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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Variation in energy sorghum hybrid TX08001 biomass composition and lignin chemistry during development under irrigated and non-irrigated field conditions

This study was conducted to document the extent and basis of compositional variation of shoot biomass of the energy Sorghum bicolor hybrid TX08001 during development under field conditions. TX08001 is capable of accumulating ~40 Mg/ha of dry biomass under good growing conditions and this genotype allocates ~80% of its shoot biomass to stems. After 150 days of growth TX08001 stems had a fresh/dry weight ratio of ~3:1 and soluble biomass accounted for ~30% of stem biomass. A panel of diverse energy sorghum genotypes varied ~6-fold in the ratio of stem structural to soluble biomass after 150 days of growth. Near-infrared spectroscopic analysis (NIRS) showed that TX08001 leaves accumulated higher levels of protein, water extractives and ash compared to stems, which have higher sugar, cellulose, and lignin contents. TX08001 stem sucrose content varied during development, whereas the composition of TX08001 stem cell walls, which consisted of ~45–49% cellulose, ~27–30% xylan, and ~15–18% lignin, remained constant after 90 days post emergence until the end of the growing season (180 days). TX08001 and Della stem syringyl (S)/guaiacyl (G) (0.53–0.58) and ferulic acid (FA)/para-coumaric acid (pCA) ratios were similar whereas ratios of pCA/(S+G) differed between these genotypes. Additionally, an analysis of irrigated versus non-irrigated TX08001 revealed that non-irrigated hybrids exhibited a 50% reduction in total cell wall biomass, an ~2-fold increase in stem sugars, and an ~25% increase in water extractives relative to irrigated hybrids. This study provides a baseline of information to help guide further optimization of energy sorghum composition for various end-uses.

09 BIOMASS FUELS↗
DOE OSTI2018

Mapping of crown rust resistance gene Pc53 in oat ( Avena sativa )

Crown rust disease caused by the fungus Puccinia coronata f. sp. avenae ( Pca ) is a major production constraint of oat in North America, Europe, and Australia. There are over 100 genes effective against one or more Pca races, but only a handful of seedling resistance ( Pc ) genes have been mapped to a known chromosomal location. The goal of the present study was to use linkage mapping to identify the genomic location of the Pc53 gene, and to produce a list of linked SNPs with potential as molecular markers for marker assisted breeding. The Pc53 gene was placed on the linkage group Mrg08 at 82.4 cM using F 5 -derived recombinant inbred lines (RILs) from a cross between the Pc53 carrier 6-112-1-15 (PI 311624) and the susceptible cultivar Otana. The map location was validated using RILs from a cross between 6-112-1-15 and the Pc50 differential line. Single nucleotide polymorphism marker GMI_ES02_c14533_567 was the closest to Pc53 . A major seedling resistance gene ‘ PcKM ’ and QTL QcC . Core . 08 . 1 , QCr . Core . 08 . 2 , QCr . Core . 08 . 3 and QCr . cdl9-12D were previously reported on Mrg08. QPc . Core . 08 . 1 and PcKM were mapped to within 1 cM of Pc53 ; but previous virulence studies have indicated separate identities. The chromosomal location of Pc53 and SNPs linked with it will facilitate the utilization of Pc53 in oat breeding programs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Mapping crown rust resistance in the oat diploid accession PI 258731 ( Avena strigosa )

