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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Are grid cells used for navigation? On local metrics, subjective spaces, and black holes

The symmetric, lattice-like spatial pattern of grid-cell activity is thought to provide a neuronal global metric for space. This view is compatible with grid cells recorded in empty boxes but inconsistent with data from more naturalistic settings. Here, we review evidence arguing against the global-metric notion, including the distortion and disintegration of the grid pattern in complex and three-dimensional environments. We argue that deviations from lattice symmetry are key for understanding grid-cell function. We propose three possible functions for grid cells, which treat real-world grid distortions as a feature rather than a bug. First, grid cells may constitute a local metric for proximal space rather than a global metric for all space. Second, grid cells could form a metric for subjective action-relevant space rather than physical space. Third, distortions may represent salient locations. Finally, we discuss mechanisms that can underlie these functions. These ideas may transform our thinking about grid cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Dynamics of Rubisco regulation by sugar phosphate derivatives and their phosphatases

Regulating the central CO 2 -fixing enzyme Rubisco is as complex as its ancient reaction mechanism and involves interaction with a series of cofactors and auxiliary proteins that activate catalytic sites and maintain activity. A key component among the regulatory mechanisms is the binding of sugar phosphate derivatives that inhibit activity. Removal of inhibitors via the action of Rubisco activase is required to restore catalytic competency. In addition, specific phosphatases dephosphorylate newly released inhibitors, rendering them incapable of binding to Rubisco catalytic sites. The best studied inhibitor is 2-carboxy-d-arabinitol 1-phosphate (CA1P), a naturally occurring nocturnal inhibitor that accumulates in most species during darkness and low light, progressively binding to Rubisco. As light increases, Rubisco activase removes CA1P from Rubisco, and the specific phosphatase CA1Pase dephosphorylates CA1P to CA, which cannot bind Rubisco. Misfire products of Rubisco’s complex reaction chemistry can also act as inhibitors. One example is xylulose-1,5-bisphosphate (XuBP), which is dephosphorylated by XuBPase. Here we revisit key findings related to sugar phosphate derivatives and their specific phosphatases, highlighting outstanding questions and how further consideration of these inhibitors and their role is important for better understanding the regulation of carbon assimilation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A new Wilson line-based action for gluodynamics

We perform a canonical transformation of fields that brings the Yang-Mills action in the light-cone gauge to a new classical action, which does not involve any triple-gluon vertices. The lowest order vertex is the four-point MHV vertex. Higher point vertices include the MHV and $\overline{\mathrm{MHV}}$ vertices, that reduce to the corresponding amplitudes in the on-shell limit. In general, any n -leg vertex has 2 ≤ m ≤ n - 2 negative helicity legs. The canonical transformation of fields can be compactly expressed in terms of path-ordered exponentials of fields and their functional derivative. We apply the new action to compute several tree-level amplitudes, up to 8-point NNMHV amplitude, and find agreement with the standard methods. The absence of triple-gluon vertices results in fewer diagrams required to compute amplitudes, when compared to the CSW method and, obviously, considerably fewer than in the standard Yang-Mills action.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Causality and dimensionality in geometric scattering

We report the scattering matrix which describes low-energy, non-relativistic scattering of spin-1/2 fermions interacting via finite-range potentials can be obtained from a geometric action principle in which space and time do not appear explicitly. In the case of zero-range forces, causality leads to constraints on scattering trajectories in the geometric picture. The effect of spatial dimensionality is also investigated by considering scattering in two and three dimensions. In the geometric formulation it is found that dimensionality is encoded in the phase of the harmonic potential that appears in the geometric action.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Dual-Color Optical Recording of Bioelectric Potentials by Polymer Electrochromism

