Results for “balanced detection” — Page 4

DOE OSTI2025

A Rhenium Bis -tetramethylphenanthroline Catalyst for CO 2 Reduction to Formate

Catalytic CO 2 reduction reactions featuring high selectivity toward formate are relatively rare. In some homogeneous molecular CO 2 -reducing electrocatalysis, using triethylamine (TEA) and isopropanol (IPA) as additives improves catalytic performance in producing formate. In this paper, we investigate whether the rhenium(I) bis-diimine dicarbonyl complexes, cis-[Re(N^N) 2 (CO) 2 ] + , where N^N is 2,2’-bipyridine ([1] + ) or 3,4,7,8-tetramethyl-1,10-phenanthroline ([2] + ), are capable of electrocatalytically reducing CO 2 to formate in acetonitrile containing TEA and IPA. Catalyst [1] + was ineffective at CO 2 reduction, yielding formate quantities comparable to those produced in experiments without the catalyst. Catalyst [2] + , however, is a promising electrocatalyst for the CO 2 reduction reaction in the presence of TEA and IPA, with formate being produced in millimolar concentrations (10.5 mM), as detected by 1 H NMR spectroscopy after 6 h electrolysis (formate Faradaic efficiency = 11%, with the major balance going to H 2 ). Upon more detailed examination, [2] + exhibited a turnover frequency (TOF) of 12 s –1 for formate, comparable to other leading molecular catalysts that competently execute this reduction. Combinations of spectroscopy, electrochemistry, and theory were used to better understand the mechanism of CO 2 reduction by [2] + . Fourier transform infrared spectroelectrochemical (FTIR-SEC) data provided no evidence for CO ligand dissociation or substitution upon one- and two-electron reduction of [2] + , suggesting that a mechanism distinct from one that is metal-hydride-based is operative in catalysis. Computational studies guide mechanistic investigations toward the proposed formation of a hydrophenanthroline-based intermediate responsible for hydride transfer to CO 2 and electrocatalytic formate production from [2] + .

Beverages↗

DOE OSTI2025

Analysing a multi‐stage cyber threat and its impact on the power system

Abstract Electric power systems are composed of physical and cyber sub‐systems. The sub‐systems depend on each other. If the cyber sub‐system is compromised by a cyber threat, what is the impact on the physical system? This paper presents a case study that shows the steps of a multi‐stage cyber threat involving a database injection attack, and what happens to the power system if this threat is not detected in its early stages. The threat first affects one utility but it can spread to the balancing authority, which is responsible for keeping the voltage and frequency stable in the power grid. During the cyber threat, the authors also show defence tools, such as a cyber‐physical data fusion tool that displays and analyses power and cyber telemetry.

Al Homoud, Leen [Texas A&amp,M University College ↗

DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Rubisco regulation in response to altered carbon status in the cyanobacterium Synechococcus elongatus PCC 7942

Abstract Photosynthetic organisms possess a variety of mechanisms to achieve balance between absorbed light (source) and the capacity to metabolically utilize or dissipate this energy (sink). While regulatory processes that detect changes in metabolic status/balance are relatively well studied in plants, analogous pathways remain poorly characterized in photosynthetic microbes. Here, we explored systemic changes that result from alterations in carbon availability in the model cyanobacterium Synechococcus elongatus PCC 7942 by taking advantage of an engineered strain where influx/efflux of a central carbon metabolite, sucrose, can be regulated experimentally. We observed that induction of a high-flux sucrose export pathway leads to depletion of internal carbon storage pools (glycogen) and concurrent increases in estimates of photosynthetic activity. Further, a proteome-wide analysis and fluorescence reporter-based analysis revealed that upregulated factors following the activation of the metabolic sink are concentrated on ribulose-1,5-bisphosphate carboxylase-oxygenase (Rubisco) and auxiliary modules involved in Rubisco maturation. Carboxysome number and Rubisco activity also increased following engagement of sucrose secretion. Conversely, reversing the flux of sucrose by feeding exogenous sucrose through the heterologous transporter resulted in increased glycogen pools, decreased Rubisco abundance, and carboxysome reorganization. Our data suggest that Rubisco activity and organization are key variables connected to regulatory pathways involved in metabolic balancing in cyanobacteria.