Oat crown rust, caused by Puccinia coronata Corda f. sp. avenae Eriks. (Pca), is a major biotic impediment to global oat production. Crown rust resistance has been described in oat diploid species A. strigosa accession PI 258731 and resistance from this accession has been successfully introgressed into hexaploid A. sativa germplasm. The current study focuses on 1) mapping the location of QTL containing resistance and evaluating the number of quantitative trait loci (QTL) conditioning resistance in PI 258731; 2) understanding the relationship between the original genomic location in A. strigosa and the location of the introgression in the A. sativa genome; 3) identifying molecular markers tightly linked with PI 258731 resistance loci that could be used for marker assisted selection and detection of this resistance in diverse A. strigosa accessions. To achieve this, A. strigosa accessions, PI 258731 and PI 573582 were crossed to produce 168 F5:6 recombinant inbred lines (RILs) through single seed descent. Parents and RILs were genotyped with the 6K Illumina SNP array which generated 168 segregating SNPs. Seedling reactions to two isolates of Pca (races TTTG, QTRG) were conditioned by two genes (0.6 cM apart) in this population. Linkage mapping placed these two resistant loci to 7.7 (QTRG) to 8 (TTTG) cM region on LG7. Field reaction data was used for QTL analysis and the results of interval mapping (MIM) revealed a major QTL (QPc.FD-AS-AA4) for field resistance. SNP marker assays were developed and tested in 125 diverse A. strigosa accessions that were rated for crown rust resistance in Baton Rouge, LA and Gainesville, FL and as seedlings against races TTTG and QTRG. Our data proposed SNP marker GMI_ES17_c6425_188 as a candidate for use in marker-assisted selection, in addition to the marker GMI_ES02_c37788_255 suggested by Rine’s group, which provides an additional tool in facilitating the utilization of this gene in oat breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Effects of 9.5 years warming on SOC concentration and composition in bulk soil and density fractions

Original data of whole-soil warming experiment after 9.5 years at Blodgett Forest Research Station. The Blodgett Forest is a mixed coniferous temperate forest with Mediterranean climate. The annual air temperature is 12.5℃ and the annual precipitation is 1774 mm yr-1- The soil is mesic ultic Alfisol of granitic origin, equivalent to Dystric Cambisol according to The World Reference Base for Soil Resources (WRB) system. The soil is warmed down to 1 m at + 4℃ by vertically installed heating cables. At the time of soil sampling on 1 May 2023, the whole-soil warming experiment had been running for approximately 9.5 years, from January 2014 to May 2023. The dataset includes: - Bulk_EA: C, N content, δ13C, and CN ratio of bulk soil; - Density_fractionation: organic carbon concentration, δ13C, and C/N ratio of free light fraction (fLF), occluded ligh fraction (oLF), and heavy fraction (HF); - PCA_DRIFT_AUC: original data of area under the curve (AUC) values of eight carbon bond types integrated on diffuse reflectance infrared fourier transform spectroscopy for each soil sample and soil fraction, which are consequently used for principal component analysis (PCA); - DRIFTS_stability_index: the calculation of aliphatic C–H (3000–2800 cm-1) to aromatic C=C (1670–1600 cm-1) ratios for each bulk soil sample and soil fraction. All data are provided in CSV format and can be viewed using Microsoft Excel.

Climate change↗
DOE OSTI2024

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. A Principal Component Analysis (PCA) approach is being developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and PCA model, along with initial performance metrics. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Carpenter, A.↗
DOE OSTI2025

Predicting Dynamic-to-Static Correction Factor from Petrophysical Data and Chemostratigraphy using Unsupervised Machine Learning

Estimating static mechanical properties of stratigraphic layers is critical for optimizing subsurface engineering applications. To estimate dynamic-to-static correction factor F ds (static-to-dynamic Young’s modulus ratio) across the Caney shale interval in Oklahoma, USA, we integrated triaxial test measurements and petrophysical data, including well logs and X-ray fluorescence (XRF) using unsupervised machine learning (ML). We used a novel workflow that includes principal component analysis (PCA) to reduce data set dimensionality of well logs and XRF data sets—both separately and combined—creating three scenarios, and later applied inverse distance weighting (IDW) to derive F ds profiles for these scenarios. Furthermore, we applied K-means clustering on each scenario to predict depositional facies, and built a stiffness zonation profile through chemostratigraphic analysis of the terrigenous elements to validate the predicted F ds . The predicted F ds profile from each scenario using the PCA-IDW method was compared with the constant F ds approach from our previous study by calculating the root mean square error (RMSE). The combined data sets scenario yielded the lowest RMSE value of 0.113, while the RMSE values for the well logs and XRF scenarios were 0.131 and 0.129, respectively. In addition, the predicted F ds from the XRF scenario well-matched the stiffness zonation from the chemostratigraphic analysis that was built using the optimized K-means clustering of nine clusters for that scenario. These methods and findings offer a valuable tool for refining lithological classification and improving the F ds profile, potentially enhancing drilling and stimulation strategies for subsurface energy engineering applications.

