Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “characterization challenges”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Detecting and Characterizing Patterns of Failure in Complex Systems: An Ontology Development and Clustering Approach

While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

Systems Engineering↗
DOE OSTI2025

A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties

Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose an approach for building self-supervised autonomy into contact-based robotic systems that teach the robot to follow domain expert measurement principles at high throughputs. We demonstrate the performance of this approach by autonomously driving a 4-DOF robotic probe for 24 hours to characterize semiconductor photoconductivity at 3025 uniquely predicted poses across a gradient of drop-casted perovskite film compositions, achieving throughputs of more than 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals compositional trends and regions of inhomogeneity, valuable for identifying manufacturing defects. With this self-supervised neural network–driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.

Science & Technology - Other Topics↗
DOE OSTI2021

High-Performance Piezoelectric MEMS Manufacturing & Application on Micro Power Generators (CRADA Final Report)

This project aimed to develop and characterize a micro-fabrication technology for wafer-scale heterogeneous integration of bulk piezoelectric materials on silicon substrates that could enable manufacturing of high-performance micro transducers. Typical technical challenges for piezoelectric microfabrication technologies are high-temperature processing, non-uniform film quality, and low process repeatability, which will be addressed during this project. Progress was made on preliminary research towards development of a technology platform that will enable a new micro-fabrication process to provide the highest electromechanical coupling among any other thin/thick-film deposition methods available today. This work has tested and characterized samples that we hope will further our goal of refining and enabling existing micro transducers to achieve unprecedentedly high performance. This project focused solely on the characterization of the proposed microfabrication process technology, and it did not consist of design or manufacturing of new transducers, such as sensors, actuators or energy harvesters based on this fabrication process. Future work will be aimed at the research and development of process steps specifically to enable integration of on-chip electronics with piezoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spin-orbit-controlled metal–insulator transition in Sr 2 IrO 4

In the context of correlated insulators, where electron–electron interactions (U) drive the localization of charge carriers, the metal–insulator transition is described as either bandwidth- or filling-controlled. Motivated by the challenge of the insulating phase in Sr 2 IrO 4 , a new class of correlated insulators has been proposed, in which spin–orbit coupling (SOC) is believed to renormalize the bandwidth of the half-filled j eff = 1/2 doublet, allowing a modest U to induce a charge-localized phase. Although this framework has been tacitly assumed, a thorough characterization of the ground state has been elusive. Furthermore, direct evidence for the role of SOC in stabilizing the insulating state has not been established, because previous attempts at revealing the role of SOC have been hindered by concurrently occurring changes to the filling. Here, we overcome this challenge by employing multiple substituents that introduce well-defined changes to the signatures of SOC and carrier concentration in the electronic structure, as well as a new methodology that allows us to monitor SOC directly. Specifically, we study Sr 2 Ir 1-x T x O 4 (T = Ru, Rh) by angle-resolved photoemission spectroscopy, combined with ab initio and supercell tight-binding calculations. This allows us to distinguish relativistic and filling effects, thereby establishing conclusively the central role of SOC in stabilizing the insulating state of Sr 2 IrO 4 . Most importantly, we estimate the critical value for SOC in this system to be λ c = 0.42 ± 0.01 eV, and provide the first demonstration of a spin–orbit-controlled metal–insulator transition.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2025

Atomistic characterization of hydration-dependent fuel cell ionomer nanostructure: validation by vibrational spectroscopy