Plant Sciences↗

DOE OSTI2023

Development of a Low-Cost, Sensitivity-Optimized Fluorescence Sensor for Visible Spectrum Analysis

This paper presents an economically viable, sensitivity-optimized fluorescence sensor for detecting low concentrations of fluorophores in the visible spectrum range. The sensor employs a deep ultraviolet (DUV) 275-nm LED and a dichroic mirror to establish a co-axial optical path for excitation and emission. Instead of utilizing a general-purposed spectrometer optimized for high spectral resolution, the sensor strikes a balance between resolution and sensitivity by using an appropriate aperture size and optimized optical design tailored for the spectral characteristics of fluorophores. Here, the manuscript demonstrates measurements of ppb-level rare earth element (REE) samples containing terbium (Tb), europium (Eu), dysprosium (Dy), and samarium (Sm) in aqueous solutions. Through balanced design that optimizes collection of fluorescence emission without losing spectral information, this work shows the feasibility of developing a low-cost, compact, and highly sensitive fluorescence sensors with improved detection sensitivity when compared to bench-top commercial spectrofluorometers at far higher costs.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach

The growing global concerns about the effects to public health from human exposure to per- and polyfluoroalkyl substances (PFAS) motivates the development of strategies for reliable monitoring of PFAS in environmental streams, as well as for their rapid, effective removal if detected. For the continuous PFAS monitoring, an inexpensive, field-deployable, in situ sensor is urgently needed; yet the prevalent in situ techniques often struggle to strike a balance between the practical sensitivity and selectivity demands of the real world. Similarly, for effective PFAS removal, strategies for their fast, selective, and quantitative capture are desired, yet the present commercially available sorbents are unable to meet the requirements of rapid, quantitative capture of all PFAS components, and are notably inefficient in removing the more toxic smaller chains. To address these twin challenges, Pacific Northwest National Laboratory is developing strategies for improved detection and remediation of PFAS. For the rapid, selective, quantitative removal of PFAS from environmental streams, the strategy relies on designing capture probes with exclusively tailored electronic and spatial affinities for the PFAS that are able to selectively capture them from environmental streams. For the in situ detection and quantification of PFAS in complex, multicomponent matrices such as groundwater, the approach relies on the targeted capture of specific PFAS by these PFAS-specific capture probes immobilized on a platform. The platform acts as an electrode to directly measure PFAS concentration through a proportional change in electrical response upon their capture. A combination of optimization of platform design and incorporation of additional, sensitive detection modalities have allowed us to achieve detection limits as low as 0.5 ng/L for detection of PFAS compounds (compared to the 70 ng/L Health Advisory Limit of the U.S. Environmental Protection Agency).

Per- and poly-fluorinated alkyl substances (PFAS),↗

DOE OSTI2020

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach - 20080

The growing global concerns about the effects to public health from human exposure to per- and polyfluoroalkyl substances (PFAS) motivates the development of strategies for reliable monitoring of PFAS in environmental streams, as well as for their rapid, effective removal if detected. For the continuous PFAS monitoring, an inexpensive, field-deployable, in situ sensor is urgently needed; yet the prevalent in situ techniques often struggle to strike a balance between the practical sensitivity and selectivity demands of the real world. Similarly, for effective PFAS removal, strategies for their fast, selective, and quantitative capture are desired, yet the present commercially available sorbents are unable to meet the requirements of rapid, quantitative capture of all PFAS components, and are notably inefficient in removing the more toxic smaller chains. To address these twin challenges, Pacific Northwest National Laboratory is developing strategies for improved detection and remediation of PFAS. For the rapid, selective, quantitative removal of PFAS from environmental streams, the strategy relies on designing capture probes with exclusively tailored electronic and spatial affinities for the PFAS that are able to selectively capture them from environmental streams. For the in situ detection and quantification of PFAS in complex, multicomponent matrices such as groundwater, the approach relies on the targeted capture of specific PFAS by these PFAS-specific capture probes immobilized on a platform. The platform acts as an electrode to directly measure PFAS concentration through a proportional change in electrical response upon their capture. A combination of optimization of platform design and incorporation of additional, sensitive detection modalities have allowed us to achieve detection limits as low as 0.5 ng/L for detection of PFAS compounds (compared to the 70 ng/L Health Advisory Limit of the U.S. Environmental Protection Agency). (authors)

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Identifying Band Inversions in Topological Materials Using Diffusion Monte Carlo

