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DOE OSTI2020

Understanding the Structure and Dynamics of Complex Biomembrane Interactions by Neutron Scattering Techniques

The membrane is one of the key structural materials of biology at the cellular level. Composed predominantly of a bilayer of lipids with embedded and bound proteins, it defines the boundaries of the cell and many organelles essential to life and therefore is involved in almost all biological processes. Membrane-specific interactions, such as drug binding to a membrane receptor or the interactions of an antimicrobial compound with the lipid matrix of a pathogen membrane, are of interest across the scientific disciplines. Herein we present a review, aimed at nonexperts, of the major neutron scattering techniques used in membrane studies: small-angle neutron scattering, neutron membrane diffraction, neutron reflectometry, quasielastic neutron scattering, and neutron spin echo. Neutron scattering techniques are well suited to studying biological membranes. The nondestructive nature of cold neutrons means that samples can be measured for long periods without fear of beam damage from ultraviolet, electron, or X-ray radiation, and neutron beams are highly penetrating, thus offering flexibility in samples and sample environments. Most important is the strong difference in neutron scattering lengths between the two most abundant forms of hydrogen, protium and deuterium. Changing the relative amounts of protium/deuterium in a sample allows the production of a series of neutron scattering data sets, enabling the observation of differing components within complex membrane architectures. This approach can be as simple as using the naturally occurring neutron contrast between different biomolecules to study components in a complex by changing the solution H 2 O/D 2 O ratio or as complex as selectively labeling individual components with hydrogen isotopes. This review presents an overview of each experimental technique with the neutron instrument configuration, related sample preparation and sample environment, and data analysis, highlighted by a special emphasis on using prominent neutron contrast to understand structure and dynamics. This review gives researchers a practical introduction to the often enigmatic suite of neutron beamlines, thereby lowering the barrier to taking advantage of these large-facility techniques to achieve new understandings of membranes and their interactions with other molecules.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Multimodal imaging of oil-in-water bilgewater emulsion and biofilms

Bilgewater emulsions, consisting of oil and surfactant mixtures in water, affect many aquatic species. Thus development of methods and techniques to treat or mitigate the formation and undesired consequences of shipboard emulsions are urgently needed. This work presents new results of the evolution of the bilgewaer chemical and physical properties using in situ imaging. A Navy O/W emulsion consisting of three oils and a detergent mixture was used as the synthetic bilgewater model [1, 2]. Synthetic oil-in-water (O/W) emulsion droplets were seled in a vacuum compatible microfluidic sample holder and imaged using a suite of tools including scanning electron microscopy (SEM), time-of-flight secondary ion mass spectrometry (ToF-SIMS), confocal laser scanning microscopy, and optical microscopy, highlighting the advantage of multiscale analysis and its applications in waste reduction and treatment in the environment. The transferrable and vacuum compatible microfluidic interface, System for Analysis at the Liquid Vacuum Interface (SALVI), was used in this study. SALVI enabled surface analysis of liquids and liquid-solid interactions using ToF-SIMS and SEM [3, 4]. Its detection window is an aperture of 2 ?m in diameter open to vacuum, permitting direct detection of the liquid surface. Liquid is withheld by surface tension within the aperture. The interface is composed of a silicon nitride membrane and polydimethylsiloxane microchannel [3, 4]. A variety of samples including complex liquid mixtures, ionic liquids, single mammalian cells, live biofilms, and solid-electrolyte interface have been analyzed using in situ imaging [5, 6]. The feasibility of using SALVI for in situ SEM imaging of nanoparticles and colloids in liquid was recently demonstrated [7]. This paper shows new findings of multimodal imaging and analysis of synthesized emulsion particles in water. Figure 1 depicts a schematic of multimodal in situ imaging of bilgewater emulsion using SEM and ToF-SIMS. Furthermore, we study the interactions of microbes and bilgewater emulsion. Microbes play an important role in the ocean. We hypothesize that microbes release organics that can act as surfactants that affect bilgewater formation or weakening. We present the first systematic study of emulsions and biofilms and investigate the effects of biofilms on bilgewater emulsions. Pseudomonas was selected as a model strain. Biofilms were cultured in a microchannel to allow culture [8]. Once a thick layer of biofilms was formed, the medium solution was changed to a mixture consisting of 50 % bilgewater emulsion. Dispersed biofilms were collected at 24 hrs. and 48 hrs. after emulsions were introduced into the channel. Bilgewater emulsions, biofilms, and mixtures of bilgewater emulsions and biofilms were analyzed using multiple in situ and ex situ techniques as described earlier. Our findings indicate that biofilms change the chemical makeup of the emulsion surface compositions and emulsion droplet size distribution, confirming the hypothesis that extracellular polymeric substance (EPS) related components released from biofilms can function as surfactants and change the oil-in-water interfaces.

