method.
A novel pH sensor based on hydroquinone monosulfonate-doped conducting polypyrrole
Publisher: Elsevier BV
Date: 12-2008
Carboxylic Acids Affect Sorption and Micro-Scale Distribution of Phosphorus in an Acidic Soil
Publisher: Wiley
Date: 2011
Surface Adsorbed Antibody Characterization Using ToF-SIMS with Principal Component Analysis and Artificial Neural Networks
Publisher: American Chemical Society (ACS)
Date: 19-08-2016
DOI: 10.1021/ACS.LANGMUIR.6B02312
Abstract: Artificial neural networks (ANNs) form a class of powerful multivariate analysis techniques, yet their routine use in the surface analysis community is limited. Principal component analysis (PCA) is more commonly employed to reduce the dimensionality of large data sets and highlight key characteristics. Herein, we discuss the strengths and weaknesses of PCA and ANNs as methods for investigation and interpretation of a complex multivariate s le set. Using time-of-flight secondary ion mass spectrometry (ToF-SIMS) we acquired spectra from an antibody and its proteolysis fragments with three primary-ion sources to obtain a panel of 72 spectra and a characteristic peak list of 775 fragment ions. We describe the use of ANNs as a means to interpret the ToF-SIMS spectral data, highlight the optimal neural network design and computational parameters, and discuss the technique limitations. Further, employing Bi3(+) as the primary-ion source, ANNs can accurately classify antibody fragments from the parent antibody based on ToF-SIMS spectra.
Surface properties of polypropylene following a novel industrial surface-treatment process
Publisher: Wiley
Date: 11-2008
DOI: 10.1002/SIA.2924
Facile Deposition of Mesoporous PbI2 through DMF:DMSO Solvent Engineering for Sequentially Deposited Metal Halide Perovskites
Publisher: American Chemical Society (ACS)
Date: 26-03-2020
Analyzing 3D hyperspectral TOF-SIMS depth profile data using self-organizing map-relational perspective mapping
Publisher: American Vacuum Society
Date: 11-2020
DOI: 10.1116/6.0000614
Abstract: The advantages of applying multivariate analysis to mass spectrometry imaging (MSI) data have been thoroughly demonstrated in recent decades. The identification and visualization of complex relationships between pixels in a hyperspectral data set can provide unique insights into the underlying surface chemistry. It is now recognized that most MSI data contain nonlinear relationships, which has led to increased application of machine learning approaches. Previously, we exemplified the use of the self-organizing map (SOM), a type of artificial neural network, for analyzing time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images. Recently, we developed a novel methodology, SOM-relational perspective mapping (RPM), which incorporates the algorithm RPM to improve visualization of the SOM for 2D TOF-SIMS images. Here, we use SOM-RPM to characterize and interpret 3D TOF-SIMS depth profile data, voxel-by-voxel. An organic Irganox™ multilayer standard s le was depth profiled using TOF-SIMS, and SOM-RPM was used to create 3D similarity maps of the depth-profiled s le, in which the mass spectral similarity of in idual voxels is modeled with color similarity. We used this similarity map to segment the data into spatial features, demonstrating that the unsupervised method meaningfully differentiated between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films. The method also identified unique clusters at the surface associated with environmental exposure and s le degradation. Key fragment ions characteristic of each cluster were identified, tying clusters to their underlying chemistries. SOM-RPM has the demonstrable ability to reduce vast data sets to simple 3D visualizations that can be used for clustering data and visualizing the complex relationships within.
Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF‐SIMS and Self‐Organizing maps
Publisher: Wiley
Date: 15-02-2023
Abstract: Biofilm formation is a major cause of hospital‐acquired infections. Research into biofilm‐resistant materials is therefore critical to reduce the frequency of these events. Polymer microarrays offer a high‐throughput approach to enable the efficient discovery of novel biofilm‐resistant polymers. Herein, bacterial attachment and surface chemistry are studied for a polymer microarray to improve the understanding of Pseudomonas aeruginosa biofilm formation on a erse set of polymeric surfaces. The relationships between time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data and biofilm formation are analyzed using linear multivariate analysis (partial least squares [PLS] regression) and a nonlinear self‐organizing map (SOM). The SOM models revealed several combinations of fragment ions that are positively or negatively associated with bacterial biofilm formation, which are not identified by PLS. With these insights, a second PLS model is calculated, in which interactions between key fragments (identified by the SOM) are explicitly considered. Inclusion of these terms improved the PLS model performance and shows that, without such terms, certain key fragment ions correlated with bacterial attachment may not be identified. The chemical insights provided by the combination of PLS regression and SOM will be useful for the design of materials that support negligible pathogen attachment.