Topological insulators are characterized by insulating bulk states and robust metallic surface states. Band inversion is a hallmark of topological insulators. At time-reversal invariant points in the Brillouin zone, spin–orbit coupling (SOC) induces a swapping of orbital character at the bulk band edges. Reliably detecting band inversion in solid-state systems with many-body methods would aid in identifying possible candidates for spintronics and quantum computing applications and improve our understanding of the physics behind topologically nontrivial systems. Density functional theory (DFT) methods are a well-established means of investigating these interesting materials due to their favorable balance of computational cost and accuracy but often struggle to accurately model the electron–electron correlations present in the many materials containing heavier elements. In this work, we develop a novel method to detect band inversion within continuum quantum Monte Carlo (QMC) methods that can accurately treat the electron correlation and spin–orbit coupling that are crucial to the physics of topological insulators. Our approach applies a momentum-space-resolved atomic population analysis throughout the first Brillouin zone utilizing the Löwdin method and the one-body reduced density matrix produced with diffusion Monte Carlo (DMC). We integrate this method into QMCPACK, an open source ab initio QMC package, so that these ground-state methods can be used to complement experimental studies and validate prior DFT work on predicting the band structures of correlated topological insulators. Here, we demonstrate this new technique on the topological insulator bismuth telluride, which displays band inversion between its Bi-p and Te-p states at the Γ-point. We show an increase in charge on the bismuth-p orbital and a decrease in charge on the tellurium-p orbital when comparing band structures with and without SOC. Additionally, we use our method to compare the degree of band inversion present in monolayer Bi 2 Te 3 , which has no interlayer van der Waals interactions, to that seen in the bilayer and bulk. The method presented here will enable future many-body studies of band inversion that can shed light on the delicate interplay between correlation and topology in correlated topological materials.

Band structure↗

DOE OSTI2024

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality

ABSTRACT Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint‐based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable “golden batches” in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Biotechnology & Applied Microbiology↗

DOE OSTI2026

Optimization of an Energy Tuning Assembly for High Explosives Detection

The Portable Isotopic Neutron Spectroscopy (PINS) system, employs neutron-induced gamma-ray spectroscopy and provides a nondestructive method for high explosives detection. In standard operation it uses Californium-252 as a neutron source. Operating PINS with a deuterium-tritium (DT) neutron generator has some advantages over Cf-252, including lifetime and ability to produce high-energy inelastic scattering gamma rays. However, current systems using DT neutron generators suffer from a high environmental background and reduced ability to induce neutron capture, reducing spectral quality and limiting nitrogen sensitivity. Here, this study presents the development of an energy-tuning assembly (ETA) designed to optimize the DT neutron energy spectrum to increase nitrogen reaction rates in a target, thereby improving high explosive detection capabilities. A metaheuristic optimization framework, MultiGNOWEE, coupled with MCNP, was employed to generate two ETA configurations: a single-objective ETA, which maximizes nitrogen capture reactions, and a multi-objective ETA, which balances neutron capture and inelastic scattering. Simulations demonstrated the optimized configurations achieved up to a 10-fold improvement in nitrogen capture rates compared to the bare configuration. Experimental validation was conducted using a DT neutron generator and a high-purity germanium (HPGe) detector. Two prototype ETAs were constructed and assessed on a melamine simulant. Measurements demonstrated improved nitrogen detection for both prototype ETA configurations when compared to the standard system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Applications of targeted proteomics in metabolic engineering: advances and opportunities

Optimization of metabolically engineered organisms requires good understanding of producing balanced level of pathway proteins. Targeted proteomics via selected-reaction monitoring (SRM) has been increasingly used in metabolic engineering studies to detect and quantify sets of proteins with high selectivity, multiplexity, and reproducibility. In combination with metabolomics and other omics tools, targeted proteomics has helped optimize the production of many bio-based chemicals in various metabolic engineering cell factories. In this review, we present recent applications of targeted proteomics in metabolic engineering studies and highlight several successful cases of targeted proteomics in boosting production of commodity and high value chemicals. Additionally, we also discuss challenges and limitations of current targeted proteomics and map opportunities for future research.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