The development of Nafion alternatives for fuel cells and electrolyzers requires a fundamental understanding of hydration-dependent ion-exchange site acid/base chemistry. We present here reactive force field (ReaxFF) molecular dynamics (MD) simulations of Nafion at varying molar water/ion-exchange-site ratios (λ), which we correlate to our experimental and density functional theory-based vibrational spectra. ReaxFF describes the formation and breaking of covalent bonds, enabling simulations of proton exchange between sulfonic acid/sulfonate groups and water/hydronium. Our MD simulations determine the λ-dependent equilibrium proportions of protonated and deprotonated sites. We find that protonated sites persist across all λ from 0 to 20, challenging the widely accepted notion that all sites are ionized above a threshold λ value (e.g., 3 or 4). Our simulations generate hundreds of realistic exchange site environments, the characterization of which are based on 6 Å radii sulfur-centered ‘inner-spheres’. These inner- and outer-sphere regions elucidate an interplay of stereoelectronic factors that influence protonation states, including the number of inner-sphere waters (Λ). Our simulations produce broad λ-dependent distributions of Λ values, representing non-uniform exchange site hydration. Moreover, we demonstrate that these distributions are specific to both protonated and deprotonated sites, with significant overlap between the two distributions for all non-zero λ. These distributions underpin the IR spectra of hydrated membranes, with each exchange site contributing an IR spectrum characteristic of its protonation state and Λ. We expect that these nanostructural characterizations of Nafion exchange sites will contribute to the development of new ionomers.

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

Data-driven causal model discovery and personalized prediction in Alzheimer's disease

Abstract With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Zheng, Haoyang (ORCID:0000000168358242)↗
DOE OSTI2020

Technology Development for Dry Storage of Aluminum-Clad Spent Nuclear Fuel - 20490

A candidate disposition pathway for the > 13 MTHM of aluminum-clad spent nuclear fuel (ASNF), owned and managed by the U.S. Department of Energy, is the drying and placement of the SNF into sealed-canister dry storage, with the ASNF-in- canisters 'road-ready' for transportation to and final direct disposal in a repository waste package. Technical information gaps in fuel drying, and fuel dry storage behavior, have previously challenged the declaration of technology readiness for drying and placement of this fuel into the DOE Standard Canister design for > 50 years of safe dry storage. The principal technical information gaps included: i) characterization and thermal dehydration behavior of aluminum (oxy)hydroxide films attendant on the cladding due to film formation during reactor operation and during post-discharge up to long-term wet storage histories; and ii) G-value data to enable estimation of the radiolytic gas generation from the cladding with its (oxy)hydroxide films. Thus, the oxide films on the ASNF challenged the safety of a sealed storage canister with thermal and radiolytic decomposition of the waters on the films that can lead to corrosion, pressurization, and flammability issues. These gaps in the technical information base have largely been closed. This paper discusses the investigations at the Idaho National Laboratory (INL) and the Savannah River National laboratory (SRNL), and outlines the pending technology development work for input to an engineering design to enable a road-ready dry storage system for ASNF. The ASNF inventory considered for road-ready dry storage is stored at the Savannah River Site (SRS) and at the Idaho Nuclear Technology and Engineering Center (INTEC) at the INL. The ASNF inventory in the SRS L Basin is from foreign and domestic research reactors (FRR and DRR), and is diverse in terms of design, irradiation, and post-reactor-discharge storage conditions; these factors yield a range of characteristics of cladding oxide films on ASNF. Mixed aluminum (oxy)hydroxide (boehmite and bayerite/gibbsite) films, non-uniform in thicknesses up to a maximum local thickness bounded by 25 μm, were observed on ASNF materials removed from wet storage in the L Basin and in non-sealed dry storage at the SRS following reactor service and a long-term (up to 40+ years) interim storage history. The ASNF inventory at INTEC, in both wet and vented dry storage, is predominantly from the Advanced Test Reactor (ATR), but it also includes DRR and FRR fuel. To address a profound behavior of these films, radiolysis testing of aluminum specimens with mixed type boehmite/bayerite oxide films was performed to develop basic data on G-values for production of radiolytic hydrogen under dry storage conditions with nominal relative humidity, temperature, and cover gas. Modeling and simulation of canister internal environments with postulated inventories of oxide films provides estimation of the evolution of the conditions of the canister loaded with ASNF. Simultaneous Thermal Analysis (TGA/DSC) of hydrated oxide powders, and laboratory-scale aluminum specimens with a bayerite film (∼10 μm) using TGA methods, inform drying time/temperature conditions to be used for the ASNF. A demonstration project is recommended for Verification and Validation of the drying and storage of the ASNF. Remaining major tasks leading up to the hot demonstration include scale-up radiolysis testing and scale-up drying testing. Engineering design with the information from the technology program will establish the safety basis and enable long-term (> 50 years) dry storage compatibility with ASNF in the DOE Standard Canister pending its transportation to and disposal in a repository. This full capability would show the ASNF-in-canister storage system to be road-ready. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Characterization of superconducting through-silicon vias as capacitive elements in quantum circuits