Optical recording based on voltage-sensitive fluorescent reporters allows for spatial flexibility of measuring from desired cells, but photobleaching and phototoxicity of the fluorescent labels often limit their sensitivity and recording duration. Voltage-dependent optical absorption, rather than fluorescence, of electrochromic materials, would overcome these limitations to achieve long-term optical recording of bioelectrical signals. Electrochromic materials such as PEDOT:PSS possess the property that an applied voltage can either increase or decrease the light absorption depending on the wavelength. In this work, we harness this anticorrelated light absorption at two different wavelengths to significantly improve the signal detection. With dual-color detection, electrical activity from cells produces signals of opposite polarity, while artifacts, mechanical motions, and technical noises are uncorrelated or positively correlated. Using this technique, we are able to optically record cardiac action potentials with a high signal-to-noise ratio, 10 kHz sampling rate, >15 min recording duration, and no time-dependent degradation of the signal. Furthermore, we can reliably perform multiple recording sessions from the same culture for over 25 days.

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

Draft Environmental Impact Statement for Proposed Energy Conservation Standards for Manufactured Housing

This draft environmental impact statement (EIS) analyzes the impacts related to DOE’s proposed energy conservation standards for manufactured homes. DOE’s proposed energy conservation standards are based on the 2021 International Energy Conservation Code (IECC) and relate to the building thermal envelope; air sealing; installation of insulation; duct sealing; heating, ventilation and air conditioning (HVAC); service hot water systems; mechanical ventilation fan efficacy; and heating and cooling equipment sizing. The draft EIS evaluates DOE’s proposed action (energy conservation standards tiered based the manufacturers retail list price), alternatives to the proposed action (including energy conservation standards tiered based on size of the manufactured home and untiered standards), and a no action alternative. The draft EIS presents analysis of impacts related to (1) energy resources; (2) air resources (including greenhouse gases, climate change, ambient air quality and indoor air quality); (3) health and safety; (4) socioeconomics; and (5) environmental justice.

MacDonell, Margaret↗
DOE OSTI2021

Pressure Response Optimization of an Eddy Current-Driven Flyer Plate Valve for the ITER Shattered Pellet Injection System

One technique for mitigating disruptions in a tokamak is shattered pellet injection (SPI). SPI is a process in which a large solid pellet consisting of deuterium, neon, or argon is desublimated in a pipe gun barsrel and launched downstream. Pellets are shattered just before entering the plasma by an impact with an angled tube. Injection of these materials into the plasma radiates stored thermal energy, limits current decay rates, suppresses the generation of runaway electrons, and dissipates runaway electrons if necessary. A critical element of the SPI system is a fast-acting valve that releases high-pressure gas to dislodge and accelerate pellets directly, or indirectly via a mechanical punch. A prototype valve sized for the ITER SPI system has been designed and fabricated. A pulsed high-voltage power supply energizes the valve’s internal magnetic coil, which induces eddy currents in the adjacent flyer plate resulting in a repulsive force between the flyer plate and the coil. The flyer plate action lifts a valve seat, allowing high-pressure gas to flow from the valve plenum to the downstream (breech) location of the pellet or mechanical punch. All of the valve’s internal components are designed to operate in ITER-level static background magnetic fields. Here, a study was conducted to optimize the downstream pressure response for a range of valve sizes and operating pressures. In particular, the study analyzes the breech pressure response associated with varying plenum pressures as well as varying breech volumes. A computational fluid dynamics simulation was built in STAR-CCM+ and validated against data from laboratory experiments. The resulting simulation outputs, in the form of downstream responses for a variety of initial plenum pressures and breech volumes, will be used as a complement to experimental data to ensure the pressure pulse is suitable for pellet survivability. These data, combined with studies on pellet shear strength and shock response, will be applied to optimization of overall operating parameters of the ITER SPI system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Using the Principals of Electrochemistry to Understand and Overcome the Complicated Degradation Mechanisms of Silicon Anodes in Lithium-Ion Batteries [Slides]