bilgewater, multimodal imaging, biofilms↗

DOE OSTI2025

Extension of Complex Refractive Index Measurements to the Near-Infrared for Liquids: Methodology and Uncertainty Analysis

Optical identification of liquid droplets, aerosols, or thin films is important for many applications. While reference spectra are sometimes available for such measurements, they are not always applicable to the observed spectrum or the given sample morphology. Reference spectra for many forms can be modeled, however, if the n/k vectors (real and imaginary refractive indices) are available. In previous work we have reported protocols to determine the n/k vectors for dozens of liquids, primarily in the mid-infrared (MIR) spectral range from 7500 to 400 cm –1 . In this work we extend the spectral range into the near-infrared (NIR) region, demonstrating a method to measure and merge the data sets to create composite n/k data ranging from 10 000 to 400 cm –1 (1.0 to 25 µm) with absorbance fidelity spanning over four orders of magnitude, and vastly improved signal-to-noise in the NIR. The precision of the composite data is evaluated for three different liquids, focusing primarily on the steps for converting the raw absorbance spectra to k values. The variability in both MIR and NIR data as well as in the final n/k vectors is also investigated for several liquids. For typical liquids, the overall variability (reported as 2σ) in the final n and k-vectors is determined to be ∼0.4% and 3%, respectively. Finally, the derived n/k data are used to calculate absorbance spectra for aerosol droplets, showing marginal variability due to the typical measurement errors in the final n/k vectors.

47 OTHER INSTRUMENTATION↗

NASA NTRS2022

Titan In-situ Resource Utilization (ISRU) Sample Return (TISR)

Titan is unique in the outer solar system in that it is the only moon with a thick atmosphere, and the only body in the solar system outside the Earth with liquid seas on its surface. The Titanian oceans, however, are seas of liquid hydrocarbons, and the rocks on the surface are solid water ice. Like other icy Moons of the outer solar system, beneath the ice crust, Titan also has a subsurface ocean. Rodriguez et al. refer to it as the “world with two oceans”, an organic-rich body with interior-surface-atmosphere interactions that are comparable in complexity to the Earth. [1] Titan is scientifically fascinating in many ways [2], [3], [4]. The Compass Team will emphasize just one here: Titan is a high priority target for astrobiology [4] [5] [6] [7] [8] [9] [10] [11]. It is a world with a surface and atmosphere rich in the complex organic compounds known as tholins. A detailed understanding of the nature of these complex compounds will require an analysis using a full laboratory on Earth. Because of its value to understanding the organic compounds of the outer solar system which may be the primordial building-blocks of life, return of samples from Titan to laboratories on Earth will be the primary goal of this mission. While this would give unprecedented science return, returning even a small sample from Titan using conventional technology would be tremendously difficult. Saturn is almost a billion miles from the Earth, about thirteen times farther than Mars. A return mission to Saturn requires such a large total-mission ∆V that, with conventional technology, the mass ratios required are prohibitive. Such a sample return would truly be “mission incredible.” But to date, a sample return mission from so distant a target has been assumed to be, not merely incredible, but mission impossible. The Compass Team has proposed [2] [12] that by manufacturing the propellant for the return to Earth using the resources available on Titan, such a mission becomes possible. The task of this report is to show that it is reasonable with credible space technology.

Titan↗

DOE OSTI2025

Quantum algorithm to simulate Lindblad master equations

We present a quantum algorithm for simulating a family of Markovian master equations that can be realized through a probabilistic application of unitary channels and state preparation. Our approach employs a second-order product formula for the Lindblad master equation, achieved by decomposing the dynamics into dissipative and Hamiltonian components and replacing the dissipative segments with randomly compiled, easily implementable elements. The sampling approach eliminates the need for ancillary qubits to simulate the dissipation process and reduces the gate complexity in terms of the number of jump operators. We provide a rigorous performance analysis of the algorithm. We also extend the algorithm to time-dependent Lindblad equations, generalize the family of Markovian master equations it can be applied to, and explore applications beyond the Markovian noise model. A new error bound, in terms of the diamond norm, for second-order product formulas for time-dependent Liouvillians is provided that might be of independent interest. Published by the American Physical Society 2025

