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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Carbon Nanostructures in Biological Sensors for Space and Terrestrial Applications

Biosensing devices comprised of carbon nanotubes and nanofibers have been developed for astronaut crew point-of-care. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using carbon nanotube and carbon nanofiber electrodes for biomedical applications. First, a 3x3 electrode device, with each electrode containing 40,000 carbon nanofiber nanoelectrodes was fabricated on silicon using traditional microfabrication processing. The device was demonstrated as a multiplexed immunosensor for simultaneous, label-free detection of cardiac troponin-I, C-reactive protein and myoglobin. Antibodies specific to cardiac troponin-I, C-reactive protein and myoglobin were covalently bound to the CNF surface and were characterized using electrochemical impedance spectroscopy and differential pulse voltammetry. Each step of the modification process resulted in changes in resistance to charge transfer due to the changes at the electrode surface upon antibody immobilization and binding to the specific cardiac protein. The real-time label free detection of the three cardiac markers from pure components and mixtures was demonstrated with high sensitivity, down to 0.2 ng/mL, and good selectivity. Detection in human blood serum did not present false positives from non-specific protein adsorption. More recently, this detection scheme has been applied to inkjet printed carbon nanotube electrodes on Kapton and paper. Printed devices have several unique advantages including simple and inexpensive fabrication. The results demonstrate that these sensors can serve a miniaturized, low cost device for detection of proteins in complex mixtures making this platform a good candidate for early stage diagnosis of myocardial infarction. Future inkjet printed devices can be fabricated have the added advantage in their suitability to be manufactured in an in-space, microgravity environment.

carbon nanotubes↗
NASA NTRS2019

Carbon Nanomaterials for Biosensing Applications

Biosensing devices comprised of carbon nanotubes and nanofibers have been developed for astronaut crew point-of-care. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using carbon nanotube and carbon nanofiber electrodes for biomedical applications. First, a 3x3 electrode device, with each electrode containing 40,000 carbon nanofiber nanoelectrodes was fabricated on silicon using traditional microfabrication processing. The device was demonstrated as a multiplexed immunosensor for simultaneous, label-free detection of cardiac troponin-I, C-reactive protein and myoglobin. Antibodies specific to cardiac troponin-I, C-reactive protein and myoglobin were covalently bound to the CNF surface and were characterized using electrochemical impedance spectroscopy and differential pulse voltammetry. Each step of the modification process resulted in changes in resistance to charge transfer due to the changes at the electrode surface upon antibody immobilization and binding to the specific cardiac protein. The real-time label free detection of the three cardiac markers from pure components and mixtures was demonstrated with high sensitivity, down to 0.2 ng/mL, and good selectivity. Detection in human blood serum did not present false positives from non-specific protein adsorption. More recently, this detection scheme has been applied to inkjet printed carbon nanotube electrodes on Kapton and paper. Printed devices have several unique advantages including simple and inexpensive fabrication. The results demonstrate that these sensors can serve a miniaturized, low cost device for detection of proteins in complex mixtures making this platform a good candidate for early stage diagnosis of myocardial infarction. Future inkjet printed devices can be fabricated have the added advantage in their suitability to be manufactured in an in-space, microgravity environment.

carbon nanotubes↗
DOE OSTI2021

Detection and Aggregation of Listeria Monocytogenes Using Polyclonal Antibody Gold-Coated Magnetic Nanoshells Surface-Enhanced Raman Spectroscopy Substrates

Magnetic nanoshells with tailored surface chemistry can enhance bacterial detection and separation technologies. This work demonstrated a simple technique to detect, capture, and aggregate bacteria with the aid of end-functionalized polyclonal antibody gold-coated magnetic nanoshells (pAb-Lis-AuMNs) as surface-enhanced Raman spectroscopy (SERS) probes. Listeria monocytogenes were used as the pathogenic bacteria and the pAb-LisAuMNs, 300 nm diameter, were used as probes allowing facile magnetic separation and aggregation. An optimized covalent bioconjugation procedure between the magnetic nanoshells and the polyclonal antibody was performed at pH six via a carbodiimide crosslinking reaction. Spectroscopic and morphological characterization techniques confirmed the fabrication of stable pAb-Lis-AuMNs. The resulting pAb-Lis-AuMNs acted as a SERS probe for L. monocytogenes based on the targeted capture via surface binding interactions and magnetically induced aggregation. Label-free SERS measurements were recorded for the minimum detectable amount of L. monocytogenes based on the SERS intensity at the 1388 cm -1 Raman shift. L. monocytogenes concentrations exhibited detection limits in the range of 10 4 –10 7 CFU ml -1 , before and after aggregation. By fitting these concentrations, the limit of detection of this method was ~10 3 CFU ml -1 . Using a low-intensity magnetic field of 35 G, pAb-LisAuMNs aggregated L. monocytogenes as demonstrated with microscopy techniques, including SEM and optical microscopy. Overall, this work presents a label-free SERS probe method comprised of a surface-modified polyclonal antibody sub-micron magnetic nanoshell structures with high sensitivity and magnetic induced separation that could lead to the fabrication of multiple single-step sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Single-cell proteomics of Arabidopsis leaf mesophyll reveals dynamic protein responses to water-deficit stress