Battery technology is the most significant problem facing widespread market adoption of battery electric vehicles (BEVs). The low energy density of state-of-the-art lithium-ion electrode materials leaves BEV owners and prospective buyers with lower ranges than a comparable internal combustion engine (ICE) vehicle, and vulnerable to a nascent fast charging network. Silicon has the potential to increase the anode energy density by nearly ten times compared to the incumbent material (graphite) and make BEVs a more competitive transportation option. However, lithiated silicon is extremely reactive towards components of the electrolyte and forms a heterogeneous, complicated, and dynamic solid at its surface known as the solid electrolyte interphase (SEI). Ideally, the SEI would passivate the silicon surface, but continuous chemical degradation persists even when the battery is not operating. This reactivity reduces silicon anode lifetimes well below the necessary standards for BEVs. The NREL-led Silicon Consortium Project is dedicated to understanding and solving these mechanisms of degradation. Here, I will discuss an electrochemical method that provides deep insights into the silicon interface during battery operation. I will link these observations to fundamental electrochemical principals and how they translate into actionable strategies that extend the lifetime of silicon anodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Human–mouse comparison of the multistage nature of radiation carcinogenesis in a mathematical model

Abstract Mouse models are vital for assessing risk from environmental carcinogens, including ionizing radiation, yet the interspecies difference in the dose response precludes direct application of experimental evidence to humans. Herein, we take a mathematical approach to delineate the mechanism underlying the human–mouse difference in radiation‐related cancer risk. We used a multistage carcinogenesis model assuming a mutational action of radiation to analyze previous data on cancer mortality in the Japanese atomic bomb survivors and in lifespan mouse experiments. Theoretically, the model predicted that exposure will chronologically shift the age‐related increase in cancer risk forward by a period corresponding to the time in which the spontaneous mutational process generates the same mutational burden as that the exposure generates. This model appropriately fitted both human and mouse data and suggested a linear dose response for the time shift. The effect per dose decreased with increasing age at exposure similarly between humans and mice on a per‐lifespan basis (0.72‐ and 0.71‐fold, respectively, for every tenth lifetime). The time shift per dose was larger by two orders of magnitude in humans (7.8 and 0.046 years per Gy for humans and mice, respectively, when exposed at ~35% of their lifetime). The difference was mostly explained by the two orders of magnitude difference in spontaneous somatic mutation rates between the species plus the species‐independent radiation‐induced mutation rate. Thus, the findings delineate the mechanism underlying the interspecies difference in radiation‐associated cancer mortality and may lead to the use of experimental evidence for risk prediction in humans.

Oncology↗
DOE OSTI2025

Population Balance Models for Catalytic Depolymerization: From Elementary Steps to Multiphase Reactors

Here, the ongoing accumulation of plastic waste in landfills and in the environment is driving research on chemical processes and catalysts to recycle polymers. Traditional modeling strategies are not applicable to these processes because they involve too many reactants and intermediates, one for each molecular weight and each functionalization. To model the kinetics, we have developed population balance models (PBMs) that account for macromolecular reactants in the bulk and macromolecular catalytic intermediates. These PBMs couple to each other through polymer adsorption and desorption models and to traditional rate equations for small molecule products and co-reactants (like hydrogen or ethylene). The models, in combination with experimental data, are being used in many ways: (i) to test mechanistic hypotheses, (ii) to extract rate parameters, (iii) to quantitatively compare catalyst activities, (iv) to account for mass transfer and vapor–liquid partitioning in two-phase reactors, and (v) to design novel support architectures and catalysts that mimic the processive action of natural depolymerization enzymes. Some key theoretical advances allow PBMs to be constructed from elementary rates and mechanisms, as opposed to traditional formulations with pseudoelementary rate parameters invoked as fitting parameters. We discuss ways to build these models “bottom-up” from first-principles calculations and ways to extract model parameters from “top down” analyses of rate data. The combination provides a quantitative bridge between first-principles calculations and the kinetics of complex macromolecular transformations for polymer upcycling and beyond.

