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A method for preparing an electrochemical biosensor uses bias-assisted assembly of unreactive -onium molecules on an electrode array followed by post-assembly electro-addressable conversion of the unreactive group to a chemical or biological recognition group. Electro-addressable functionalization of electrode arrays enables the multi-target electrochemical sensing of biological and chemical analytes.
A system and method of using one or more DC-DC/DC-AC converters and/or alternative devices allows strings of multiple module technologies to coexist within the same PV power plant. A computing (optimization) framework estimates the percentage allocation of PV power plant capacity to selected PV module technologies. The framework and its supporting components considers irradiation, temperature, spectral profiles, cost and other practical constraints to achieve the lowest levelized cost of electricity, maximum output and minimum system cost. The system and method can function using any device enabling distributed maximum power point tracking at the module, string or combiner level.
A method of controlling a vehicle includes determining a current operating situation of the vehicle, and identifying a subset of a plurality of sensors of the vehicle needed to provide data to enable a vehicle control function for the current operating situation of the vehicle. A remainder of the plurality of sensors is disengaged to reduce electric energy usage by the vehicle while the vehicle is operating in the current operating situation of the vehicle. A sampling rate for the selected subset of sensors may be reduced to further reduce energy usage of the vehicle. Additionally, an energy reduction processing strategy may be implemented to reduce a processor frequency or a voltage of a computing device used to provide the vehicle control function to further reduce energy usage of the vehicle.
As the world endures environmental crises associated with climate change and the rise of atmospheric CO2 concentrations from anthropogenic CO2 emission, carbon capture and utilization (CCU) technologies are increasingly necessary. Reactive carbon capture (RCC) technologies, in which capture and conversion of CO2 occur in a single reactor, are more energetically and economically attractive by avoiding the need to purify, compress, and transport the captured CO2. To this end, the development of dual function materials (DFM) that enable CO2 capture and conversion to useful C1 products, namely (1) methane, (2) CO, and (3) methanol, are attractive near-term targets for commercial CCU processes for renewable fuels and chemicals. DFM are composed of sorbents and catalysts co-dispersed on the same high surface area carrier. The sorbent component allows for selective capture of CO2 from a gas stream and the catalyst component subsequently performs the in-situ conversion of the adsorbed CO2 upon introduction of a reactive gas (typically H2). The survey of DFM formulations reveals a variety of sorbent+catalyst combinations of interest. There is, however, a lack of depth in fundamental understanding of how the CO2 binds to these materials and how the subsequent reaction mechanisms are affected, which are critical features for the development of next-generation materials with improved performance. To this end, we will describe in this work the CO2 binding characteristics of established and/or new DFM and their sorbent-only counterparts. The surface CO2 binding mechanism and capture capacity will be probed using techniques such as in-situ DRIFTS, operando TGA, and CO2 chemisorption.
The U.S. nuclear power industry primarily relies on mature, well-proven analog technology as the basis for most instrumentation and control (I and C) equipment employed in its nuclear power plants (NPPs). However, the market for this legacy instrumentation is very small and availability of such equipment for spare parts or replacement is increasingly limited. Frequently, modern I and C equipment contains embedded digital devices (EDDs), such as microprocessors and programmable logic devices. While the enhanced functional capabilities provided by the digital components offer improved performance, reliability, and maintainability, the corresponding complexity of sophisticated digital implementations also exacerbate concerns about an increased potential for undetected systematic faults, which can lead to common-cause failure (CCF). As part of a research project sponsored by the U.S. Department of Energy, an investigation has been conducted of commercially available instrumentation marketed for nuclear or industrial application. The objective was to identify the usage of EDDs and to determine the types of functional roles allocated to the devices. Examples of the equipment surveyed include smart sensors, sequencers for emergency diesel generators, pumps, valve actuators, motor control centers, breakers, priority logic modules, and uninterruptible power sources. Where sufficient detail was found, an evaluation was performed to ascertain the nature and role of the digital technology implemented in the instrumentation. The information gained through this investigation serves as technical input for defining a classification approach for EDDs based on the functional impact of the device. For example, the role of an EDD can involve any of the following functions: performance monitoring and diagnostics, information extraction and communication, instrument condition monitoring and protection, or execution of core instrument functions. Classifying digital devices according to their role in the operation of the instrumentation in which they are embedded can enable determination of the potential functional impact of failure of that device and contribute to determination of the potential safety significance of a CCF event disabling all instances of that device. This paper will report on the findings of the investigation of instrumentation with EDDs and indicate considerations for the emerging classification approach. The higher-level goal of this effort is to determine whether a graded approach to qualification testing may be possible based on classification. (authors)