ToF-SIMS and Principal Component Analysis Investigation of Denatured, Surface-Adsorbed Antibodies
Publisher: American Chemical Society (ACS)
Date: 14-10-2016
DOI: 10.1021/ACS.LANGMUIR.6B02754
Abstract: Antibody denaturation at solid-liquid interfaces plays an important role in the sensitivity of protein assays such as enzyme-linked immunosorbent assays (ELISAs). Surface immobilized antibodies must maintain their native state, with their antigen binding (Fab) region intact, to capture antigens from biological s les and permit disease detection. In this work, two identical s le sets were prepared with whole antibody IgG, F(ab')
Rapid evaluation of immobilized immunoglobulins using automated mass-segmented ToF-SIMS
Publisher: AIP Publishing
Date: 11-2019
DOI: 10.1063/1.5121450
Abstract: Surface interactions largely control how biomaterials interact with biology and how many other types of materials function in industrial applications. ToF-SIMS analysis is extremely useful for interrogating the surfaces of complex materials and shows great promise in analyzing biological s les. Previously, the authors demonstrated that segmentation (between 1 and 0.005 m/z mass bins) of the mass spectral axis can be used to differentiate between polymeric materials with both very similar and dissimilar molecular compositions. Here, the same approach is applied for the analysis of proteins on surfaces, focusing on the effect of binding and orientation of an antibody on the resulting ToF-SIMS spectrum. Due to the complex nature of the s les that contain combinations of only 20 amino acids differing in sequence, it is enormously challenging and prohibitively time-consuming to distinguish the minute variances presented in each dataset through manual analysis alone. Herein, the authors describe how to apply the newly developed rapid data analysis workflow to previously published ToF-SIMS data for complex biological materials, immobilized antibodies. This automated method reduced the analysis time by two orders of magnitudes while enhancing data quality and allows the removal of any user bias. The authors used mass segmentation at 0.005 m/z over a 1–300 mass range to generate 60 000 variables. In contrast to the previous manual binning approach, this method captures the entire mass range of the spectrum resulting in an information-rich dataset rather than specifically selected mass spectral peaks. This work constitutes an additional proof of concept that rapid and automated data analyses involving mass-segmented ToF-SIMS spectra can efficiently and robustly analyze a broader range of complex materials, ranging from generic polymers to complicated biological s les. This automated analysis method is also ideally positioned to provide data to train machine learning models of surface-property relationships that can greatly enhance the understanding of how the surface interacts with biology and provides more accurate and robust quantitative predictions of the biological properties of new materials.
Optimal machine learning models for robust materials classification using ToF-SIMS data
Publisher: Elsevier BV
Date: 09-2019
Visualizing ToF-SIMS Hyperspectral Imaging Data Using Color-Tagged Toroidal Self-Organizing Maps
Publisher: American Chemical Society (ACS)
Date: 24-09-2019
DOI: 10.1021/ACS.ANALCHEM.9B03322
Abstract: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface characterization technique capable of producing high spatial resolution hyperspectral images, in which each pixel comprises an entire mass spectrum. Such images can provide insight into the chemical composition across a surface. However, issues arise due to the size and complexity of the data produced. Data are particularly complicated for biological s les, primarily due to overlapping spectra produced by similar components. The traditional approach of selecting in idual ion peaks as representative of particular components is insufficient for such complex data sets. Multivariate analysis (MVA) can help to overcome this significant hurdle. We demonstrate that Kohonen self-organizing maps (SOMs) with a toroidal topology can be used to analyze a ToF-SIMS hyperspectral imaging data set and identify spectral similarities between pixels. We present a method for color-tagging the toroidal SOM output, which reduces the entire data set to a single RGB image in which similar pixels-based on their associated mass spectra-are assigned a similar color. This method was exemplified using a ToF-SIMS image of dried large multilamellar vesicles (LMVs), loaded with the antibiotic cefditoren pivoxil (CP). We successfully identified CP-loaded and empty LMVs without the need for any prior knowledge of the s le, despite their highly similar spectra. We also identified which specific ion peaks were most important in differentiating the two LMV populations. This approach is entirely unsupervised and requires minimal experimenter input. It was developed with the aim of providing a user-friendly yet sophisticated workflow for understanding complex biological s les using ToF-SIMS images.