Background The application of single-cell omics tools to biological systems can provide unique insights into diverse cellular populations and their heterogeneous responses to internal and external perturbations. Thus far, most single-cell studies in plant systems have been limited to RNA-sequencing approaches, which only provide indirect readouts of cellular functions. Results Here, we present a single-cell proteomics workflow for plant cells that integrates tape-sandwich protoplasting, piezoelectric cell sorting, nanoPOTS sample preparation, and ion mobility-based MS data acquisition method for label-free single-cell proteomics analysis of Arabidopsis leaf mesophyll cells. From a single leaf protoplast, over 3,000 proteins were quantified with high precision. The workflow is demonstrated to identify stress associated changes in protein abundance by analyzing 117 protoplasts from well-watered and water-deficit stressed plants. Additionally, we describe a new approach for constructing covarying protein networks at the single-cell level and demonstrate how single-cell protein covariation analysis can reveal previously unrecognized protein functions while also capturing stress-induced changes in protein–protein dynamics. Conclusions The label-free scProteomic approach presented here represents a significant advance through the demonstration of a facile protoplast isolation method combined with deep and precise proteomic coverage of Arabidopsis leaf mesophyll cell types. We believe this study will serve as an informative reference to future plant scProteomic investigations.

Arabidopsis↗
DOE OSTI2024

Microwave Flow Cytometric Detection and Differentiation of Escherichia coli

Label-free measurement and analysis of single bacterial cells are essential for food safety monitoring and microbial disease diagnosis. We report a microwave flow cytometric sensor with a microstrip sensing device with reduced channel height for bacterial cell measurement. Escherichia coli B and Escherichia coli K-12 were measured with the sensor at frequencies between 500 MHz and 8 GHz. The results show microwave properties of E. coli cells are frequency-dependent. A LightGBM model was developed to classify cell types at a high accuracy of 0.96 at 1 GHz. Thus, the sensor provides a promising label-free method to rapidly detect and differentiate bacterial cells. Nevertheless, the method needs to be further developed by comprehensively measuring different types of cells and demonstrating accurate cell classification with improved machine-learning techniques.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep learning classification of lipid droplets in quantitative phase images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Scattered–light–sheet microscopy with sub–cellular resolving power

Since its first demonstration over 100 years ago, scattering-based light-sheet microscopy has recently re-emerged as a key modality in label-free tissue imaging and cellular morphometry; however, scattering-based light-sheet imaging with subcellular resolution remains an unmet target to date. This is because related approaches inevitably superimpose speckle or granular intensity modulation on to the native subcellular features. Here, we addressed this challenge by deploying a time-averaged pseudo-thermalized light-sheet illumination. While pseudo-thermalizing the illumination sheet increased its lateral dimensions, we achieved subcellular resolving power at ultralow irradiance levels after image deconvolution. We validated this approach by imaging cytosolic carbon depots in yeast and bacteria with increased specificity and no staining. Altogether, we expect this scattering-based light-sheet microscopy approach will advance live single-cell imaging investigations by conferring low-irradiance and label-free operation towards eradicating phototoxicity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Detection of a multi–disease biomarker in saliva with graphene field effect transistors

Human carbonic anhydrase 1 (CA1) has been suggested as a biomarker for identification of several diseases including cancers, pancreatitis, diabetes, and Sjogren’s syndrome. However, the lack of a rapid, cheap, accurate, and easy-to-use quantification technique has prevented widespread utilization of CA1 for practical clinical applications. To this end, we present a label-free electronic biosensor for detection of CA1 utilizing highly sensitive graphene field effect transistors (G-FETs) as a transducer and specific RNA aptamers as a probe. The binding of CA1 with aptamers resulted in a positive shift in Dirac voltage V D of the G-FETs, the magnitude of which depended on target concentration. These aptameric G-FET biosensors showed the binding affinity (K D ) of ~2.3 ng/ml (70 pM), which is four orders lower than that reported using a gel shift assay. This lower value of K D enabled us to achieve a detection range (10 pg/ml - 100 ng/ml) which is well in line with the clinically relevant range. These highly sensitive devices allowed us to further prove their clinical relevance by successfully detecting the presence of CA1 in human saliva samples. In conclusion, the utilization of this label-free biosensor could facilitate the early stage identification of various diseases associated with changes in concentration of CAs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Integrative Quantitative-Phase and Airy Light-Sheet Imaging

Light-sheet microscopy enables considerable speed and phototoxicity gains, while quantitative-phase imaging confers label-free organelle recognition and metabolic information that are inaccessible by conventional methods. We report the fusion of these two modalities onto a standard inverted microscope that retains compatibility with microfluidics. We describe the utilization of an accelerating Airy-beam light-sheet yielding identical imaging areas with interferometry, and an application in unmasking the effects of cellular noise on metabolic compartmentalization.