The nuclear forensics community is currently engaged in the analysis of illicit nuclear or radioactive material for the purposes of non-proliferations and attribution. One technique commonly employed for gathering nuclear forensics information is isotope analysis. At present, the state-of-the-art methodology for obtaining isotopic distributions is thermal ionization mass spectrometry (TIMS). Although TIMS is highly accurate at determining isotope distributions, the technique requires an elementally pure sample to perform the measurement. The required radiochemical separations give rise to sample preparation times that can be in excess of one to two weeks. Clearly, the nuclear forensics community is in need of instrumentation and methods that can expedite their decision making process in the event of a radiological release or nuclear detonation. Accordingly, we are developing instrumentation that couples a high resolution IM drift cell to the front end of a MS. The IM cell provides a means of separating ions based upon their collision cross-section and mass-to-charge ratio (m/z). Two analytes with the same m/z, but with different collision cross-sections (shapes) would exit the cell at different times, essentially enabling the cell to function in a similar manner to a gas chromatography (GC) column. Thus, molecular and atomic isobaric interferences can be effectively removed from the ion beam. The mobility selected chemical species could then be introduced to a MS for high-resolution mass analysis to generate isotopic distributions of the target analytes. The outcome would be an IM/MS system capable of accurately measuring isotopic distributions while concurrently eliminating isobaric interferences and laboratory radiochemical sample preparation. The overall objective of this project is developing instrumentation and methods to produce near real-time isotope distributions with a modular mass spectrometric system that performs the required gas-phase chemistry and separations. The system couples a high-resolution ion mobility (IM) drift cell to the front end of a mass spectrometer (MS) allowing for chemical separation prior to isotope distribution analyses. This will yield isotope ratio measurement capabilities with minimal sample preparation.
Bioenergy crops are a renewable source of fuels and are a critical base for building a carbon-neutral economy. Rational engineering of bioenergy crops has the potential to enhance the yields. However, our ability to engineer plants is limited because the functions of most genes remain unknown. Systematic characterization of gene function in plants thus has the potential to greatly accelerate bioenergy research. Here, we focus on the chloroplast, an underexplored energy-producing organelle that is a hallmark of plants. The chloroplast is one of the promising targets of biofuel crop engineering efforts because of its central role in photosynthesis, metabolism, and intracellular signaling. However, the protein composition of the chloroplast and the functions of most of its proteins remain poorly characterized. At the core of this project, we sought to comprehensively determine the localization of chloroplast-associated proteins and generate a spatially defined protein-protein interaction network for chloroplast. For this purpose, we used the leading model alga Chlamydomonas reinhardtii, which greatly increased experimental speed and throughput. We illustrated the value of our findings to land plants by determining the localization of Arabidopsis thaliana land plant homologs of the Chlamydomonas proteins. Altogether, we were successful in determining the localization of 1,034 chloroplast-associated proteins in Chlamydomonas. The localizations provide numerous insights into the spatial organization of chloroplasts and how they function to support photosynthesis. The localization patterns of distinct proteins revealed new chloroplast structures and revealed new spatial organization inside the chloroplast. We also identified new components of known chloroplast structures, such as the chloroplast envelope, nucleoid, plastoglobuli, and pyrenoid. We identified these new components by investigating the interacting partners of known proteins. Many proteins localized in both the chloroplast and other cellular structures, thereby hinting at new functions and communication between cellular structures. We also applied machine learning on the atlas to generate predictions for the location of all of the proteins in Chlamydomonas. This enabled us to assign putative functions to many uncharacterized proteins based on their cellular location. Altogether, this research establishes a rich resource that opens new avenues of investigation and guides future work in deciphering and manipulating chloroplast function. Next, we developed an extensive protein-protein interaction network for the chloroplast by performing affinity purification-mass spectrometry on ~1,150 tagged chloroplast-associated proteins, the first such large-scale study in any photosynthetic organism. This dataset reveals 4,694 high-confidence protein-protein interactions, offering insights into the functions of thousands of conserved poorly-characterized chloroplast proteins. This systematic identification of protein-protein interactions in the chloroplast also provides multiple exciting new research directions and a detailed blueprint of the chloroplast's operation. This research lays the groundwork to decipher the inner workings of the chloroplast, the cell structure at the heart of photosynthesis. The spatial atlas and protein-protein interactions reveal chloroplast organizational features that would not have been accessible with traditional approaches. The localization mapping, insights into the function, and research materials generated further provide a rich resource for the research community to advance the understanding of how the chloroplast is organized to enable engineering of enhanced photosynthetic organisms.