Exploring molecular changes at the surface of polypropylene after accelerated thermomolecular adhesion treatments
Publisher: American Chemical Society (ACS)
Date: 30-04-2010
DOI: 10.1021/AM1001376
Abstract: A central composite rotatable design (CCRD) method was used to investigate the performance of the accelerated thermomolecular adhesion process (ATmaP), at different operating conditions. ATmaP is a modified flame-treatment process that features the injection of a coupling agent into the flame to impart a tailored molecular surface chemistry on the work piece. In this study, the surface properties of treated polypropylene were evaluated using X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). All s les showed a significant increase in the relative concentration of oxygen (up to 12.2%) and nitrogen (up to 2.4%) at the surface in comparison with the untreated s le (0.7% oxygen and no detectable nitrogen) as measured by XPS. ToF-SIMS and principal components analysis (PCA) showed that ATmaP induced multiple reactions at the polypropylene surface such as chain scission, oxidation, nitration, condensation, and molecular loss, as indicated by changes in the relative intensities of the hydrocarbon (C(3)H(7)(+), C(3)H(5)(+), C(4)H(7)(+), and C(5)H(9)(+)), nitrogen and oxygen-containing secondary ions (C(2)H(3)O(+), C(3)H(8)N(+), C(2)H(5)NO(+), C(3)H(6)NO(+), and C(3)H(7)NO(+)). The increase in relative intensity of the nitrogen oxide ions (C(2)H(5)NO(+) and C(3)H(7)NO(+)) correlates with the process of incorporating oxides of nitrogen into the surface as a result of the injection of the ATmaP coupling agent.
Adsorption of the trimethylamine adduct of aluminium hydride on silicon oxide
Publisher: Royal Society of Chemistry (RSC)
Date: 1992
DOI: 10.1039/C39920001423
Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems
Publisher: American Vacuum Society
Date: 03-2022
DOI: 10.1116/6.0001590
Abstract: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.
A sensitive and highly stable polypyrrole-based pH sensor with hydroquinone monosulfonate and oxalate co-doping
Publisher: Elsevier BV
Date: 05-2009
Orientation and conformation of anti-CD34 antibody immobilised on untreated and plasma treated polycarbonate
Publisher: Elsevier BV
Date: 04-0006
DOI: 10.1016/J.ACTBIO.2015.02.027
Abstract: The conformation and orientation of proteins immobilised on synthetic materials determine their ability to bind their antigens and thereby the sensitivity of the microarrays and biosensors employing them. Plasma immersion ion implantation (PIII) of polymers significantly increases both their wettability and protein binding capacity. This paper addresses the hypothesis that a PIII treated polymer surface modifies the native protein conformation less significantly than a more hydrophobic untreated surface and that the differences in surface properties also affect the protein orientation. To prove this, the orientation and conformation of rat anti-mouse CD34 antibody immobilized on untreated and PIII treated polycarbonate (PC) were investigated using ToF-SIMS and FTIR-ATR spectroscopy. Analysis of the primary structure of anti-CD34 antibody and principal component analysis of ToF-SIMS data were applied to detect the difference in the orientation of the antibody attached to untreated and PIII treated PC. The difference in the antibody conformation was analysed using deconvolution of the Amide I peak (in FTIR-ATR spectra) and curve-fitting. It was found that compared to the PIII treated s le, the antibody immobilized on the untreated PC s le has a secondary structure with a lower fraction of β-sheets and a higher fraction of α-helices and disordered fragments. Also, it was found that anti-CD34 antibody has a higher tendency to occur in the inactive 'tail-up' orientation when immobilized on an untreated PC surface than on a PIII treated surface. These findings confirm the above hypothesis.
