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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Characterization and response of newly developed high-grade glioma cultures to the tyrosine kinase inhibitors, erlotinib, gefitinib and imatinib

High-grade gliomas (HGG), are the most common aggressive brain tumours in adults. Inhibitors targeting growth factor signalling pathways in glioma have shown a low clinical response rate. To accurately evaluate response to targeted therapies further in vitro studies are necessary. Growth factor pathway expression using epidermal growth factor receptor (EGFR), mutant EGFR (EGFRvIII), platelet derived growth factor receptor (PDGFR), C-Kit and C-Abl together with phosphatase and tensin homolog (PTEN) expression and downstream activation of AKT and phosphorylated ribosomal protein S6 (P70S6K) was analysed in 26 primary glioma cultures treated with the tyrosine kinase inhibitors (TKIs) erlotinib, gefitinib and imatinib. Response to TKIs was assessed using 50% inhibitory concentrations (IC{sub 50}). Response for each culture was compared with the EGFR/PDGFR immunocytochemical pathway profile using hierarchical cluster analysis (HCA) and principal component analysis (PCA). Erlotinib response was not strongly associated with high expression of the growth factor pathway components. PTEN expression did not correlate with response to any of the three TKIs. Increased EGFR expression was associated with gefitinib response; increased PDGFR-{alpha} expression was associated with imatinib response. The results of this in vitro study suggest gefitinib and imatinib may have therapeutic potential in HGG tumours with a corresponding growth factor receptor expression profile. -- Highlights: Black-Right-Pointing-Pointer Non-responders had low EGFR expression, high PDGFR-{beta}, and a low proliferation rate. Black-Right-Pointing-Pointer PTEN is not indicative of response to a TKI. Black-Right-Pointing-Pointer Erlotinib response was not associated with expression of the proteins examined. Black-Right-Pointing-Pointer Imatinib-response correlated with expression of PDGFR-{alpha}. Black-Right-Pointing-Pointer Gefitinib response correlated with increased expression of EGFR.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

GPNMB/OA protein increases the invasiveness of human metastatic prostate cancer cell lines DU145 and PC3 through MMP-2 and MMP-9 activity

Non-metastatic glycoprotein melanoma protein B (GPNMB), also known as osteoactivin (OA) is expressed in a wide array of tumors and represents an emerging target for drug development. In this study, we investigated the role of GPNMB/OA in the progression of human metastatic DU145 and PC3 prostate cancer cells. GPNMB/OA contribution in PCa malignant phenotype has been analyzed by small interfering RNA-induced GPNMB/OA silencing. We found that following GPNMB/OA silencing the migration capability of both DU145 and PC3 cells, evaluated by using in vitro invasivity assay, as well as the metalloproteinases MMP-2 and MMP-9 activity were equally strongly inhibited. By contrast knocking down GPNMB/OA weakly attenuated cell proliferation rate of DU145, an effect that paralleled with an increase number of apoptotic cells. However, PC3 cell growth seems to be not affected by GPNMB/OA. Together, these data reveal that GPNMB/OA acts as a critical molecular mediator promoting the acquisition of the more aggressive, pro-metastatic phenotype distinctive of human DU145 and PC3 cell lines. - Highlights: • GPNMB/OA expression correlates with DU145 and PC3 cells malignant phenotype. • GPNMB/OA silencing affects the migration capability of both DU145 and PC3 cells. • GPNMB/OA increases invasiveness by up-regulating MMPs activity. • GPNMB/OA promotes DU145 and PC3 cells progression into a more aggressive phenotype.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Selective Plasmonic Gas Sensing: H2, NO2, and CO Spectral Discrimination by a Single Au-CeO2 Nanocomposite Film