High-performance photonic transformers for DC voltage conversion

Abstract Direct current (DC) converters play an essential role in electronic circuits. Conventional high-efficiency DC voltage converters, especially step-up type, rely on switching operation, where energy is periodically stored within and released from inductors and/or capacitors connected in a variety of circuit topologies. Since these energy storage components, especially inductors, are fundamentally difficult to scale down, miniaturization of switching converters proves challenging. Furthermore, the resulting switching currents produce significant electromagnetic noise. To overcome the limitations of switching converters, photonic transformers, where voltage conversion is achieved through light emission and detection processes, have been demonstrated. However, the demonstrated efficiency is significantly below that of the switching converter. Here we perform a detailed balance analysis and show that with a monolithically integrated design that enables efficient photon transport, the photonic transformer can operate with a near-unity conversion efficiency and high voltage conversion ratio. We validate the theory with a transformer constructed with off-the-shelf discrete components. Our experiment showcases near noiseless operation and a voltage conversion ratio that is significantly higher than obtained in previous photonic transformers. Our findings point to the possibility of a high-performance optical solution to miniaturizing DC power converters and improving the electromagnetic compatibility and quality of electrical power.

42 ENGINEERING↗

DOE OSTI2020

Flux Balancing Control of Ungapped Nanocrystalline Core-Based Transformer in Dual Active Bridge Converters

Transformer flux dc bias is a critical issue, impacting the reliable operation of dual active bridge (DAB) converters especially when ungapped high permeability nanocrystalline core is used. Steady state current dc bias can easily saturate ungapped nanocrystalline transformers, and it is even more dangerous in transient conditions. A dc bias model is proposed to analyze steady state dc bias in different load conditions. The magnetizing current detection is necessary for closed-loop control of dc bias; conductors' position is shown to impact sensor noise, and it is analyzed in detail. To deal with both steady state and transient dc bias, a unified flux balancing control (UFBC) is proposed introducing a predictive bias suppression (PBS) method with closed-loop flux balancing control (CFBC). With the proposed PBS, both primary/secondary current balance and flux balance can be achieved within one switching cycle using UFBC. Power characteristics and interaction between power control and flux balancing control of DAB converters are analyzed, and the CFBC needs to work in a low bandwidth due to sensor bandwidth limitation and interaction between power control and flux balancing control. Lastly, the UFBC is verified on a 300-kW cascaded DAB converter prototype.

42 ENGINEERING↗

DOE OSTI2020

Gain balanced nonlinear optical interferometer

A nonlinear fiber interferometer is disclosed suitable for fiber sensor and other applications. A first nonlinear fiber section amplifies probe and conjugate sidebands of a pump through four-wave mixing. A second section introduces a phase shift to be measured, for example from a sensor. A third nonlinear fiber section amplifies with phase-sensitive gain to increase signal-to-noise ratio. Based on phase-sensitive output power of probe and/or conjugate components, the phase shift can be measured. Superior performance can be obtained by balancing gain between the (first and third) nonlinear sections. Non-fiber, for example photonic integrated circuit, embodiments are disclosed. Differential sensing, alternative detection schemes, sensing applications, associated methods, and other variations are disclosed.

42 ENGINEERING↗

DOE OSTI2022

Gain balanced nonlinear optical interferometer

A nonlinear fiber interferometer is disclosed suitable for fiber sensor and other applications. A first nonlinear fiber section amplifies probe and conjugate sidebands of a pump through four-wave mixing. A second section introduces a phase shift to be measured, for example from a sensor. A third nonlinear fiber section amplifies with phase-sensitive gain to increase signal-to-noise ratio. Based on phase-sensitive output power of probe and/or conjugate components, the phase shift can be measured. Superior performance can be obtained by balancing gain between the (first and third) nonlinear sections. Non-fiber, for example photonic integrated circuit, embodiments are disclosed. Differential sensing, alternative detection schemes, sensing applications, associated methods, and other variations are disclosed.

Lukens, Joseph M.↗

DOE OSTI2023

Gain balanced nonlinear optical interferometer

A nonlinear fiber interferometer is disclosed suitable for fiber sensor and other applications. A first nonlinear fiber section amplifies probe and conjugate sidebands of a pump through four-wave mixing. A second section introduces a phase shift to be measured, for example from a sensor. A third nonlinear fiber section amplifies with phase-sensitive gain to increase signal-to-noise ratio. Based on phase-sensitive output power of probe and/or conjugate components, the phase shift can be measured. Superior performance can be obtained by balancing gain between the (first and third) nonlinear sections. Non-fiber, for example photonic integrated circuit, embodiments are disclosed. Differential sensing, alternative detection schemes, sensing applications, associated methods, and other variations are disclosed.

Lukens, Joseph M.↗

DOE OSTI2024

Efficiency of ML Anomaly Detection Triggers for Emerging Jets

Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Efficiency of ML Anomaly Detection Triggers for Emerging Jets

Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