clastic rock↗
DOE OSTI2015

Heat-activated Plasmonic Chemical Sensors for Harsh Environments

A passive plasmonics based chemical sensing system to be used in harsh operating environments was investigated and developed within this program. The initial proposed technology was based on combining technologies developed at the SUNY Polytechnic Institute Colleges of Nanoscale Science and Engineering (CNSE) and at the University of Minnesota (UM). Specifically, a passive wireless technique developed at UM was to utilize a heat-activated plasmonic design to passively harvest the thermal energy from within a combustion emission stream and convert this into a narrowly focused light source. This plasmonic device was based on a bullseye design patterned into a gold film using focused ion beam methods (FIB). Critical to the design was the use of thermal stabilizing under and overlayers surrounding the gold film. These stabilizing layers were based on both atomic layer deposited films as well as metal laminate layers developed by United Technologies Aerospace Systems (UTAS). While the bullseye design was never able to be thermally stabilized for operating temperatures of 500oC or higher, an alternative energy harvesting design was developed by CNSE within this program. With this new development, plasmonic sensing results are presented where thermal energy is harvested using lithographically patterned Au nanorods, replacing the need for an external incident light source. Gas sensing results using the harvested thermal energy are in good agreement with sensing experiments, which used an external incident light source. Principal Component Analysis (PCA) was used to reduce the wavelength parameter space from 665 variables down to 4 variables with similar levels of demonstrated selectivity. The method was further improved by patterning rods which harvested energy in the near infrared, which led to a factor of 10 decrease in data acquisition times as well as demonstrated selectivity with a reduced wavelength data set. The combination of a plasmonic-based energy harvesting sensing paradigm with PCA analysis and wavelength down selection offers a novel path towards simplification and integration of plasmonic-based sensing methods using selected wavelengths rather than a full spectral analysis. Integration efforts were designed and modeled for thermal and mass transport considerations by UTAS which led to the 3D printing of scaled models that would serve as the housing for the alternative energy harvesting plasmonic chemical sensor design developed by CNSE.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Microbial Oxidation of Hg(0) - Its Effect on Hg Stable Isotope Fractionation and Methylmercury Production