A Au-CeO2 nanocomposite film has been investigated as a potential sensing element for high-temperature plasmonic sensing of H2, CO, and NO2 in an oxygen containing environment. The CeO2 thin film was deposited by molecular beam epitaxy (MBE) and Au was implanted into the as-grown film at an elevated temperature followed by high temperature annealing to form well-defined Au nanoclusters. The Au-CeO2 nanocomposite film was characterized by x-ray diffraction (XRD) and Rutherford backscattering spectrometry (RBS). For the gas sensing experiments, separate exposures to varying concentrations of H2, CO, and NO2 were performed at a temperature of 500°C in oxygen backgrounds of 5.0, 10, and ~21% O2. Changes in the localized surface plasmon resonance (LSPR) absorption peak were monitored during gas exposures and are believed to be the result of oxidation-reduction processes that fill or create oxygen vacancies in the CeO2. This process affects the LSPR peak position either by charge exchange with the Au nanoparticles or by changes in the dielectric constant surrounding the particles. Multivariate analysis was used to gauge the inherent selectivity of the film between the separate analytes. From principal component analysis (PCA), unique and identifiable responses were seen for each of the analytes. Linear discriminant analysis (LDA) was also used and showed separation between analytes as well as trends in gas concentration. Results indicate that the Au-CeO2 thin film is selective to O2, H2, CO, and NO2 in separate exposures. Combined with the observed stability over long exposure periods, the Au-CeO2 film shows good potential as an optical sensing element for harsh environmental conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Plasmonic Based Sensing Using an Array of Au-Metal Oxide Thin Films

An optical plasmonic-based sensing array has been developed and tested for the selective and sensitive detection of H2, CO, and NO2 at a temperature of 500°C in an oxygen-containing background. The three element sensing array used Au nanoparticles embedded in separate thin films of yttria stabilized zirconia (YSZ), CeO2, and TiO2. A peak in the absorbance spectrum due to a localized surface plasmon resonance (LSPR) on the Au nanoparticles was monitored for each film during gas exposures and showed a blue shift in the peak positions for the reducing gases, H2 and CO, and a red shift for the oxidizing gas NO2. A more in-depth look at the sensing response was performed using the multivariate methods of principal component analysis (PCA) analysis and linear discriminant analysis (LDA) on data from across the entire absorbance spectrum range. Qualitative results from both methods showed good separation between the three analytes for both the full array and the Au-TiO2 sample. Quantification of LDA cluster separation using the Mahalanobis distance showed better cluster separation for the array, but there were some instances with the lowest concentrations where the single Au-TiO2 film had better separation than the array. A second method to quantify cluster separation in LDA space was developed using multidimensional volume analysis of the individual cluster volume, overlapped cluster volume and empty volume between clusters. Compared to the individual sensing elements, the array showed less cluster overlap, smaller cluster volumes, and more space between clusters, all of which were expected for improved separability between the analytes.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

MALDI-Mass Spectrometric Imaging Revealing Hypoxia-Driven Lipids and Proteins in a Breast Tumor Model

Hypoxic areas are a common feature of rapidly growing malignant tumors and their metastases, and are typically spatially heterogeneous. Hypoxia has a strong impact on tumor cell biology and contributes to tumor progression in multiple ways. To date, only a few molecular key players in tumor hypoxia, such as for example hypoxia-inducible factor-1 (HIF-1), have been discovered. The distribution of biomolecules is frequently heterogeneous in the tumor volume, and may be driven by hypoxia and HIF-1α. Understanding the spatially heterogeneous hypoxic response of tumors is critical. Mass spectrometric imaging (MSI) provides a unique way of imaging biomolecular distributions in tissue sections with high spectral and spatial resolution. In this paper, breast tumor xenografts grown from MDA-MB-231-HRE-tdTomato cells, with a red fluorescent tdTomato protein construct under the control of a hypoxia response element (HRE)-containing promoter driven by HIF-1α, were used to detect the spatial distribution of hypoxic regions. We elucidated the 3D spatial relationship between hypoxic regions and the localization of small molecules, metabolites, lipids, and proteins by using principal component analysis – linear discriminant analysis (PCA-LDA) on 3D rendered MSI volume data from MDA-MB-231-HRE-tdTomato breast tumor xenografts. In this study we identified hypoxia-regulated proteins active in several distinct pathways such as glucose metabolism, regulation of actin cytoskeleton, protein folding, translation/ribosome, splicesome, the PI3K-Akt signaling pathway, hemoglobin chaperone, protein processing in endoplasmic reticulum, detoxification of reactive oxygen species, aurora B signaling/apoptotic execution phase, the RAS signaling pathway, the FAS signaling pathway/caspase cascade in apoptosis and telomere stress induced senescence. In parallel we also identified co-localization of hypoxic regions and various lipid species such as PC(16:0/18:1), PC(16:0/18:2), PC(18:0/18:1), PC(18:1/18:1), PC(18:1/18:2), PC(16:1/18:4), PC(18:0/20:3), PC(16:0/22:1), among others. Lastly, our findings shed light on the biomolecular composition of hypoxic tumor regions, which may be responsible for a given tumor’s resistance to radiation or chemotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