Manis, Lela K. [University of Illinois at Urbana-C↗
DOE OSTI2021

Molecular mechanisms of metabotropic GABA B receptor function

Metabotropic γ-aminobutyric acid G protein–coupled receptors (GABA B ) represent one of the two main types of inhibitory neurotransmitter receptors in the brain. These receptors act both pre- and postsynaptically by modulating the transmission of neuronal signals and are involved in a range of neurological diseases, from alcohol addiction to epilepsy. A series of recent cryo-EM studies revealed critical details of the activation mechanism of GABA B . Structures are now available for the receptor bound to ligands with different modes of action, including antagonists, agonists, and positive allosteric modulators, and captured in different conformational states from the inactive apo to the fully active state bound to a G protein. These discoveries provide comprehensive insights into the activation of the GABA B receptor, which not only broaden our understanding of its structure, pharmacology, and physiological effects but also will ultimately facilitate the discovery of new therapeutic drugs and neuromodulators.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Simulation-Based Recovery Action Analysis Using the EMRALD Dynamic Risk Assessment Tool

A recovery action is defined as the action that prevents deviant conditions from producing unwanted effects. It generally indicates a kind of countermeasure performed in response to a failure of human action. The recovery actions especially play an important role in complex systems like nuclear power plants (NPPs), which consist of highly sophisticated controllers to ensure that desired performance and safety must be achieved and maintained. This is because a combination of human error and its recovery failure may be able to cause a catastrophic effect on a system. Analyzing recovery actions has been a critical part of HRA, which is a technique to evaluate human errors and provide human error probabilities (HEPs) for application in probabilistic safety assessment (PSA). If recovery actions are not adequately analyzed and applied to PSA models, the PSA results may be under-estimated or be not able to reasonably account for the failure of human actions in the context of PSA. For this reason, some regulatory documents such as ASME/ANS RA-Sb-2013 by the American Society for Mechanical Engineers and the American Nuclear Society and NUREG-1792 by U.S. Nuclear Regulatory Commission have emphasized the importance of recovery analysis within the HRA. A couple of existing HRA methods, such as the Technique for Human Error-Rate Prediction (THERP), the Cause-Based Decision Tree (CBDT), and the Korean Standard HRA (K-HRA), have respectively suggested their own approaches to the HRA recovery analysis. However, there are a couple of limitations to treating recovery actions using only the current HRA methods available. The biggest limitation is that the existing recovery analysis does not explicitly consider a variety of recovery action types and recovery sequences as they occur in actual NPPs. To handle the limitations of existing recovery analysis, this study proposes a simulation-based recovery analysis method using the Event Modeling Risk Assessment Using Linked Diagram (EMRALD) software. The EMRALD software is a dynamic simulation tool for PSA. It supports realistic and dynamic modeling of human actions as they would be performed at NPPs. It is also favorable to simultaneously model the specific moment at which an action is performed, the time it takes to perform the action, and the failure probability of that action. In this paper, a detailed methodology for modeling recovery actions in the simulation platform is proposed with a couple of examples. Then, outputs from the simulation are discussed as reviewing if this novel approach can complement the challenges of existing recovery analyses.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data-driven Optimal Control Strategy for Virtual Synchronous Generator via Deep Reinforcement Learning Approach

This paper aims at developing a data-driven optimal control strategy for virtual synchronous generator (VSG) in the scenario where no expert knowledge or requirement for system model is available. Firstly, the optimal and adaptive control problem for VSG is transformed into a reinforcement learning task. Specifically, the control variables, i.e., virtual inertia and damping factor, are defined as the actions. Meanwhile, the active power output, angular frequency and its derivative are considered as the observations. Moreover, the reward mechanism is designed based on three preset characteristic functions to quantify the control targets: (1) maintaining the deviation of angular frequency within special limits; (2) preserving well-damped oscillations for both the angular frequency and active power output; (3) obtaining slow frequency drop in the transient process. Next, to maximize the cumulative rewards, a decentralized deep policy gradient algorithm, which features model-free and faster convergence, is developed and employed to find the optimal control policy. With this effort, a data-driven adaptive VSG controller can be obtained. By using the proposed controller, the inverter-based distributed generator can adaptively adjust its control variables based on current observations to fulfill the expected targets in model-free fashion. Finally, simulation results validate the feasibility and effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Signaling snapshots of a serotonin receptor activated by the prototypical psychedelic LSD