The core function of the distribution grid is to provide electricity to consumers affordably, reliably, and securely. In pursuing these core objectives, distribution utilities are accountable to customers, regulators, and in some cases, shareholders. Other third parties such as aggregators and microgrids can also have a stake in the smooth operation of the grid. Each of these stakeholders has economic, business, and/or governance objectives that inform their expectations of the distribution grid. This multi-objective, multi-stakeholder environment creates tension that must be reconciled to successfully design and operate the distribution grid. Innovative companies are competing to bring high-tech solutions to electric utilities and their customers that address each of these objectives. Many developers of advanced distribution management systems (ADMS) and distributed energy resource management systems (DERMS) have adopted a modular architecture that allows grid operators to select functions and features according to their individual system needs. A modular platform also allows the solution provider to develop and integrate specific new product modules; however, the need to pursue multiple objectives with a fixed set of controllable devices makes integration expensive whether it is done at the product development stage or the deployment stage. This cost creates a significant barrier to adoption and can lengthen the product to market time of new solutions. To fundamentally address the complexity of system integration for distribution grid operations, the U.S. Department of Energy Office of Electricity has funded the GridAPPS-D project at PNNL, which streamlines integration by contributing to standards development, defining system architecture, applying advanced mathematics, and developing open-source software to demonstrate the concept of an open data-integration platform for distribution operations. The open data-integration platform concept enables system operators and solution providers to deploy ambitious, best-of-breed applications (or apps) without continually reengineering for integration. Ambitious apps developed by different solution providers will inevitably attempt to achieve different control objectives with the same set of controllable devices. If the open platform itself can resolve these conflicts in a way that achieves the best available outcomes for all apps, doesn’t restrict the ambitious design of apps, and ensures safe and secure operations, apps will be able to plug-and-play with the platform at the same time as other ambitious apps. In this paper, we describe a framework called App Deconfliction that empowers a platform to assign setpoints to controllable devices based on the values preferred by different apps (and even external stakeholder entities like customers or aggregators). The App Deconfliction framework is compatible with several methods for determining setpoint values. We present two methods based on game theory that provide a subtle built-in incentive structure for developers to adapt their apps to the fact that they will be operating in a moderated multi-app environment and to favor device setpoints that have the most effect on their objectives over those that have the least effect. Our simulation-based demonstrations have shown that game-theory-based deconfliction can lead to a 7% improvement in control space utilization compared to design-based methods.
This paper formalizes the use of integral and differential cumulants for measurements of multiparticle event-by-event transverse momentum fluctuations, rapidity fluctuations, as well as net-charge fluctuations. This enables the introduction of multiparticle balance functions, defined based on differential correlation functions (factorial cumulants), that suppress two- and three-prong resonance decays effects and enable measurements of underlying long-range correlations obeying quantum number conservation constraints. These multiparticle balance functions satisfy simple sum rules determined by quantum number conservation. It is additionally shown that these multiparticle balance functions arise as an intrinsic component of high-order net-charge cumulants. This implies that the magnitude of these cumulants, measured in a specific experimental acceptance, is strictly constrained by charge conservation and primarily determined by the rapidity and momentum width of these balance functions. The paper also presents techniques to reduce the computation time of differential correlation functions up to order n = 10 based on the methods of moments. Published by the American Physical Society 2024
Modern life is enabled by a complex and interdependent web of critical functions, including energy, communications, transportation, food, and water. Automation has significantly reduced or replaced human interactions in the delivery of these functions, resulting in a web of goods and services that are made available 24/7 only through unique and intentional deployments of microprocessors, software, and firmware technologies. The prospect of cyber-enabled sabotage of these processes disrupts traditional risk determination models. Critical Function Assurance (CFA) is a foundational approach to identifying, prioritizing, and mitigating the risk that is inherent in the delivery of critical functions that depend on digital technology. It provides rapid focus to what matters most and illuminates elements and areas of risk that otherwise are often overlooked. This focus enables effective application of available security resources and optimizes security strategy and policy efforts. This paper introduces CFA to decision makers and risk executives (including CEOs, COOs, CFOs, and CISOs) whose organizations support and deliver the critical functions that underpin national defense, societal health and safety, and a vibrant economy.