At-Line Sampling and Characterization of Pyrolytic Vapors from Biomass Feedstock Blends Using SPME-GC/MS-PCA: Influence of Char on Fast Pyrolysis

Solid-phase microextraction (SPME) coupled with GC-MS analysis was used for at-line sampling of pyrolytic vapors produced during fast pyrolysis of biomass. The pure and binary blends of switchgrass (SWG) and pine harvest residues (PT6) were used as feedstock. Sequential SPME sampling allowed for monitoring of changes in the pyrolysis vapors as char accumulated in the fluid bed. The concentration and composition of the vapors desorbed from the SPME fibers were investigated using GC-MS, and the data sets were then analyzed using principal component analysis (PCA) to compare the composition of the pyrolysis vapors over the course of the pyrolysis run. The chemical composition of both carbohydrate and lignin fragments varied as the char builds up in the reactor bed. Fragments derived from cellulose and xylan included anhydrosugars, furans and light oxygenated compounds. Lignin fragments included methoxyphenols, phenolic ketones and aldehydes, low molecular weight aromatics. The composition of the carbohydrate fragments changed more than the lignin fragments as the char build-up in the fluid bed. This combination of SPME-GC/MS-PCA were a novel, easy and effective method for measuring the composition and changes in the composition of pyrolysis vapors during the fast pyrolysis process. Here, this work also highlighted the effect of char build-up on the composition of the overall pyrolysis vapors.

09 BIOMASS FUELS↗
DOE OSTI2023

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. Furthermore, we also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Multimodal Broadband Vibrational Sum Frequency Generation (MM-BB-V-SFG) Spectrometer and Microscope

A broadband sum frequency generation (BB-SFG) spectrometer with multimodal (MM) capabilities was constructed, which could be routinely reconfigured for tabletop experiments in reflection, transmission, and total internal reflection (TIR) geometries, as well as microscopic imaging. The system was constructed using a Ti:sapphire amplifier (800 nm, pulse width = 85 fs, repetition rate = 2 kHz), an optical parameter amplification (OPA) system for production of broadband IR pulses tunable between 1000 and 4000 cm –1 , and two Fabry–Pérot etalons arranged in series for production of narrowband 800 nm pulses. The key feature allowing the MM operation was the nearly collinear alignment of the visible (fixed, 800 nm) and infrared (tunable, 1000–4000 cm –1 ) pulses which were spatially separated. Physical insights discussed in this paper include the comparison of spectral bandwidth produced with 40 and 85 fs pump beams, the improvement of spectral resolution using etalons, the SFG probe volume in bulk analysis, the normalization of SFG signals, the stitching of multiple spectral segments, and the operation in different modes for air/liquid and adsorbate/solid interfaces, bulk samples, as well as spectral imaging combined with principle component analysis (PCA). Finally, the SFG spectral features obtained with the MM-BB-SFG system were compared with those obtained with picosecond-scanning-SFG system and high-resolution BB-SFG system (HR-BB-SFG) for dimethyl sulfoxide, α-pinene, and various samples containing cellulose (purified commercial products, Cladophora cell wall, cotton and flax fibers, and onion epidermis cell wall).