Surface attachment of horseradish peroxidase to nylon modified by plasma‐immersion ion implantation
Publisher: Wiley
Date: 10-01-2011
DOI: 10.1002/APP.33355
Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder
Publisher: American Chemical Society (ACS)
Date: 26-05-2022
DOI: 10.1021/ACS.ANALCHEM.1C05453
Abstract: Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.
Surface modification of boron fibres for improved strength in composite materials
Publisher: Informa UK Limited
Date: 2005
Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix
Publisher: American Chemical Society (ACS)
Date: 27-09-2018
DOI: 10.1021/ACS.ANALCHEM.8B01951
Abstract: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is advancing rapidly, providing instruments with growing capabilities and resolution. The data sets generated by these instruments are likewise increasing dramatically in size and complexity. Paradoxically, methods for efficient analysis of these large, rich data sets have not improved at the same rate. Clearly, more effective computational methods for analysis of ToF-SIMS data are becoming essential. Several research groups are customizing standard multivariate analytical tools to decrease computational demands, provide user-friendly interfaces, and simplify identification of trends and features in large ToF-SIMS data sets. We previously applied mass segmented peak lists to data from PMMA, PTFE, PET, and LDPE. Self-organizing maps (SOMs), a type of artificial neural network (ANN), classified the polymers based on their molecular composition and primary ion probe type more effectively than simple PCA. The effectiveness of this approach led us to question whether it would be useful in distinguishing polymers that were very similar. How sensitive is the technique to changes in polymer chemical structure and composition? To address this question, we generated ToF-SIMS ion peak signatures for seven nylon polymers with similar chemistries and used our up-binning and SOM approach to classify and cluster the polymers. The widely used linear PCA method failed to separate the s les. Supervised and unsupervised training of SOMs using positive or negative ion mass spectra resulted in effective classification and separation of the seven nylon polymers. Our SOM classification method has proven to be tolerant of minor s le irregularities, s le-to-s le variations, and inherent data limitations including spectral resolution and noise. We have demonstrated the potential of machine learning methods to analyze ToF-SIMS data more effectively than traditional methods. Such methods are critically important for future complex data analysis and provide a pipeline for rapid classification and identification of features and similarities in large data sets.
C-section and systemic inflammation synergize to disrupt the neonatal gut microbiota and brain development in a model of prematurity
Publisher: Cold Spring Harbor Laboratory
Date: 23-10-2023
Effect of mass segment size on polymer ToF-SIMS multivariate analysis using a universal data matrix
Publisher: Elsevier BV
Date: 06-2019
Tof-sims investigation of epoxy resin curing reaction at different resin to hardener ratios
Publisher: Wiley
Date: 26-08-2008
DOI: 10.1002/APP.28813
Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly
Publisher: American Chemical Society (ACS)
Date: 19-05-2023
Surface Immobilized Antibody Orientation Determined using ToF-SIMS and Multivariate Analysis
Publisher: Elsevier BV
Date: 06-2017
DOI: 10.1016/J.ACTBIO.2017.03.038
Abstract: Antibody orientation at solid phase interfaces plays a critical role in the sensitive detection of biomolecules during immunoassays. Correctly oriented antibodies with solution-facing antigen binding regions have improved antigen capture as compared to their randomly oriented counterparts. Direct characterization of oriented proteins with surface analysis methods still remains a challenge however surface sensitive techniques such as Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) provide information-rich data that can be used to probe antibody orientation. Diethylene glycol dimethyl ether plasma polymers (DGpp) functionalized with chromium (DGpp+Cr) have improved immunoassay performance that is indicative of preferential antibody orientation. Herein, ToF-SIMS data from proteolytic fragments of anti-EGFR antibody bound to DGpp and DGpp+Cr are used to construct artificial neural network (ANN) and principal component analysis (PCA) models indicative of correctly oriented systems. Whole antibody s les (IgG) test against each of the models indicated preferential antibody orientation on DGpp+Cr. Cross-reference between ANN and PCA models yield 20 mass fragments associated with F(ab') Controlled orientation of antibodies at solid phases is critical for maximizing antigen detection in biosensors and immunoassays. Surface-sensitive techniques (such as ToF-SIMS), capable of direct characterization of surface immobilized and oriented antibodies, are under-utilized in current practice. Selection of a small number of mass fragments for analysis, typically pertaining to amino acids, is commonplace in literature, leaving the majority of the information-rich spectra unanalyzed. The novelty of this work is the utilization of a comprehensive, unbiased mass fragment list and the employment of principal component analysis (PCA) and artificial neural network (ANN) models in a unique methodology to prove antibody orientation. This methodology is of significant and broad interest to the scientific community as it is applicable to a range of substrates and allows for direct, label-free characterization of surface bound proteins.