Mercury (Hg) associated with mixed waste generated by nuclear weapons manufacturing has contaminated vast areas of the Oak Ridge Reservation (ORR). Neurotoxic methylmercury (MeHg) has been formed from the inorganic Hg wastes discharged into headwaters of East Fork Poplar Creek (EFPC). Thus, understanding the processes and mechanisms that lead to Hg methylation along the flow path of EFPC is critical to predicting the impacts of the contamination and the design of remedial action at the ORR. In part I of our project, we investigated Hg(0) oxidation and methylation by anaerobic bacteria. We discovered that the anaerobic bacterium Desulfovibrio desulfuricans ND132 can oxidize elemental mercury [Hg(0)]. When provided with dissolved elemental mercury, D. desulfuricans ND132 converts Hg(0) to Hg(II) and neurotoxic methylmercury [MeHg]. We also demonstrated that diverse species of subsurface bacteria oxidizes dissolved elemental mercury under anoxic conditions. The obligate anaerobic bacterium Geothrix fermentans H5, and the facultative anaerobic bacteria Shewanella oneidensis MR-1 and Cupriavidus metallidurans AE104 can oxidize Hg(0) to Hg(II) under anaerobic conditions. In part II of our project, we established anaerobic enrichment cultures and obtained new bacterial strains from the DOE Oak Ridge site. We isolated three new bacterial strains from subsurface sediments collected from Oak Ridge. These isolates are Bradyrhizobium sp. strain FRC01, Clostridium sp. strain FGH, and a novel Negativicutes strain RU4. Strain RU4 is a completely new genus and species of bacteria. We also demonstrated that syntrophic interactions between fermentative bacteria and sulfate-reducing bacteria in Oak Ridge saprolite mediate iron reduction via multiple mechanisms. Finally, we tested the impact of Hg on denitrification in nitrate reducing enrichment cultures derived from subsurface sediments from the Oak Ridge site, where nitrate is a major contaminant. We showed that there is an inverse relationship between Hg concentrations and rates of denitrification in enrichment cultures. In part III of our project, we examined in more detail the effects of microbial interactions on Hg transformations. We discovered that both sulfate reducing and iron reducing bacteria coexist in freshwater sediments and both microbial groups contribute to mercury methylation. We showed that mercury methylation by sulfate reducing and iron reducing bacteria are temporally and spatially separated processes. We also discovered that methanogens can methylate mercury. We showed that Methanospirillum hungatei JF-1 methylated Hg at comparable rates, but with higher yields, than those observed for sulfate-reducing bacteria and iron-reducing bacteria. Finally, we demonstrated that syntrophic interactions between different microbial groups increase mercury methylation rates. We showed that Hg methylation rates are stimulated via inter-species hydrogen and acetate transfer (i) from sulfate-reducing bacteria to methanogens and (ii) from fermenters to the sulfate-reducing bacteria. In part IV of the project, we studied Hg bioavailability and Hg isotope fractionation. We demonstrated that thiol-bound Hg is bioavailable to mercury resistant bacteria. We found that uptake of Hg from Hg-glutathione and Hg-cysteine complexes does not require functioning glutathione and cystine/cysteine transport systems. We demonstrated that a wide range of methylmercury complexes (e.g. MeHgOH, MeHg-cysteine, and MeHg-glutathione) are bioavailable to mercury resistant bacteria. The rate of MeHg demethylation varies more between different species of mercury resistant bacteria than among MeHg complexes. We showed that microbial demethylation of MeHg depends more on the species of microorganism than on the types and relative concentrations of thiols or other MeHg ligands present. Finally, we demonstrated that Hg methylation by Geobacter sulfurreducens PCA and Desulfovibrio desulfuricans ND132 imparts mass-dependent discrimination against 202Hg relative to 198Hg. G. sulfurreducens PCA and D. desulfuricans ND132 have similar kinetic reactant/product Hg fractionation factors. Using the Hg isotope data, we showed that there are multiple intra- and/or extracellular pools provide substrate inorganic Hg for methylation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Structural Health Monitoring of Epoxy Bond between Dissimilar Materials

Structures composed of adhesively bonded materials face many challenges when subjected to thermal loading, as adherend material(s) behave differently and can induce stresses in the glue bond. Current methods to test bond strength are limited to destructive tests, which increase costs to produce adhesively bonded structures. Strain gages are proposed to monitor the expansion profile of adhesive bonds as structures undergo thermal loading. Because resulting signals are DC in nature, no spectral quantities can be observed. Principle component analysis (PCA) is proposed to differentiate between damaged and undamaged structures. Training data are taken for various damage states and the minimum Mahalanobis distance is calculated to categorize testing data into damage state. The results indicate the potential for a strain-gage based monitoring system with PCA based decision-making, with further research necessary prior to implementation of the system.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. A Principal Component Analysis (PCA) approach is being developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and PCA model, along with initial performance metrics. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Carpenter, A.↗
DOE OSTI2025