The large physical size of superconducting qubits and their associated on-chip control structures presents a practical challenge toward building a large-scale quantum computer. In particular, transmons require a high-quality-factor shunting capacitance that is typically achieved by using a large coplanar capacitor. Other components, such as superconducting microwave resonators used for qubit state readout, are typically constructed from coplanar waveguides, which are millimeters in length. Here, we use compact superconducting through-silicon vias to realize lumped-element capacitors in both qubits and readout resonators to significantly reduce the on-chip footprint of both of these circuit elements. We measure two types of devices to show that through-silicon vias are of sufficient quality to be used as capacitive circuit elements and provide a significant reduction in size over existing approaches.

Physics↗
DOE OSTI2026

Characterizing the Velocity Anisotropy of the Milky Way’s Stellar Halo

Abstract Modeling the Milky Way stellar halo requires well-determined density and velocity anisotropy profiles. However, it has been challenging to gather a large sample of stars with six-dimensional data that extend beyond 40 kpc to map the outer halo. Our work investigates the velocity anisotropy in the Milky Way stellar halo with more than 10,000 blue horizontal-branch stars, combining Gaia astrometric data and spectroscopic data from SEGUE, DESI, and LAMOST. This large sample allows us to obtain a detailed profile of up to ∼50 kpc. Radial velocities are predominant in the inner halo (<30 kpc), and the anisotropy presents a smooth decrease before rapidly dropping to negative values, becoming dominated by tangential dispersion velocities. Removing the main known accreted structures of the Milky Way makes the anisotropy profile radially dominated at all radii. Our profile clearly shows an increase in the anisotropy from the center of the Galaxy, in accordance to the simulations. We also investigate the correlation of anisotropy with metallicity and with color. The lack of correlation between metallicity and anisotropy in our clean sample reinforces that this relation is driven by merger events. The initial exploration with color indicates a relation between kinematics and age, showing that older stars are dynamically colder and present less radial orbits than younger stars in the inner halo.

Barbosa, F. O. [Universidade de São Paulo] (ORCID:↗
NASA NTRS2015

Applying Advanced Ground-Based Remote Sensing in the Southeast Asian Maritime Continent to Characterize Regional Proficiencies in Smoke Transport Modeling

This work describes some of the most extensive ground-based observations of the aerosol profile collected in Southeast Asia to date, highlighting the challenges in simulating these observations with a mesoscale perspective. An 84-h WRF Model coupled with chemistry (WRF-Chem) mesoscale simulation of smoke particle transport at Kuching, Malaysia, in the southern Maritime Continent of Southeast Asia is evaluated relative to a unique collection of continuous ground-based lidar, sun photometer, and 4-h radiosonde profiling. The period was marked by relatively dry conditions, allowing smoke layers transported to the site unperturbed by wet deposition to be common regionally. The model depiction is reasonable overall. Core thermodynamics, including landsea-breeze structure, are well resolved. Total model smoke extinction and, by proxy, mass concentration are low relative to observation. Smoke emissions source products are likely low because of undersampling of fires in infrared sun-synchronous satellite products, which is exacerbated regionally by endemic low-level cloud cover. Differences are identified between the model mass profile and the lidar profile, particularly during periods of afternoon convective mixing. A static smoke mass injection height parameterized for this study potentially influences this result. The model does not resolve the convective mixing of aerosol particles into the lower free troposphere or the enhancement of near-surface extinction from nighttime cooling and hygroscopic effects.

