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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Fibrosis biomarkers in workers exposed to MWCNTs

Multi-walled carbon nanotubes (MWCNT) with their unique physico-chemical properties offer numerous technological advantages and are projected to drive the next generation of manufacturing growth. As MWCNT have already found utility in different industries including construction, engineering, energy production, space exploration and biomedicine, large quantities of MWCNT may reach the environment and inadvertently lead to human exposure. This necessitates the urgent assessment of their potential health effects in humans. The current study was carried out at NanotechCenter Ltd. Enterprise (Tambov, Russia) where large-scale manufacturing of MWCNT along with relatively high occupational exposure levels was reported. The goal of this small cross-sectional study was to evaluate potential biomarkers during occupational exposure to MWCNT. All air samples were collected at the workplaces from both specific areas and personal breathing zones using filter-based devices to quantitate elemental carbon and perform particle analysis by TEM. Biological fluids of nasal lavage, induced sputum and blood serum were obtained from MWCNT-exposed and non-exposed workers for assessment of inflammatory and fibrotic markers. It was found that exposure to MWCNTs caused significant increase in IL-1β, IL6, TNF-α, inflammatory cytokines and KL-6, a serological biomarker for interstitial lung disease in collected sputum samples. Moreover, the level of TGF-β1 was increased in serum obtained from young exposed workers. Overall, the results from this study revealed accumulation of inflammatory and fibrotic biomarkers in biofluids of workers manufacturing MWCNTs. Therefore, the biomarkers analyzed should be considered for the assessment of health effects of occupational exposure to MWCNT in cross-sectional epidemiological studies. - Highlights: • The effects of MWCNT exposure in humans remain unclear. • We found increased KL-6/TGF-β levels in the biofluids of MWCNT-exposed workers. • Inflammatory cytokines content was also increased in these workers. • The identified markers could be useful for potential health surveillance in exposed populations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Local Resistance Measurement for Degradation of c-Si Heterojunction with Intrinsic Thin Layer (HIT) Solar Modules

Silicon heterojunction with intrinsic thin layer (or HIT) modules typically degrade at a rate of less than 1% annually in solar fields with dominant degradation in open-circuit voltage and some degradation in series resistance. However, detailed mechanisms can differ from module to module. Here, we study increases in local series resistance that occur over long-term field deployment, indicated by cell areas where the photoluminescence intensity does not degrade but the electroluminescence degrades significantly. To directly measure the local series resistance, we have cored out the local electroluminescence-degraded area, and we measured the sheet resistance by 4-point-probe and local nm-scale resistance using scanning spreading resistance microscopy (SSRM). The results by 4-point-probe show scattered sheet resistance that can be caused, for example, by nonuniform current paths through the transparent conductive oxide layer, the a-Si:H emitter, or the near-junction c-Si inversion layer. In contrast, the SSRM results indicate a relatively uniform and non-degraded resistivity on smaller nanometer spatial scales. SSRM is an atomic force microscopy-based two-terminal resistance mapping technique that measures the local resistance in nm-volume beneath the probe. The consistent resistances measured on the control and degraded samples can exclude the degradation of transparent conductive oxide resistance.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2014

Kinetics of solid-gas reactions characterized by scanning AC nano-calorimetry with application to Zr oxidation

Scanning AC nano-calorimetry is a recently developed experimental technique capable of measuring the heat capacity of thin-film samples of a material over a wide range of temperatures and heating rates. Here, we describe how this technique can be used to study solid-gas phase reactions by measuring the change in heat capacity of a sample during reaction. We apply this approach to evaluate the oxidation kinetics of thin-film samples of zirconium in air. The results confirm parabolic oxidation kinetics with an activation energy of 0.59 ± 0.03 eV. The nano-calorimetry measurements were performed using a device that contains an array of micromachined nano-calorimeter sensors in an architecture designed for combinatorial studies. We demonstrate that the oxidation kinetics can be quantified using a single sample, thus enabling high-throughput mapping of the composition-dependence of the reaction rate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