Biological sciences, Biological techniques, Micros↗
DOE OSTI2025

Enhanced biochemical sensing with high- Q transmission resonances in free-standing membrane metasurfaces

Optical metasurfaces provide solutions to label-free biochemical sensing by localizing light resonantly beyond the diffraction limit, thereby selectively enhancing light–matter interactions for improved analytical performance. However, high-Q resonances in metasurfaces are usually achieved in the reflection mode, which impedes metasurface integration into compact imaging systems. Here, we demonstrate a metasurface platform for advanced biochemical sensing based on the physics of the bound states in the continuum (BIC) and electromagnetically induced transparency (EIT) modes, which arise when two interfering resonances from a periodic pattern of tilted elliptic holes overlap both spectrally and spatially, creating a narrow transparency window in the mid-infrared spectrum. We experimentally measure these resonant peaks observed in transmission mode (Q ~ 734 at λ ~ 8.8 µm) in free-standing silicon membranes and confirm their tunability through geometric scaling. We also demonstrate the strong coupling of the BIC-EIT modes with a thinly coated PMMA film on the metasurface, characterized by a large Rabi splitting (32 cm -1 ) and biosensing of protein monolayers in transmission mode. Our new photonic platform can facilitate the integration of metasurface biochemical sensors into compact and monolithic optical systems while being compatible with scalable manufacturing, thereby clearing the way for on-site biochemical sensing in everyday applications.

Rosas, Samir [Univ. of Wisconsin, Madison, WI (Uni↗
DOE OSTI2024

Human Host Cellular Response to HCoV-229E Infection Proteomics (ACS-JM-DP2)

The purpose of this experiment was to evaluate the human host cellular response to wild-type Human coronavirus strain 229E (HCoV-229E) infection. Sample data was obtained for mock and infected immortalized human lung epithelial cells (A549) (MOI 5) nuclear extracts, immortalized human lung fibroblasts cells (MRC5) (MOI5) nuclear extracts, and primary human airway epithelial (HAE) (MOI 3) cells from lung tissue and processed for proteome analysis. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files and supporting metadata materials. Experimental proteomics samples were prepared using Limited Proteolysis (LiP) methods for Label-free quantification (LFQ) and global proteomic evaluation. Sample data was acquired using a Q-Exactive HF-X mass spectrometer and was processed and compiled using MaxQuant software (v.1.6.17.0). Processed proteomic data downloads include a sample naming key, processed MaxQuant results/parameters, and protein annotated relative abundance files. See corresponding primary data accessions below and Viral Experiment LiP Analysis source code supporting data transparency and reuse. Experimental transcriptomics samples were collected in parallel and processed for RNA sequencing (RNA-Seq) as summarized under ACS-DP1 (https://data.pnnl.gov/group/nodes/dataset/34069).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. Here we extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across datasets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS datasets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dispersion-Engineered Deep Sub-Wavelength Plasmonic Metasurfaces for Broadband Seira Applications

Surface enhanced infrared absorption (SEIRA) spectroscopy is a powerful tool in which plasmonically enhanced electromagnetic fields provide high-sensitivity molecular detection. Most SEIRA platforms operate at a single resonant frequency, which must be tuned to match that of the target molecule, and commonly rely on time-consuming lithographic techniques. This study presents a high-throughput and cost-effective plasmonic metasurface for broadband, tunable, and strong infrared signal enhancement. The platform is built upon the principle of dispersion-engineered plasmonic Fabry–Pérot (FP) nanocavity arrays. It offers 1) tight squeezing of infrared (IR) photons into deep sub-wavelength nano-volumes and 2) spectrally tunable near-field enhancements of up to ≈10 6 , two to three orders of magnitude higher than most optical metasurface systems. By coupling multilayer nano-thin film deposition and nanoskiving fabrication techniques, the dispersive FP metasurfaces can be rapidly and reproducibly constructed in a scalable and lithography-free manner. Using IR spectroscopy, the selective and sensitive label-free detection of a molecular monolayer is achieved at a range of frequencies. An enhancement factor of nearly 10 5 is measured at the carbonyl (C = O) vibrational marker band of the molecule. The confluence of high field enhancement, broadband plasmonic response, and facile fabrication makes this metasurface a promising platform for SEIRA spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues

Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to enhancements in image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. Here, we have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ~77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Mass Spectrometry-Based High-Throughput Quantification of Bioproducts in Liquid Culture

To meet the ever-increasing need for high-throughput screening in metabolic engineering, information-rich, fast screening methods are needed. Mass spectrometry (MS) provides an efficient and general approach for metabolite screening and offers the capability of characterizing a broad range of analytes in a label-free manner, but often requires a range of sample clean-up and extraction steps. Liquid extraction surface analysis (LESA) coupled MS is an image-guided MS surface analysis approach that directly samples and introduces metabolites from a surface to MS. Here, we combined the advantages of LESA–MS and an acoustic liquid handler with stable isotope-labeled internal standards. This approach provides absolute quantitation of target chemicals from liquid culture-dried droplets and enables high-throughput quantitative screening for microbial metabolites. In this paper, LESA–MS was successfully applied to quantify several different metabolites (itaconic acid, triacetic acid lactone, and palmitic acid) from different yeast strains in different mediums, demonstrating its versatility, accuracy, and efficiency across a range of microbial engineering applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