The Fortran programming language standard defines a broad feature set supporting the interoperability of Fortran programs with program written according to the C programming language standard. Among Fortran's C-interoperability features is a a C header file "ISO_Fortran_binding.h" This header file defines the interface to various C data structures and functions that C programs may use to access Fortran data entities. The ISO_Fortran_binding_m software defines a native Fortran module that presents an interface to these same data structures and functions. ISO_Fortran_bind_m thus enables Fortran programs to access and manipulate Fortran entities in ways that precisely mirror what C programs can do using ISO_Fortran_binding.h. ISO_Fortran_binding_m facilitates writing portable standard-conforming Fortran programs that emulate non-interoperable features, e.g., dynamic polymorphism, in a standard-conforming interoperable way similar but broader than what is demonstrated in Berkeley Lab's Caffeine software [1]. ISO_Fortran_binding_m also enables a Fortran programmer to extend Fortran's capabilities to emulate certain C functionality such as memory address arithmetic or computing C's "sizeof" function. [1] https://github.com/BerkeleyLab/caffeine/blob/213e3df1c319f0663306354414f352acda42a24f/src/caffeine/collective_subroutines/co_reduce_s.f90#L88 [2] https://github.com/BerkeleyLab/ISO_Fortran_binding_m/blob/0c585362bb4f2c72cf9049c800a7115b529ec533/src/iso_fortran_binding_m.F90#L196
We augment the two widest/deepest 1.4 GHz radio surveys, the NRAO VLA Sky Survey (NVSS) and the Faint Images of the Radio Sky at Twenty centimeters (FIRST), with the mean epoch in which each source was observed. We use these catalogs to search for unresolved sources that vary between the FIRST and NVSS epochs. We find 43 variable sources (0.1% of the sources) that vary by more than 4{sigma}, and we construct the mean structure function of these objects. This enables us to explore radio variability on timescales between several months and about five years. We find that, on these timescales, the mean structure function of the variable sources is consistent with a flat structure function. A plausible explanation to these observations is that a large fraction of the variability at 1.4 GHz is induced by scintillations in the interstellar medium, rather than by intrinsic variability. Finally, for a subsample of the variables for which the redshift is available, we do not find strong evidence for a correlation between the variability amplitude and the source redshift.
Postsynthetic functionalization of metal organic frameworks (MOFs) enables the controlled, high-density incorporation of new atoms on a crystallographically precise framework. Leveraging the broad palette of known atomic layer deposition (ALD) chemistries, ALD in MOFs (AIM) is one such targeted approach to construct diverse, highly functional, few-atom clusters. We here demonstrate the saturating reaction of trimethylindium (InMe3) with the node hydroxyls and ligated water of NU-1000, which takes place without significant loss of MOF crystallinity or internal surface area. We computationally identify the elementary steps by which trimethylated trivalent metal compounds (ALD precursors) react with this Zr-based MOF node to generate a uniform and well characterized new surface layer on the node itself, and we predict a final structure that is fully consistent with experimental X-ray pair distribution function (PDF) analysis. We further demonstrate tunable metal loading through controlled number density of the reactive handles (-OH and -OH2) achieved through node dehydration at elevated temperatures.
High-throughput molecular screening of microbial colonies and DNA libraries are critical procedures that enable applications such as directed evolution, functional genomics, microbial identification, and creation of engineered microbial strains to produce high-value molecules. A promising chemical screening approach is the measurement of products directly from microbial colonies via optically guided matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Measuring the compounds from microbial colonies bypasses liquid culture with a screen that takes approximately 5 s per sample. We describe a protocol combining a dedicated informatics pipeline and sample preparation method that can prepare up to 3,000 colonies in under 3 h. The screening protocol starts from colonies grown on Petri dishes and then transferred onto MALDI plates via imprinting. The target plate with the colonies is imaged by a flatbed scanner and the colonies are located via custom software. The target plate is coated with MALDI matrix, MALDI-MS analyzes the colony locations, and data analysis enables the determination of colonies with the desired biochemical properties. This workflow screens thousands of colonies per day without requiring additional automation. The wide chemical coverage and the high sensitivity of MALDI-MS enable diverse screening projects such as modifying enzymes and functional genomics surveys of gene activation/inhibition libraries.
Functionalized scanning probe tips hold great promise for the controlled delivery of signals from and to selected nanoscale volumes. Here, a new nanotechnological approach for the functionalization of scanning probe tips is presented, targeting the transfer of the optical, electrical, thermal, or chemical properties of precisely characterized epitaxial semiconductor layers to the nanoscale probe tip. Homogeneously doped, strain‐relaxed heteroepitaxial germanium layers, several micrometers thick, are first grown on silicon wafers by low‐energy plasma‐enhanced chemical vapor deposition and then employed to functionalize the probe apex. This choice of materials and growth technique enables the future scalability of the tip functionalization process toward high production volumes. The fabricated probe tips are investigated in a transmission electron microscope, revealing that the crystal structure and the homogeneous doping level of the epitaxial layers are unchanged after the probe‐tip functionalization process. These doped‐germanium tips are also tested as nanoemitters of light at telecom wavelengths (1.55 µm) and applied as scattering probes for near‐field mid‐infrared microscopy. The reported combination of heteroepitaxial growth and a nanofabrication approach can be extended to a variety of epitaxial materials, thus enabling a new generation of scanning probes for the investigation of nanostructured materials and devices.