macroMS: Image-Guided Analysis of Random Objects by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Mass spectrometry imaging is well-suited to characterizing sample surfaces for their chemical content in a spatially resolved manner. However, when the surface contains small objects with significant empty spaces between them, more efficient approaches to sample acquisition are possible. Image-guided mass spectrometry (MS) enables high-throughput analysis of a diverse range of sample types, such as microbial colonies, liquid microdroplets, and others, by recognizing and analyzing selected location targets in an image. Here, we describe an imaging protocol and macroMS, an online software suite that can be used to enhance MS measurements of macroscopic samples that are imaged by a camera or a flatbed scanner. Furthermore, the web-based tool enables users to find and filter targets from the optical images, correct optical distortion issues for improved spatial location of selected targets, input the custom geometry files into an MS device to acquire spectra at the selected locations, and finally, perform limited data analysis and use visualization tools to aid locating samples containing compounds of interest. Using the macroMS suite, an enzyme mutant library of Saccharomyces cerevisiae and nL droplet arrays of Escherichia coli and Pseudomonas fluorescens have been assayed at a rate of ~2 s/sample.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Plume collimation for laser ablation electrospray ionization mass spectrometry

In various embodiments, a device may generally comprise a capillary having a first end and a second end; a laser to emit energy at a sample in the capillary to ablate the sample and generate an ablation plume in the capillary; an electrospray apparatus to generate an electrospray plume to intercept the ablation plume to produce ions; and a mass spectrometer having an ion transfer inlet to capture the ions. The ablation plume may comprise a collimated ablation plume. The device may comprise a flow cytometer. Methods of making and using the same are also described.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Mechanism of γ-irradiation induced phase transformations in nanocrystalline Mn{sub 0.5}Zn{sub 0.5}Fe{sub 2}O{sub 4} ceramics

The structural, infrared absorption and magnetic property transformations in nanocrystalline Mn{sub 0.5}Zn{sub 0.5}Fe{sub 2}O{sub 4} samples irradiated with different doses (0, 15, 25 and 50 kGy) of γ-irradiation were investigated in this work and a mechanism of phase transformation/decomposition is provided based on the metastable nature of the Mn-atoms in the spinel lattice. The nano-powder sample was prepared by solution combustion route and the pellets of the sample were exposed to γ-radiation. Up to a dose of 25 kGy of γ-radiation, the sample retained the single phase cubic spinel (Fd-3m) structure, but the disorder in the sample increased. On irradiating the sample with 50 kGy γ-radiation, the spinel phase decomposed into new stable phases such as α-Fe{sub 2}O{sub 3} and ZnFe{sub 2}O{sub 4} phases along with amorphous MnO phase, leading to a change in the surface morphology of the sample. Along with the structural transformations the magnetic properties deteriorated due to breakage of the ferrimagnetic order with higher doses of γ-irradiation. Our results are important for the understanding of the stability, durability and performance of the Mn-Zn ferrite based devices used in space applications. - Graphical abstract: The nanocrystalline Mn{sub 0.5}Zn{sub 0.5}Fe{sub 2}O{sub 4} ceramic sample transforms to crystalline α-Fe{sub 2}O{sub 3} and ZnFe{sub 2}O{sub 4} phases (and amorphous MnO phase) at a γ-irradiation dose of 50 kGy, as MnO goes out of the spinel lattice. The high energy γ-irradiation causes structural damage to the nanomaterials leading to change in morphology of the sample as seen in the SEM images. - Highlights: • Mn atoms are more unstable in the Mn-Zn ferrite spinel lattice than Zn-atoms. • Displacement of Mn atoms by γ-radiation from the lattice renders phase transformation. • In Mn{sub 0.5}Zn{sub 0.5}Fe{sub 2}O{sub 4}, Mn-ferrite cell transforms to crystalline α-Fe{sub 2}O{sub 3} and amorphous MnO. • The stable ZnFe{sub 2}O{sub 4} phase retains its structure even after 50 KGy γ-irradiation. • The γ-irradiation degrades the magnetic properties of Mn{sub 0.5}Zn{sub 0.5}Fe{sub 2}O{sub 4} ceramics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2025