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Exploring Responses of Contact Kelvin Probe Force Microscopy in Triple-Cation Double-Halide Perovskites

Hybrid organic–inorganic perovskite (HOIP) solar cells have undergone unprecedentedly rapid improvement in power conversion efficiencies during the last decade, but controversy remains regarding the role of local phenomena and heterogeneity in grain by grain. These challenges in turn necessitate understanding the fundamental mechanisms operating at individual grains and exploring how these mechanisms give rise to the collective behaviors of the films. Although numerous efforts have targeted local phenomena in these materials using classical scanning probe microscopy modalities, the measurements remain difficult to interpret due to intrinsic coupling between possible ferroelectric, ion migration, bulk and surface electrochemical phenomena, and the effects of light on these behaviors. Here, we explore the electromechanical responses in (Cs 0.05 FA 0.85 MA 0.15 )Pb(I 0.85 Br 0.15 ) 3 perovskite thin film in the dark and under illumination using band excitation-piezoresponse force microscopy (BE-PFM). To identify and separate ferroic, electrostatic, and electrochemical responses, we utilize contact-Kelvin probe force microscopy (cKPFM). The cKPFM responses show weak variability with morphological features in the dark, while such variability significantly increases under illumination. Deconvolution of the cKPFM responses via principal component analysis (PCA) shows heterogeneity in grain to grain possibly due to variation in migration and segregation of halide ions in mixed perovskites at specific grains. Further, we propose an electrostatic mechanism based on the physics of semiconductors, which can directly link the observed cKPFM behaviors with the ionic and electronic density of states (DOS) and their time dynamics. Here, these findings suggest that cKPFM is a powerful probe of local ionic behaviors on the nanoscale, both in HOIPs and other materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Multiresolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multiresolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training data set by an order of magnitude without additional cost. We obtain very good agreement for 13 C, 15 N, and 17 O chemical shifts when compared to ab initio quantum chemistry methods, with the highest accuracy found for 1 H chemical shifts that is comparable to the error between the ab initio results and experimental measurements. Principal component analysis (PCA) is used to both understand these greatly improved predictions for 1 H , as well as indicating that chemical shift prediction for 13 C, 15 N, and 17 O, which have far fewer training environments than the 1 H atom type, will improve once more unique training samples are made available to exploit the deep network architecture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

MODE: A Web Application for Interactive Visualization and Exploration of Omics Data

Studies generating transcriptomics, proteomics, lipidomics, and metabolomics (colloquially referred to as “omics”) data allow researchers to find biomarkers or molecular targets, or understand complex biological structures and functions by identifying changes in biomolecule abundance and expression between experimental conditions. Omics data is multi-dimensional and oftentimes summarization techniques such as principal component analysis (PCA) are used to identify high-level patterns in data. Though useful, these summaries don’t allow exploration of detailed patterns in omics data that may have biological relevance. The use of interactive HTML displays with plots allows researchers to interact with omics data at a detailed level, but building these displays requires significant coding expertise. To overcome this barrier, the software MODE was built to empower users to build their own interactive HTML displays to support scientific discovery. These displays are easily shareable, do not depend on a specific operating system, and allow users to effortlessly sort and filter plots by categorical or numerical variables. MODE allows users to build and share these displays with several options for plot design and meta selection. In conclusion, the MODE web application and its capabilities are presented and then demonstrated on lipidomics data from a leaf wounding study.

lipidomics↗
DOE OSTI2020

Understanding Time Dependence on Zinc Metal-Organic Framework Growth Using In Situ Liquid Secondary Ion Mass Spectrometry

The abundance of novel metal-organic framework (MOF) materials continues to increase as more applications are discovered for these highly porous, well-ordered crystalline adsorbents. The simplicity of its constituents allows for the design of new MOFs with virtue of functionality and pore topology towards target adsorbates. However, the fundamental understanding of how these frameworks age during nucleation and growth is mostly limited to speculation from simulation studies. In this effort, we utilize the microfluidic interface of liquid and vacuum in a unique SALVI device to analyze the formation and aging of the benchmark MOF-74 framework using time-of-flight secondary ion mass spectrometry (ToF-SIMS). Principal component analysis (PCA) of the SIMS mass spectra together with ex situ microscopy and porosimetry, provide new insights into the growth and aging process.