Serotonin (5-hydroxytryptamine [5-HT]) 5-HT2-family receptors represent essential targets for lysergic acid diethylamide (LSD) and all other psychedelic drugs. Although the primary psychedelic drug effects are mediated by the 5-HT 2A serotonin receptor (HTR2A), the 5-HT 2B serotonin receptor (HTR2B) has been used as a model receptor to study the activation mechanisms of psychedelic drugs due to its high expression and similarity to HTR2A. Here, in this study, we determined the cryo-EM structures of LSD-bound HTR2B in the transducer-free, Gq-protein-coupled, and β-arrestin-1-coupled states. These structures provide distinct signaling snapshots of LSD’s action, ranging from the transducer-free, partially active state to the transducer-coupled, fully active states. Insights from this study will both provide comprehensive molecular insights into the signaling mechanisms of the prototypical psychedelic LSD and accelerate the discovery of novel psychedelic drugs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Experimental and theoretical studies of the doubly substituted methyl-ethyl Criegee intermediate: Infrared action spectroscopy and unimolecular decay to OH radical products

In this work the infrared (IR) action spectrum of the doubly substituted methyl-ethyl Criegee intermediate (MECI) is observed in the CH stretch overtone region with detection of OH products. The MECI exhibits four conformers, all of which undergo unimolecular decay via a 1,4 H-atom transfer mechanism, followed by the rapid release of OH products. Conformers with different orientations of the carbonyl oxide group with respect to the methyl and ethyl substituents (i.e., anti and syn ) decay via distinct transition state barriers (16.1 kcal mol –1 and 15.4 kcal mol –1 , respectively). The observed IR action spectrum is in good agreement with the predicted anharmonic IR absorption spectrum, but exhibits significant congestion, which is attributed to couplings between spectroscopic bright states and nearby dark states. Energy-dependent OH appearance rates are measured upon IR excitation of the strongest features in the IR action spectrum and are found to be on the order of 10 6 –10 7 s –1 . The experimental rates are in good agreement with computed Rice–Ramsperger–Kassel–Marcus rates for the unimolecular decay of MECI at these energies, which incorporate quantum mechanical tunneling and sophisticated hindered rotor treatments, as well as high-level theoretical calculations of the TS barrier heights, rovibrational properties, and torsional barriers associated with the MECI conformers. Master equation modeling is used to predict thermal rates for the unimolecular decay of anti - and syn -MECI of 473 s –1 and 660 s –1 , respectively. Comparison with other previously studied Criegee intermediate systems provides insights into substituent effects on unimolecular decay under both energy-dependent and thermal conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Shot‐Noise‐Driven Macroscopic Vibrations and Displacement Transduction in Quantum Tunnel Junctions

Back‐action is an inevitable result of quantum measurement. Although the microscopic impacts of shot‐noise back‐action have been explored, macroscopic evidence is seldom documented, especially in the field of electrical transport. Tunneling shot‐noise has been shown to excite the fundamental flexural mode of the host crystal using a sub‐GHz lumped‐element radio‐frequency quantum point contact (QPC). Here, in this study, this aspect of shot‐noise back‐action is examined at much higher operational frequencies and wider bandwidths by employing a GaAs QPC integrated into a planar superconducting cavity within the circuit‐QED framework, leading to the observation of the excitation of multiple mechanical modes. The device operates in the shot‐noise‐limited regime. Constructed on a piezoelectric platform, there is positive feedback between the electrical and mechanical degrees of freedom within the QPC. Shot‐noise excites piezoelectric vibrational modes; concurrently the resulting polarization charges enhance tunneling and develop peaks in the shot‐noise spectra at the modal frequencies. The excitation of vibrational modes is a macroscopic demonstration of measurement back‐action, and the amplitudes of the noise‐peaks enable us to calibrate the displacement sensitivity of the QPC‐resonator systems, which is in the range $≈ 35 fm/√ Hz$, making it an excellent sensor for ultra‐sensitive and rapid strain/displacement detection.

Kumbhakar, Prasanta [Indian Institute of Science E↗