NLR HPC Eagle GPU Node Metrics

Ganglia node metrics and iLO (Integrated Lights Out) power data captured from six representative Eagle GPU nodes The Eagle HPC operated at NLR from 2019 through 2024. Eagle was a 2,000-node, 8-petaflop system. This dataset is a representative sample of metrics for 6 of the GPU nodes. Each GPU node contained 2 CPUs and 2 GPUs. Data provided in compressed CSV format. Ganglia and iLO Power Time Series Fields ts: Timestamp dv: Device / Node - Rack and Unit - r103u17 == r(ack)103u(nit)17 mt: Metric (only present for Ganglia) vl: Value - Value in watts for iLO power (instantaneous value at sampling time) or specified Ganglia metric below Ganglia Metrics Metric name -- Metric description -- Unit cpu_aidle -- Percent of time since boot idle CPU -- Percent cpu_idle -- Percent CPU idle -- Percent cpu_nice -- Percent CPU nice -- Percent cpu_speed -- Speed in MHz of CPU -- MHz cpu_user -- Percent CPU user -- Percent cpu_wio -- The percentage of CPU Wait I/O -- Percent gpu0_bar1_memory -- Used GPU bar1 memory -- MB gpu0_decoder_util -- GPU decoder utilization -- Percent gpu0_ecc_db_error -- Total ECC error counts for the GPU -- Number gpu0_encoder_util -- GPU encoder utilization -- Percent gpu0_fan -- Fan speed -- RPM gpu0_fb_memory -- Used GPU framebuffer memory -- MB gpu0_graphics_clock_report -- Current clock speeds for the device -- MHz gpu0_mem_total -- Memory total -- MB gpu0_mem_util -- Memory utilization -- Percent gpu0_power_usage_report -- Power usage report -- Watts gpu0_temp -- GPU 1 temperature -- Celsius gpu1_bar1_memory -- Used GPU bar1 memory -- MB gpu1_decoder_util -- GPU decoder utilization -- Percent gpu1_ecc_db_error -- Total ECC error counts for the GPU -- Number gpu1_encoder_util -- GPU encoder utilization -- Percent gpu1_fan -- Fan speed -- RPM gpu1_fb_memory -- Used GPU framebuffer memory -- MB gpu1_graphics_clock_report -- Current clock speeds for the GPU -- MHz gpu1_mem_total -- Memory total -- MB gpu1_mem_util -- Memory utilization -- MB gpu1_power_usage_report -- Power usage report -- Watts gpu1_temp -- GPU 1 temperature -- Celsius ipmi_cpu1_temp -- CPU 1 temperature -- Celsius ipmi_cpu2_temp -- CPU 2 temperature -- Celsius ipmi_inlet_ambient_temp -- Temperature measured at intake -- Celsius ipmi_vr_p1_temp -- CPU 1 voltage regulator temperature -- Celsius ipmi_vr_p2_temp -- CPU 2 voltage regulator temperature -- Celsius mem_buffers -- Amount of buffered memory -- Bytes mem_cached -- Amount of cached memory -- Bytes mem_free -- Amount of available memory -- Bytes mem_shared -- Amount of shared memory -- Bytes mem_total -- Amount of available memory -- Bytes

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Analysis of Powders Containing Illicit Drugs Using Magnetic Levitation

Magneto-Archimedes levitation (MagLev) enables the separation of powdered mixtures of illicit drugs (cocaine, methamphetamine, heroin, fentanyl, and its analogues), adulterants, and diluents based on density, and allows the presumptive identification of individual components. Small samples (mass <50 mg), with low concentrations of illicit drugs, present a particular challenge to analysis for forensic chemists. The MagLev device, a cuvette containing a solution of paramagnetic gadolinium(III) chelate in a non-polar solvent, placed between two like-poles-facing NdFeB magnets, allowed separation of seven relevant compounds simultaneously. In particular, initial separation with MagLev, followed by characterization by FTIR-ATR, enabled identification of fentanyl in a sample of fentanyl-laced heroin (1.3 wt % fentanyl, 2.6 wt % heroin, and 96.1 wt % lactose). MagLev allows identification of unknown powders in mixtures and enables confirmatory identification based on structure-specific techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2023