Aerosols; Air quality; Lidars/Lidar observations; ↗
NASA NTRSDate not supplied

Detecting and Characterizing Patterns of Failure in Complex Engineered Systems: an Ontology Development and Clustering Approach

While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

Systems Engineering↗
NASA NTRSDate not supplied

Initial Whirl-Flutter Characterization of the TiltRotor Aeroelastic Stability Testbed

This paper discusses the initial wind tunnel test of the TiltRotor Aeroelastic Stability Testbed (TRAST). TRAST is a generic tiltrotor testbed developed in collaboration between NASA and the Army. Ultimately, this test was a checkout of the model systems, functionality and familiarization, but also obtained subcritical whirl-flutter data in the terms of frequency and damping. Flutter data include two main configurations with different pitch spring stiffness, referred to as 4k and 8k, that were tested at various rotor speeds and airspeeds at the NASA Langley Transonic Dynamics Tunnel. The test included two modes of drivetrain operation: powered and windmilling. However, powered mode of operation was only conducted with the 8k pitch spring. This test reinforced the traditional knowledge of whirl-flutter trends such as flutter speed would decrease with an increase in rotor speed. The critical mode consistently being the wing vertical bending mode. The chord mode as expected was not affected by the pitch spring and was likely to go unstable at a tunnel airspeed slightly beyond the wing vertical bending mode. There were also test specific challenges such as the TRAST modal damping was more sensitive to temperature and amplitude motor than was expected. This test gathered valuable data on the baseline characterization of TRAST, how to improve the model and test practices for future wind tunnel testing. Additionally, a new more automated method for experimental subcritical damping determination based on the Stockwell transform has been demonstrated that may lead to more consistent whirl-flutter stability boundaries.

whirl flutter↗
NASA NTRS2023

Initial Characterization of the TiltRotor Aeroelastic Stability Testbed

This paper discusses the initial wind tunnel test of the TiltRotor Aeroelastic Stability Testbed (TRAST). TRAST is a generic tiltrotor testbed developed in collaboration between NASA and the Army. Ultimately, this test was a checkout of the model systems, functionality and familiarization, but also obtained subcritical whirl-flutter data in the terms of frequency and damping. Flutter data include two main configurations with different pitch spring stiffness, referred to as 4k and 8k, that were tested at various rotor speeds and airspeeds at the NASA Langley Transonic Dynamics Tunnel. The test included two modes of drivetrain operation: powered and windmilling. However, powered mode of operation was only conducted with the 8k pitch spring. This test reinforced the traditional knowledge of whirl-flutter trends such as flutter speed would decrease with an increase in rotor speed. The critical mode consistently being the wing vertical bending mode. The chord mode as expected was not affected by the pitch spring and was likely to go unstable at a tunnel airspeed slightly beyond the wing vertical bending mode. There were also test specific challenges such as the TRAST modal damping was more sensitive to temperature and amplitude motor than was expected. This test gathered valuable data on the baseline characterization of TRAST, how to improve the model and test practices for future wind tunnel testing. Additionally, a new more automated method for experimental subcritical damping determination based on the Stockwell transform has been demonstrated that may lead to more consistent whirl-flutter stability boundaries.