Borras, Evan (ORCID:000900017709037X)↗

DOE OSTI2021

DOE JGI Metagenome Workflow

The DOE Joint Genome Institute (JGI) Metagenome Workflow performs metagenome data processing, including assembly; structural, functional, and taxonomic annotation; and binning of metagenomic data sets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) (I.-M. A. Chen, K. Chu, K. Palaniappan, A. Ratner, et al., Nucleic Acids Res, 49:D751–D763, 2021, https://doi.org/10.1093/nar/gkaa939) comparative analysis system and provided for download via the JGI data portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here, we describe the different tools, databases, and parameters used at different steps of the workflow to help with the interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a workflow description language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in the Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, et al., Nucleic Acids Res, 49:D723–D733, 2021, https://doi.org/10.1093/nar/gkaa983).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Nb3Sn Coating of Twin Axis Cavity for SRF Applications

The twin axis cavity with two identical accelerating beams has been proposed for Energy recovery linac (ERL) applications. Nb3Sn is a superconducting material with a higher critical temperature and a higher critical field as compared to Nb, which promises a lower operating cost due to higher quality factors. Two niobium twin axis cavities fabricated at JLab and were proposed to be coated with Nb3Sn. Due to their more complex geometry, the typical coating process used for basic elliptical cavities needs to be improved to coat these cavities. This devel-opment advances the current coating system at JLab for coating complex cavities. Two twin axis cavities were coated recently for the first time. This contribution dis-cusses initial results from coating of twin axis cavities, RF testing and witness sample analysis with an overview of the current challenges towards high performance Nb3Sn coated twin axis cavities.

Tiskumara, J.↗

DOE OSTI2022

Nb₃Sn Coating of Twin Axis Cavity for SRF Applications

The twin axis cavity with two identical accelerating beams has been proposed for energy recovery linac (ERL) applications. Nb₃Sn is a superconducting material with a higher critical temperature and a higher critical field as compared to Nb, which promises a lower operating cost due to higher quality factors. Two niobium twin axis cavities were fabricated at JLab and were proposed to be coated with Nb₃Sn. Due to their more complex geometry, the typical coating process used for basic elliptical cavi-ties needs to be improved to coat these cavities. This development advances the current coating system at JLab for coating complex cavities. Two twin axis cavities were coated recently for the first time. This contribution dis-cusses initial results from coating of twin axis cavities, RF testing and witness sample analysis with an overview of the current challenges towards high performance Nb₃Sn coated twin axis cavities.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Proteomic Analysis of Exosomes for Discovery of Protein Biomarkers for Prostate and Bladder Cancer

Extracellular vesicles (EVs) are released by nearly all cell types as part of normal cell physiology, transporting biological cargo, including nucleic acids and proteins, across the cell membrane. In pathological states such as cancer, EV-derived cargo may mirror the altered state of the cell of origin. Exosomes are the smaller, 50–150 nanometer-sized EVs released from fusion of multivesicular endosomes with the plasma membrane. Exosomes play important roles in cell-cell communication and participate in multiple cancer processes, including invasion and metastasis. Therefore, proteomic analysis of exosomes is a promising approach to discover potential cancer biomarkers, even though it is still at an early stage. Herein, we critically review the advances in exosome isolation methods and their compatibility with mass spectrometry (MS)-based proteomic analysis, as well as studies of exosomes in pathogenesis and progression of prostate and bladder cancer, two common urologic cancers whose incidence rates continue to rise annually. As urological tumors, both urine and blood samples are feasible for noninvasive or minimally invasive analysis. A better understanding of the biological cargo and functions of exosomes via high-throughput proteomics will help provide new insights into complex alterations in cancer and provide potential therapeutic targets and personalized treatment for patients.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1975

Skylab M518 multipurpose furnace convection analysis

An analysis was performed of the convection which existed on ground tests and during skylab processing of two experiments: vapor growth of IV-VI compounds growth of spherical crystals. A parallel analysis was also performed on Skylab experiment indium antimonide crystals because indium antimonide (InSb) was used and a free surface existed in the tellurium-doped Skylab III sample. In addition, brief analyses were also performed of the microsegregation in germanium experiment because the Skylab crystals indicated turbulent convection effects. Simple dimensional analysis calculations and a more accurate, but complex, convection computer model, were used in the analysis.