Kinetic Suppression of Photoinduced Halide Migration in Wide Bandgap Perovskites via Surface Passivation

In this work, we study the kinetics of photoinduced halide migration in FA 0.8 Cs 0.2 Pb(I 0.8 Br 0.2 ) 3 wide (~1.69 eV) bandgap perovskites and show that halide migration slows down following surface passivation with (3-aminopropyl) trimethoxysilane (APTMS). We use scanning Kelvin probe microscopy (SKPM) to probe the contact potential difference (CPD) shift under illumination and the kinetics of surface potential relaxation in the dark. Our results show that APTMS-passivated perovskites exhibit a smaller CPD shift under illumination and a slower surface potential relaxation in the dark. We compare the evolution of the photoluminescence spectra of APTMS-passivated and unpassivated perovskites under illumination. We find that APTMS-passivated perovskites exhibit more than 5 times slower photoluminescence red-shift, consistent with the slower surface potential relaxation as observed by SKPM. In conclusion, these observations provide evidence for kinetic suppression of photoinduced halide migration in APTMS-passivated samples, likely due to reduced halide vacancy densities, opening avenues to more efficient and stable devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Broadband Wide-Angle VElocity Selector (BWAVES) neutron spectrometer designed for the SNS Second Target Station

A recently proposed wide-angle velocity selector (WAVES) device for choosing the velocity of detected neutrons after they have been scattered by the sample paves the way for inverted geometry neutron spectrometers with continuously adjustable final neutron wavelength. BWAVES broadband inverted geometry spectrometer proposed for the Second Target Station at the Spallation Neutron Source at Oak Ridge National Laboratory is designed using WAVES to simultaneously probe dynamic processes spanning 4.5 decades in time (energy transfer). This makes BWAVES a uniquely flexible instrument which can be viewed as either a quasielasitc neutron scattering (QENS) spectrometer with a practically unlimited (overlapping with the vibrational excitations) range of energy transfers, or a broadband inelastic vibrational neutron spectrometer with QENS capabilities, including a range of accessible momentum transfer (Q) and a sufficiently high energy resolution at the elastic line. The new capabilities offered by BWAVES will expand the application of neutron scattering in ways not possible with existing neutron spectrometers.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Optically detected magnetic resonance imaging

Optically detected magnetic resonance provides ultrasensitive means to detect and image a small number of electron and nuclear spins, down to the single spin level with nanoscale resolution. Despite the significant recent progress in this field, it has never been combined with the power of pulsed magnetic resonance imaging techniques. Here, we demonstrate how these two methodologies can be integrated using short pulsed magnetic field gradients to spatially encode the sample. This result in what we denote as an 'optically detected magnetic resonance imaging' technique. It offers the advantage that the image is acquired in parallel from all parts of the sample, with well-defined three-dimensional point-spread function, and without any loss of spectroscopic information. In addition, this approach may be used in the future for parallel but yet spatially selective efficient addressing and manipulation of the spins in the sample. Such capabilities are of fundamental importance in the field of quantum spin-based devices and sensors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Investigation of electrochemical migration on Sn-0.7Cu-0.3Ag-0.03P-0.005Ni solder alloy in HNO{sub 3} solution