Regime Characterization of Offshore Wind Resource Using Unsupervised Learning

Predictability of wind resource conditions is critical for offshore wind design and operations. While many studies of extreme wind conditions focus on specific events such as low-level jets or ramps, these rely on threshold definitions that limit generality. Here we present a data-driven framework that combines principal component analysis (PCA), self-organizing maps (SOM), and k-means clustering to classify wind resource conditions as typical and anomalous from climatological data. Anomalies are defined not by fixed thresholds but by flagging samples located far from SOM node centers inside the baseline SOM structure. This reframes extremes as rare ebents and hence, likely difficult to anticipate by numerical weather prediction models. We applied this approach to 23 years (2000–2022) of hourly profiles from the NOW-23 hindcast model at the Humboldt Wind Energy Area. Classification is conducted on a feature space consisting of 10 m wind speed and direction, bulk shear and veer across 30–270 m, and a low-level jet index. Dimensionality reduction is achieved through PC. A 2 × 3 OM lattice trained on the PCA vectors identified six baseline regimes spanning weak to strong flow states. High quantization-error profiles are identified and re-clustered into four anomalous regimes. The baseline regimes exhibited clear seasonal and diurnal cycles. Meanwhile, the anomalous regimes represented <10 % of all hours but showed distinct combinations of speed, shear, and veer, when compared to the baseline regimes. Anomalous regimes are typically short-lived (~few hours), yet their transitions can lead to hub-height wind changes of −18 to +9 m s -1 . For a representative 15 MW turbine, these shifts imply rapid swings in capacity factor from near-full output to negligible generation. Validation with lidar buoy data showed 51% agreement in SOM labels across ~6,000 overlapping hours, with most mismatches confined to adjacent speed classes. HRRR comparisons further revealed that anomalous regimes were disproportionately associated with forecast biases exceeding 5 m s -1 . Together, these results reframe extremes in offshore wind from absolute maxima or minima to weather states that are difficult to anticipate from models.

17 WIND ENERGY↗
DOE OSTI2025

Uncertainty Quantification for Smooth Functional Data with Application to Material Properties

This document outlines a method for processing functional output (i.e., curves) for the ultimate purpose of sampling curves under specified input conditions for use in modeling and simulation uncertainty quantification (UQ) studies. A set of benchmark curves sufficiently representative of the relevant scenario(s) being simulated are provided to the process and formatted as described in Section 1. Principal Component Analysis (PCA) is utilized to discover the components of uncertainty in the benchmark curves and is outlined in Section 2. Section 3 describes the application of uncertainty quantification to the PCA results for the purpose of sampling curves to be used in UQ analysis. Section 4 applies these techniques to an example benchmark dataset. Concluding remarks are provided in the final section.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reduced Models for Chemical Kinetics derived from Parallel Ensemble Simulations of Stirred Reactors

Detailed chemical kinetic models are essential to improve the predictive capability of computational fluid dynamics (CFD) simulations and to impact the development of next generation combustion technologies. However, such detailed kinetic models are beyond the reach of CFD codes due to the computational cost of transporting additional chemical degrees of freedom. Reduced order models for combustion kinetics exploit the presence of lower dimensional manifolds in the state space and require far fewer number of scalars to be transported as part of CFD simulations. In this paper, we describe the software infrastructure developed to allow large ensembles of stirred reactor configurations with detailed chemical kinetics. The canonical reactor simulations are incorporated in a workflow with sparse grid and data reduction methodologies aimed at development of reduced order models. We present results from analyzing the results of the stirred reactor calculations for syngas mixture using principal component analysis (PCA) and neural network methodologies. The results show that nonlinear autoencoder model can surpass PCA in representing the original dataset with far fewer degrees of freedom.

Zhang, Pei↗
DOE OSTI2021

A priori examination of reduced chemistry models derived from canonical stirred reactors using three-dimensional direct numerical simulation datasets