This paper develops a novel method for simultaneously determining the plasma frequency ω{sub P} and the damping constant γ{sub free} in the bulk damped oscillator Drude model, based on experimentally measured real and imaginary parts of the metal refractive index in the IR wavelength range, lifting the usual approximation that restricts frequency values to the UV-deep UV region. Our method was applied to gold, silver, and copper, improving the relative uncertainties in the final values for ω{sub p} (0.5%–1.6%) and for γ{sub free} (3%–8%), which are smaller than those reported in the literature. These small uncertainties in ω{sub p} and γ{sub free} determination yield a much better fit of the experimental complex dielectric function. For the case of nanoparticles (Nps), a series expansion of the Drude expression (which includes ω{sub p} and γ{sub free} determined using our method) enables size-dependent dielectric function to be written as the sum of three terms: the experimental bulk dielectric function plus two size corrective terms, one for free electron, and the other for bound-electron contributions. Finally, size distribution of nanometric and subnanometric gold Nps in colloidal suspension was determined through fitting its experimental optical extinction spectrum using Mie theory based on the previously determined dielectric function. Results are compared with size histogram obtained from Transmission Electron Microscopy (TEM)
Using year 2014 cost information gathered from twenty different locations within the current commercial nuclear power station fleet, an assessment was performed concerning compliance costs associated with the offsite emergency Planning Standards contained in 10 CFR 50.47(b). The study was conducted to quantitatively determine the potential cost benefits realized if an emergency planning zone (EPZ) were reduced in size according to the lowered risks expected to accompany small modular reactors (SMR). Licensees are required to provide a technical basis when proposing to reduce the surrounding EPZ size to less than the 10 mile plume exposure and 50 mile ingestion pathway distances currently being used. To assist licensees in assessing the savings that might be associated with such an action, this study established offsite emergency planning costs in connection with four discrete EPZ boundary distances, i.e., site boundary, 2 miles, 5 miles and 10 miles. The boundary selected by the licensee would be based on where EPA Protective Action Guidelines are no longer likely to be exceeded. Additional consideration was directed towards costs associated with reducing the 50 mile ingestion pathway EPZ. The assessment methodology consisted of gathering actual capital costs and annual operating and maintenance costs for offsite emergency planning programs at the surveyed sites, partitioning them according to key predictive factors, and allocating those portions to individual emergency Planning Standards as a function of EPZ size. Two techniques, an offsite population-based approach and an area-based approach, were then employed to calculate the scaling factors which enabled cost projections as a function of EPZ size. Site-specific factors that influenced source data costs, such as the effects of supplemental funding to external state and local agencies for offsite response organization activities, were incorporated into the analysis to the extent those factors could be representatively apportioned.
DNA-functionalized hydrogels are capable of sensing oligonucleotides, proteins, and small molecules, and specific DNA sequences sensed in the hydrogels’ environment can induce changes in these hydrogels’ shape and fluorescence. Fabricating DNA-functionalized hydrogel architectures with multiple domains could make it possible to sense multiple molecules and undergo more complicated macroscopic changes, such as changing fluorescence or changing the shapes of regions of the hydrogel architecture. However, automatically fabricating multi-domain DNA-functionalized hydrogel architectures, capable of enabling the construction of hydrogel architectures with tens to hundreds of different domains, presents a significant challenge. We describe a platform for fabricating multi-domain DNA-functionalized hydrogels automatically at the micron scale, where reaction and diffusion processes can be coupled to program material behavior. Using this platform, the hydrogels’ material properties, such as shape and fluorescence, can be programmed, and the fabricated hydrogels can sense their environment. DNA-functionalized hydrogel architectures with domain sizes as small as 10 microns and with up to 4 different types of domains can be automatically fabricated using ink volumes as low as 50 μL. We also demonstrate that hydrogels fabricated using this platform exhibit responses similar to those of DNA-functionalized hydrogels fabricated using other methods by demonstrating that DNA sequences can hybridize within them and that they can undergo DNA sequence-induced shape change.