Current issue in lead-free solder in term of its reliability is still under investigation. This high impact research attempts to investigate the electrochemical migration (ECM) on Sn-0.7Cu-0.3Ag-0.03P-0.005Ni solder alloy by Water Drop Test (WDT) in different concentration of HNO{sub 3} solution. The concentration of HNO{sub 3} solution used in this research was 0.05, 0.10, 0.50 and 1M. Optical Microscope (OM), Field Emission Scanning Electron Microscope (FESEM) and Energy Dispersive X-Ray Analysis (EDX) were carried out in order to analysis the ECM behavior based on the growth of dendrite formation after WDT. In general, the results demonstrated that dendrite growth is faster in higher concentration compared with low concentration of HNO{sub 3}. The concentration of HNO{sub 3} solution used has a strong correlation with Mean-Time-To-Failure (MTTF). As the concentration of HNO{sub 3} increases, the MTTF value decreases. Based on the MTTF results the solder alloy in 1M HNO{sub 3} solution is most susceptible to ECM. SnO{sub 2} forms as a corrosion by-product in the samples proved by EDX analysis. The solder alloy poses a high reliability risk in microelectronic devices during operation in 1M HNO{sub 3} solution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Controlling polarization of spintronic THz emitter by remanent magnetization texture

Terahertz (THz) sciences and technologies have contributed to a rapid development of a wide range of applications and expanded the frontiers in fundamental science. Spintronic terahertz emitters offer conceptual advantages since the spin orientation in the magnetic layer can be easily controlled either by the externally applied magnetic field or by the internal magnetic field distribution determined by the specific shape of the magnetic elements. Here, we report a switchable terahertz source based on micropatterned magnetic heterostructures driven by femtosecond laser pulses. We show that the precise tunability of the polarization state is facilitated by the underlying magnetization texture of the magnetic layer that is dictated by the shape of the microstructure. These results also reveal the underlying physical mechanisms of a nonuniform magnetization state on the generation of ultrafast spin currents in the magnetic heterostructures. Our findings indicate that the emission of the linearly polarized THz waves can be switched on and off by saturating the sample using a biasing magnetic field, opening fascinating perspectives for integrated on-chip THz devices with wide-ranging potential applications.

Wu, Weipeng↗
DOE OSTI2016

A graphite oxide (GO)-based remote readable tamper evident seal

Here, this paper presents a prototype of a remotely readable graphite oxide (GO) paper-based tamper evident seal. The proposed device combines the tunable electrical properties offered by reduced graphite oxide (RGO) with a compressive sampling scheme. The benefit of using RGO as a tamper evident seal material is the sensitivity of its electrical properties to the common mechanisms adopted to defeat tamper-evident seals. RGO’s electrical properties vary upon local stress or cracks induced by mechanical action (e.g., produced by shimming or lifting attacks). Further, modification of the seal’s electrical properties can result from the incidence of other defeat mechanisms, such as temperature changes, solvent treatment and steam application. The electrical tunability of RGO enables the engraving of a circuit on the area of the tamper evident seal intended to be exposed to malicious attacks. The operation of the tamper evident seal, as well as its remote communication functionality, is supervised by a microcontroller unit (MCU). The MCU uses the RGO-engraved circuitry to physically implement a compressive sampling acquisition procedure. The compressive sampling scheme provides the seal with self-authentication and self-state-of-health awareness capabilities. Finally, the prototype shows potential for use in low-power, embedded, remote-operation nonproliferation security related applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An automated platform for in situ serial crystallography at room temperature

Direct observation of functional motions in protein structures is highly desirable for understanding how these nanomachineries of life operate at the molecular level. Because cryogenic temperatures are non-physiological and may prohibit or even alter protein structural dynamics, it is necessary to develop robust X-ray diffraction methods that enable routine data collection at room temperature. We recently reported a crystal-on-crystal device to facilitate in situ diffraction of protein crystals at room temperature devoid of any sample manipulation. Here an automated serial crystallography platform based on this crystal-on-crystal technology is presented. A hardware and software prototype has been implemented, and protocols have been established that allow users to image, recognize and rank hundreds to thousands of protein crystals grown on a chip in optical scanning mode prior to serial introduction of these crystals to an X-ray beam in a programmable and high-throughput manner. This platform has been tested extensively using fragile protein crystals. We demonstrate that with affordable sample consumption, this in situ serial crystallography technology could give rise to room-temperature protein structures of higher resolution and superior map quality for those protein crystals that encounter difficulties during freezing. This serial data collection platform is compatible with both monochromatic oscillation and Laue methods for X-ray diffraction and presents a widely applicable approach for static and dynamic crystallographic studies at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