Bourgeois, S. V.↗

NASA NTRS1992

Characterization of low thermal conductivity PAN-based carbon fibers

The microstructure and surface chemistry of eight low thermal conductivity (LTC) PAN-based carbon fibers were determined and compared with PAN-based fibers heat treated to higher temperatures. Based on wide-angle x ray diffraction, the LTC PAN fibers all appear to have a similar turbostratic structure with large 002 d-spacings, small crystallite sizes, and moderate preferred orientation. Limited small-angle x ray scattering (SAXS) results indicate that, with the exception of LTC fibers made by BASF, the LTC fibers do not have well developed pores. Transmission electron microscopy shows that the texture of the two LTC PAN-based fibers studied (Amoco T350/23X and /25X) consists of multiple sets of parallel, wavy, bent layers that interweave with each other forming a complex three dimensional network oriented randomly around the fiber axis. X ray photoelectron spectroscopy (XPS) analysis finds correlations between heat treated temperatures and the surface composition chemistry of the carbon fiber samples.

Katzman, Howard A.↗

DOE OSTI2020

Leveraging Machine Learning Capabilities for the Characterization of Irradiated Uranium: A Case Study of Analysis Methods for Nuclear Safeguards and Nuclear Forensics

Nondestructively determining the initial enrichment of irradiated uranium is a complex and laborious multivariable problem due to the presence of fission products. This work demonstrates the capabilities of machine learning to analyze gamma-ray spectral data to determine initial enrichment without knowledge of the decay time of the sample. The approach developed is agnostic to the particular scenario and is applicable to a wide variety of applications in nuclear forensics and nuclear safeguards. We irradiated 5 mg uranium standard reference materials at discrete enrichment values ranging from 0.02% to 97% 235 U (weight percent) in UT Austin’s Nuclear Engineering Teaching Laboratory TRIGA Mark II 1.1 MW research reactor, allowed each to decay for 8 hours, and then measured each sample via gamma-ray spectrometry for 50 hours post-irradiation yielding 1,400 individual gamma-ray spectra discretized into 8,192 energy bins. We then trained decision trees models to analyze individual gamma-ray spectra and estimate the associated initial enrichment without knowledge of the time since end of irradiation. We evaluated the performance of the models with a reserved test set not used for training or calibrating the model. A decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 2.3% (weight percent 235 U). Next, we implemented a principal component analysis pre-processing routine of the gamma-ray spectrometry data to reduce the dimensionality of the dataset from 8,192 channels in the spectrum to 10 principal components while retaining over 99% of the inherent variance in the data. Decision tree models constructed with these data demonstrated decreased mean absolute error in enrichment determination, reduced computational time, and decreased complexity. A single decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 0.05% (weight percent 235 U). Furthermore, we analyzed these models with learning curves to ensure that overfitting did not occur. The capabilities provided by these models can be naturally extended to other application-focused measurements in the fields of nuclear safeguards, nuclear forensics, and nuclear non-proliferation.

Drescher, Adam↗

NASA NTRS2004

A Novel Biomedical Device Utilizing Light Emitting Nano-Structures

This paper will discuss the development of a novel biomedical detection device that will be used to detect microorganisms with the use of infrared fluorochrome polymers attached to antibodies in fluids such as water. The fluorochrome polymers emit light in the near inferred region (NIR), approximately 805 nm, when excited by an NIR laser at 778 nm. The device could remarkably change the way laboratory testing is done today. The testing process is usually performed on a time scale of days while our device will be able to detect microorganisms in minutes. This type of time efficient analysis is ideal for use aboard the International Space Station and the Space Shuttle (ISS/SS) and has many useful commercial applications, for instance at a water treatment plant and food processing plants. With more research and experimentation the testing might also one day be used to detect bacteria and viruses in complex fluids such as blood, which would revolutionize blood analysis as it is performed today. My contribution to the project has been to develop a process which will allow an antibody/fluorescent dye pair to be conjugated to a specific bacteria or virus and than to to be separated from a sample body of water for detection. The antibody being used in this experiment is anti beta galactosidase and its complement enzyme is beta galactosidase, a non harmful derivative of E. Coli. The anti beta galactosidase has been conjugated to the fluorochrome polymer, IRDye800, which emits at approximately 806 nm. The dye when excited by the NIR laser emits a signal which is detected by a spectrometer and then is read by state of the art computer software. The state-of-the-art process includes incubating the anti beta galactosidase and beta galactosidase in a phosphate buffer solution in a test tube, allowing the antibody to bind to specific sites on the enzyme. After the antibody is bound to the enzyme, it is centrifuged in specific filters that will allow free antibody to wash away and leave the antibody-enzyme complexes on top in solution for testing and analysis. This solution is pipetted into a cuvette, a special plastic test tube, which will then be excited by the laser. The signal read will tell US that an antibody is present and since it is bound to the enzyme, that the bacteria is also present.