Data-driven approaches to construct reduced chemical kinetic models, that rely heavily on thermo-chemical datasets with full chemical kinetics, have been gaining popularity. Datasets from direct numerical simulations (DNS) under three-dimensional (3-D) realistic turbulent flow conditions are desirable but limited to carefully designed parametric conditions due to the computational cost. Constructing datasets from a large ensemble of zero-dimensional stirred reactors like perfectly stirred reactor (PSR) and partially stirred reactor (PaSR) is a computationally efficient solution to consider the turbulence-chemistry interactions and cover a broad range of parametric conditions. In this paper, we derive reduced chemistry models from solutions of a large number of PSR and PaSR reactors using autoencoder (AE) neural networks and principal component analysis (PCA), and conduct a priori examination of the reduced models in three temporally evolving 3-D DNS jet flames featuring local extinction and re-ignition. The results show that the reduced models derived from PaSR datasets, i.e., AE-PaSR and PCA-PaSR, generally show significant improvement over the ones derived from PSR datasets. Among all the reduced models, AE-PaSR shows the best agreement with DNS results on the reconstruction accuracy and the representation of temporally evolving local extinction and re-ignition events.

Zhang, Pei↗
DOE OSTI2022

Applying Two-Dimensional Correlation Spectroscopy and Principal Component Analysis to Understand How Temperature Affects the Neptunium(V) Absorption Spectrum

The visible-near infrared (Vis-NIR) electronic absorption spectrum of neptunium(V) (NpO 2 + ) comprises numerous f-f electronic transitions with mostly undocumented temperature dependencies. The effect of temperature on the absorption spectrum of the pentavalent neptunyl dioxocation (NpO 2 + ) is an important factor to consider with spectrophotometric applications but has often been overlooked. Optical Vis-NIR absorption spectra (400–1700 nm) of Np(V) (0.017–0.89 M) in 1 M nitric acid were evaluated with varying temperatures (T = 10–80 °C). The intensity, position, and overall shape of the bands were sensitive to interactions with the solvent and coordination environment. Numerous temperature-induced isosbestic points were identified resulting from dynamic, overlapping peak shifts. Spectral variations were characterized using principal component analysis (PCA) and 2D correlation spectroscopy (COS). 2D-COS revealed that the absorption band near 1095 nm likely consists of two bands centered near 1087 and 1096 nm, which cannot be explained by current computational methods. 2D-COS analysis also provided an unambiguous assignment of unresolved peaks in the visible region for comparison with computational predictions. PCA was used to identify nonlinearity in the spectral response at elevated Np(V) concentrations ≥ 0.5 M. This unique experimental data and interpretation will foster a deeper understanding of the absorption spectra for complex actinyl ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Molecular Structural Changes in Alfalfa Detected by ATR-FTIR Spectroscopy in Response to Silencing of TT8 and HB12 Genes

This study investigated the spectral changes in alfalfa molecular structures induced by silencing of Transparent Testa 8 ( TT8 ) and Homeobox 12 ( HB12 ) genes with univariate and multivariate analyses. TT8 -silenced (TT8i), HB12-silenced (HB12i) and wild type (WT) alfalfa were grown in a greenhouse under normal conditions and were harvested at early-to-mid vegetative stage. Samples were free-dried and grounded through 0.02 mm sieve for spectra collections with attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. Afterwards, both univariate and multivariate analyses were conducted on amide, carbohydrate and lipid regions. Univariate results showed that silencing of TT8 and HB12 genes affected peak heights of most total carbohydrate (TC) and structural carbohydrate (STC), and structural carbohydrate area (STCA) in carbohydrate regions; and β-sheet height, amide areas, and ratios of amide I/II and α-helix/β-sheet in amide region; and symmetric CH2 (SyCH2), asymmetric CH2 (AsCH2) and (a)symmetric CH2 and CH3 area (ASCCA) in the lipid region. Multivariate analysis showed that both hierarchy cluster analysis (HCA) and principal component analysis (PCA) clearly separated WT from transgenic plants in all carbohydrate regions and (a)symmetric CH2 and CH3 (ASCC) lipid region. In the amide region, PCA separated WT, TT8i and HB12i into different groups, while HCA clustered WT into a separate group. In conclusion, silencing of TT8 and HB12 affected intrinsic molecular structures of both amide and carbohydrate profiles in alfalfa, and multivariate analyses successfully distinguished gene-silenced alfalfa from its parental WT control.

Biochemistry & Molecular Biology↗