XPSSurfA: An open collaborative XPS data repository using the CMSShub platform
Publisher: Wiley
Date: 23-03-2018
DOI: 10.1002/SIA.6417
Long-Term Stability of Metallic Iron inside Carbon Nanotubes
Publisher: American Chemical Society (ACS)
Date: 11-10-2011
DOI: 10.1021/JP207939S
Time-of-flight secondary ion mass spectrometry investigation of epoxy resin curing behavior in real Time
Publisher: Wiley
Date: 29-04-2009
DOI: 10.1002/APP.30136
Peptide hormone relaxin: from bench to bedside
Publisher: American Physiological Society
Date: 06-2018
DOI: 10.1152/AJPREGU.00276.2017
Abstract: The peptide hormone relaxin has numerous roles both within and independent of pregnancy and is often thought of as a “pleiotropic hormone.” Relaxin targets several tissues throughout the body, and has many functions associated with extracellular matrix remodeling and the vasculature. This review considers the potential therapeutic applications of relaxin in cervical ripening, in vitro fertilization, preecl sia, acute heart failure, ischemia-reperfusion, and cirrhosis. We first outline the animal models used in preclinical studies to progress relaxin into clinical trials and then discuss the findings from these studies. In many cases, the positive outcomes from preclinical animal studies were not replicated in human clinical trials. Therefore, the focus of this review is to evaluate the various animal models used to develop relaxin as a potential therapeutic and consider the limitations that must be addressed in future studies. These include the use of human relaxin in animals, duration of relaxin treatment, and the appropriateness of the clinical conditions being considered for relaxin therapy.
Investigation of the postcure reaction and surface energy of epoxy resins using Time-of-flight secondary ion mass spectrometry and Contact-angle measurements
Publisher: Wiley
Date: 29-04-2009
DOI: 10.1002/APP.30135
Multivariate analysis of ToF‐SIMS data using mass segmented peak lists
Publisher: Wiley
Date: 10-05-2018
DOI: 10.1002/SIA.6462
Large Area Ultrathin InN and Tin Doped InN Nanosheets Featuring 2D Electron Gases
Publisher: American Chemical Society (ACS)
Date: 04-04-2022
Abstract: Indium nitride (InN) has been of significant interest for creating and studying two-dimensional electron gases (2DEG). Herein we demonstrate the formation of 2DEGs in ultrathin doped and undoped 2D InN nanosheets featuring high carrier mobilities at room temperature. The synthesis is carried out via a two-step liquid metal-based printing method followed by a microwave plasma-enhanced nitridation reaction. Ultrathin InN nanosheets with a thickness of ∼2 ± 0.2 nm were isolated over large areas with lateral dimensions exceeding centimeter scale. Room temperature Hall effect measurements reveal carrier mobilities of ∼216 and ∼148 cm
Use of pre-defined architectures for incorporation of aligned carbon nanotubes into epoxy resin
Publisher: IEEE
Date: 02-2008
ToF-SIMS and Machine Learning for Single-Pixel Molecular Discrimination of an Acrylate Polymer Microarray
Publisher: American Chemical Society (ACS)
Date: 04-2020
Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data
Publisher: American Chemical Society (ACS)
Date: 02-07-2020
Solid source growth of graphene with Ni-Cu catalysts: towards high quality in situ graphene on silicon
Publisher: IOP Publishing
Date: 02-2017
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