whirl flutter↗
NASA NTRS2014

The Spectral Response of the Landsat-8 Operational Land Imager

This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and their magnitudes are consistent with the results of the pre-launch characterizations. The OLI in-band response was characterized both at the integrated instrument level for a sampling of detectors and by an analytical stack-up of component measurements. The out-of-band response was characterized using a combination of Focal Plane Module (FPM) level measurements and optical component level measurements due to better sensitivity. One of the challenges of a pushbroom design is to match the spectral responses for all detectors so that images can be flat-fielded regardless of the spectral nature of the targets in the imagery. Spectral variability can induce striping (detector-to-detector variation), banding (FPM-to-FPM variation) and other artifacts in the final data products. Analyses of the measured spectral response showed that the maximum discontinuity between FPMs due to spectral filter differences is 0.35% for selected targets for all bands except for Cirrus, where there is almost no signal. The average discontinuity between FPMs is 0.12% for the same targets. These results were expected and are in accordance with the OLI requirements. Pre-launch testing identified low levels (within requirements) of spectral crosstalk amongst the three HgCdTe (Cirrus, SWIR1 and SWIR2) bands of the OLI and on-orbit data confirms this crosstalk in the imagery. Further post-launch analyses and simulations revealed that the strongest crosstalk effect is from the SWIR1 band to the Cirrus band; about 0.2% of SWIR1 signal leaks into the Cirrus. Though the total crosstalk signal is only a few counts, it is evident in some scenes when the in-band cirrus signal is very weak. In moist cirrus-free atmospheres and over typical land surfaces, at least 30% of the cirrus signal was due to the SWIR1 band. In the SWIR1 and SWIR2 bands, crosstalk accounts for no more than 0.15% of the total signal.

Characterization↗
NASA NTRS2008

New Worlds Observer Formation Control Design Based on the Dynamics of Relative Motion

The New Worlds Observer (NWO) mission is designed for the direct detection and characterization of extrasolar planets. The NWO mission concept employs a two spacecraft leader-follower formation on a trajectory around the Earth/Moon-Sun L(sub 2) Libration Point. The leader spacecraft is baselined as a 4 meter optical telescope. The follower, Starshade spacecraft, is designed to suppress light from a central body star permitting direct detection of a surrounding exoplanetary system. The current design requires a nominal leader-follower separation range of 72 Megameters. NWO poses many challenges including formation control. NWO cycles between three principal control modes during the nominal mission timeline: science (fine pointing), realignment and transition. This paper examines formation control strategies in the context of dynamics of relative motion for two spacecraft operating in the vicinity of the Earth/Moon-Sun L(sub 2)libration point. The paper presents an overview of the equations of relative motion followed by a discussion of each of the control modes. Discussion and analysis characterize control strategies for each of the mission control modes, including requirements, implementation challenges and project fuel budgets.

Luquette, Richard J.↗
DOE OSTI2025

Foaming prediction in pure liquids from dimensionless numbers inspired by the theory of fluid behavior for drops

Foaming prediction is critical for selecting materials and designing processes in industries such as bioprocessing and gas processing. Existing models lack the generality needed for a wide range of materials and overlook the foaming behavior in pure liquids. Here, this work presents a novel method for predicting foaming in pure liquids based on their density, surface tension, and viscosity, using Reynolds ( Re ) and Ohnesorge ( Oh ) numbers. A foaming prediction map, leveraging the theory of fluid drop behavior, was developed by plotting these numbers. This map delineates distinct non-foaming and foaming regions, functioning as a binary classifier for foaming predictions. The map was fitted and validated through shake test experiments on 46 liquids, demonstrating reliable predictions, except for a specific region characterized by small Oh and large Re numbers. This region corresponded to relatively low foam stability and high turbulence, making foaming predictions challenging for liquids in this category.

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

Machine Learning‐Augmented Molecular Dynamics Simulations (MD) Reveal Insights Into the Disconnect Between Affinity and Activation of ZTP Riboswitch Ligands

Abstract The challenge of targeting RNA with small molecules necessitates a better understanding of RNA–ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand‐induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure‐informed design, crystallography, and machine learning‐augmented all‐atom molecular dynamics simulations (MD), we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the 5‐aminoimidazole–4–carboxamide ribonucleotide triphosphate (ZTP) riboswitch, a ligand‐binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on‐rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

Chemistry↗