Sabale, Sandip R.↗
DOE OSTI2026

Composition, Activity, and Stability of IrO x Oxygen Evolution Reaction Electrocatalysts

The oxygen evolution reaction (OER) is integral to several electrochemical energy conversion and storage technologies, including carbon dioxide reduction to value added fuels, nitrogen reduction to ammonia, reversible fuel cells, rechargeable metal−air batteries, and water electrolysis to produce hydrogen. Iridium oxide (IrO x ) is widely recognized as the benchmark OER catalyst for acidic environments. Despite widespread use of IrO x catalysts, most notably in proton-exchange membrane water electrolyzers (PEMWEs), a comprehensive understanding of the physicochemical properties of commercial catalysts and the impact of these properties on both the activity and stability of these catalysts is lacking. Here, we study commercial IrO x catalysts with different physicochemical properties, three nominally considered amorphous and three rutile, to elucidate how structural and compositional variations affect OER activity and stability. Utilizing standardized aqueous electrochemical protocols, time-resolved dissolution quantification using inductively-coupled plasma mass spectrometry, and physicochemical characterization, including multiple synchrotron X-ray techniques, we systematically correlate catalyst properties with OER performance and degradation behavior aided by principal component analysis (PCA). Our results demonstrate the general trend of amorphous IrO x having higher intrinsic activity but limited stability and crystalline rutile IrO 2 having lower activity but enhanced stability against dissolution. The trends within the amorphous and rutile catalyst groups correlate with inherent material properties, including phase composition and structure, crystallinity, particle size, surface area, and surface structure/chemistry. Notably, we identify a rutile catalyst with the largest crystallite/ domain sizes, moderate surface area, a small fraction of hydrous phase, and a favorable pore structure (trimodal distributions of pore sizes ranging from 2−5 nm) that exhibits the best balance between activity and stability among the six catalysts studied here. These findings illustrate a fundamental structure-governed trade-off between activity and stability and highlight the critical role of surface chemistry modification and structure engineering in IrO x catalyst optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Passively Sampled Environmental Films Show Geographic Variability and Host a Variety of Microorganisms

Environmental films are ubiquitous coatings that form on virtually all surfaces exposed to the troposphere. This work analyzes large molecular weight species in native environmental films acquired at three collection sites in the vicinity of Iowa City, Iowa, USA that include an urban residence, a wooded area, and a metropolitan building rooftop. The films are developed for up to twelve months on polished silicon wafer substrates housed in flat-plate passive samplers. Film composition was assessed by Fourier transform ion cyclotron resonance secondary ion mass spectrometry (FTICR-SIMS), and spatially resolved chemical morphology was analyzed via timeof- flight secondary ion mass spectrometry (ToF-SIMS) imaging. Mass analysis spanning up to m/z 2,000 included detection of a variety of organic and inorganic compounds. We characterize MS data with PCA to identify variability between the collection sites and the respective films generated therein, where we detected a variety of organic and biotically generated species consistent with fungal and bacterial microorganisms. Here, the film collection site as well as deposition time contribute to spectral variability, suggesting that geographic location as well as seasonal diversity may affect composition. SIMS analyses were complemented with microscopy and SYBR-Safe staining to highlight biological microorganisms within the films, which include evidence of fungi, protozoa, and bacteria. SIMS imaging also reveals domains concentrated with fragments indicative of proteinaceous macromolecules and microbial biomass, characteristic of biological presence within the film.

42 ENGINEERING↗
DOE OSTI2024

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

ammonia↗
DOE OSTI2021

Measuring Plant Metabolite Abundance in Spearmint ( Mentha spicata L.) with Raman Spectra to Determine Optimal Harvest Time

A fast field-deployable method utilizing Raman spectroscopy to determine the optimal harvest time of plants to extract the highest abundance of target metabolites is presented. Rosmarinic acid is a metabolite extracted from spearmint (Mentha spicata L.). Leaves from commercial “Native” and proprietary clonal line “KI110” spearmint were measured as a function of cell type and age to determine rosmarinic acid abundance. A linear regression model with leave-one-out cross-validation (R 2 CV = 0.61, RMSECV = 11.1 mg/g) was developed between selected Raman peak areas and rosmarinic acid concentrations determined by high-performance liquid chromatography (HPLC). A principal component analysis (PCA) model was also developed to determine rosmarinic acid abundance. The method may be suited to the analysis of many agriculturally relevant plant species and metabolites with distinct Raman peaks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