Varaljay, Vanessa A.↗

NASA NTRS2008

Compound-Specific Isotope Analysis of Amino Acids for Stardust-Returned Samples

Significant portions of the early Earth's prebiotic organic inventory , including amino acids, could have been delivered to the Earth's sur face by comets and their fragments. Analysis of comets via spectrosc opic observations has identified many organic molecules, including me thane, ethane, arnmonia, cyanic acid, formaldehyde, formamide, acetal ehyde, acetonitrile, and methanol. Reactions between these identifie d molecules could allow the formation of more complex organics such a s amino acids. Isotopic analysis could reveal whether an extraterrest rial signature is present in the Stardust-exposed amines and amino ac ids. Although bulk isotopic analysis would be dominated by the EACA contaminant's terrestrial signature, compoundspecific isotope analysi s (CSIA) could determine the signature of each of the other individua l amines. Here, we report on progress made towards CSIA of the amino acids glycine and EACA in Stardustreturned samples.

Cook, Jamie↗

DOE OSTI2020

Sample Preparation for 3D Characterization of Irradiated Fuel

Thorough characterization of nuclear fuel is essential when striving to understand its microstructural evolution in response to varying irradiation conditions. A detailed understanding of the microstructural evolution of irradiated fuel can be useful when predicting the fuel’s response to off-normal conditions and related phenomena such as high burnup fuel fragmentation (HBFF) and fission gas release. This sort of characterization and the study of complex phenomena like HBFF that are related to fuel performance are an important part of the US Department of Energy Office of Nuclear Energy’s Advanced Fuels Campaign (AFC). Multiple characterization techniques are commonly used to perform post-irradiation examination (PIE) analysis on an irradiated fuel sample, but many of these techniques are limited to the 2D surface analysis of the fuel pellet. Although there is a lot of useful information to be gained by analyzing the fuel pellet surface, a 2D analysis provides only an approximation of the nature of the material’s features, such as porosity and grain structure. Therefore, it is critical to analyze the 3D structure of the material, as materials exist and respond to conditions in all three dimensions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Galaxy bias in the era of LSST: perturbative bias expansions

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. Here, we consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of k max = 0.4 Mpc -1 for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2004

Feature-Identification and Data-Compression Software

A report discusses the continuing development of Windows Interface for Nominal Displacement Selection (WINDS), a computer program for automated analysis of images of the Sun and planets acquired by scientific instruments aboard spacecraft. WINDS is intended to afford capabilities for identification of features, measurement of displacements and velocities, analysis of terrain and of atmospheres, and synthesis of animation sequences of images of terrains and atmospheres from small sets of samples by use of velocity-based interpolation. A major element of WINDS will be a nonlinear correlator capable of tracking small features in complex image sequences. For dynamic image sequences, the correlator will enable compression of data by factors >100. In processing image data, WINDS will take account of such factors as texture in image data, rotation of features during measurement intervals, effects of viewing and solar illumination angles, and vertical structures of atmospheres. WINDS will also take account of positions, aiming directions, and fields of view of cameras to determine three-dimensional feature structures by use of triangulation and stereoscopic analysis techniques.

Jong, Eric De↗

DOE OSTI2020

Sensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing Platform

Top-down proteomics is a powerful tool for characterizing genetic variations and post-translational modifications at intact protein level. That being said, one significant technical gap of top-down proteomics is the inability to analyze low amount of biological samples, which limits its access to isolated rare cells, fine-needle aspiration biopsies, and tissue substructures. Herein, we developed an ultrasensitive top-down platform by incorporating a microfluidic sample preparation system, termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples), into a top-down proteomics workflow. A unique combination of a nonionic detergent dodecyl-ß-D-maltopyranoside (DDM) with urea as protein extraction buffer significantly improved both protein extraction efficiency and sample recovery. We hypothesize that the DDM detergent improves protein recovery by efficiently reducing non-specific adsorption of intact proteins on container surfaces, while urea serves as a strong denaturant to disrupt noncovalent complexes and release intact proteins for downstream analysis. The nanoPOTS-based top-down platform reproducibly and quantitatively identified ~170 to ~620 proteoforms from ~70 to ~770 HeLa cells containing ~10 to ~115 ng of total protein. A variety of post-translational modifications including acetylation, myristoylation, and iron binding were identified using only less than 800 cells. We anticipate the nanoPOTS top-down proteomics platform will be broadly applicable in biomedical researches, particularly where clinical specimens are not available in amounts amenable to standard workflows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