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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Effects of Tethered Polymers on Dynamics of Nanoparticles in Unentangled Polymer Melts

Polymer-tethered nanoparticles (NPs) are commonly added to a polymer matrix to improve the material properties. Critical to the fabrication and processing of such composites is the mobility of the tethered NPs. Here, we study the motion of tethered NPs in unentangled polymer melts using molecular dynamics simulations, which offer a precise control of the grafted chain length N g and the number z of grafted chains per particle. As Ng increases, there is a crossover from particle-dominated to tethered-chain-dominated terminal diffusion of NPs with the same z. The mean squared displacement of loosely tethered NPs in the case of tethered-chain-dominated terminal diffusion exhibits two subdiffusive regimes at intermediate time scales for small z. The first one at shorter time scales arises from the dynamical coupling of the particle and matrix chains, while the one at longer time scales is due to the participation of the particle in the dynamics of the tethered chains. The friction of loosely grafted chains in unentangled melts scales linearly with the total number of monomers in the chains, as the friction of individual monomers is additive in the absence of hydrodynamic coupling. As more chains are grafted to a particle, hydrodynamic interactions between grafted chains emerge. As a result, there is a nondraining layer of hydrodynamically coupled chain segments surrounding the bare particle. Outside the nondraining layer is a free-draining layer of grafted chain segments with no hydrodynamic coupling. The boundary of the two layers is the stick surface where the shear stress due to the relative melt flow is balanced by the friction between the grafted and melt chains in the interpenetration layer. The stick surface is located further away from the bare surface of the particle with higher grafting density.

42 ENGINEERING↗
DOE OSTI2023

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are threefold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws governing vehicle dynamics into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Strengthening the Security of Operational Technology: Understanding Contemporary Bill of Materials

The evolution of cyber-physical infrastructure has made its security more challenging. The last few years have witnessed a convergence of hardware and software segments in various domains, including operational technology (OT) which is responsible for carrying out critical tasks such as monitoring and controlling power grids, nuclear plants, transportation, and emergency services. Both hardware and software encapsulate numerous open source and proprietary subcomponents, making it crucial for end-users to understand the composition of the products they are using. For example, wind turbines incorporate thousands of lines of code (software) used for the turbine's design, planning, operation, and analytics in addition to the numerous hardware subcomponents that construct it. Due to the highly complex nature of software and hardware, knowledge of the components and subcomponents is required to mitigate cyber vulnerabilities and defend against cyberattacks. There has also been a transformation from a traditional linear supply chain into a global, dynamic, diverse, and interconnected system. The digitization of the supply chain makes it easier to find and exploit vulnerabilities. Critical infrastructures (e.g., power grids, oil, natural gas, water, and wastewater) rely on OT to function, and if the OT is compromised, equipment damage and potential interruption of services could result. A significant security measure to protect OT systems from disruption is to develop a supply chain bill of materials (BoM) corresponding to the software and hardware used in OT, along with attestations amongst vendors and asset owners. A supply chain BoM is a proactive way to understand the inherent vulnerabilities in the system and mitigate them in advance of being exploited. BoMs bolster the trust placed in the digital infrastructure and enhance software supply chain security by sustaining the management of component obsolescence and compliance, along with the seclusion of unsafe segments of a specific product. Adopting BoM tools is becoming increasingly important across various government sectors, as evidenced by the recent U.S. executive order on cybersecurity (NIST 2021). This paper aims to classify BoMs based on structure, functionality, component type, and architecture. The work also discusses case studies to further highlight the benefits of BoMs. In addition, it identifies missing pieces in existing BoM implementations so that future research may identify bounds on where it could expect to make improvements and directly enable researchers to identify promising areas for exploration. Further, the authors provide valuable recommendations to tool developers, researchers, and standardizing organizations (policymakers), additionally benefitting critical infrastructure owners and government executives. This aids in paving a path for future work, thereby, providing suggestions to determine a tool for consumers that best suit their needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An optimization method for chaotic turbulent flow

Evidence indicates that quantities-of-interest in some turbulent flows can be controlled despite the overall chaotic dynamics. It is typically thought that this is via relatively deterministic, larger-scale components of the turbulence. However, finding such controls, if they exist, is challenging because chaos causes sensitivity gradients to explode and the search space to become intractably non-convex. This challenge is analyzed, and a penalty method is introduced to cope with it. In the new approach, the time domain is broken into segments approximately matching the chaos time scales, so that the solution within each segment is both physical and relatively deterministic. The initial condition of each segment is included in an adjoint-based gradient optimization, which temporarily introduces artificial Δq discontinuities in the overall solution. The optimization then proceeds in stages with increasing penalization of Δq. The method is developed and illustrated for a logistic map, the Lorenz Equation, and an advection augmented Kuramoto–Sivashinsky Equation. These examples show how the Δq temporarily increases the search scale prior to the strong Δq → 0 penalization that recovers a physical solution. It is then applied to turbulent Kolmogorov flow, for which it also far outperforms a standard adjoint-based gradient search. Finally, the utility of such an optimized chaotic solution is discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Probing High-Pressure Structural Evolution in Polyurea with In Situ Energy-Dispersive X-ray Diffraction and Molecular Dynamics Simulations

Polyurea, an elastomer with a phase-segregated microstructure, has been proven as an effective coating in defense applications. To gain a more complete understanding of the high-pressure atomic-level morphology of these phases and to validate molecular dynamics (MD) simulations, multi-angle energy-dispersive X-ray diffraction experiments were performed in situ up to pressures of ~6 GPa at room temperature. Structure factors were obtained and compared to MD simulations with an average error of less than 5% between major peak positions. The first sharp diffraction peak shifted from 4.56 Å to lower d-spacing with pressure, indicating compression between hard segments. This was further supported by the behavior of a peak at ~3.86 Å from the pair distribution function (PDF), suspected to represent π-stacking and separation between soft segments. Compression within the hard segments themselves is minimal as low-r peaks in the PDF are not greatly affected by pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

NUMERICAL CALCULATION OF LOSSES OF TRAPPED VORTICES UNDER STRONG RF MEISSNER CURRENT AND DC SUPERHEATING FIELD IN TYPE II SUPERCONDUCTORS

Research on the vortex dynamics and enhancing of superheating field in superconductors has attracted much attention in accelerator physics community to develop next-generation high-performance accelerator cavities. However, the extreme dynamics of curvilinear elastic vortices driven by very strong currents close to the depairing limit or superheating field of a superconductor with a nanostructured surface has not been well understood. We calculated the superheating field Hsh and critical momentum kc characterizing the wavelength of the instability ?m of the Meissner state to flux penetration by solving numerically the Ginzburg?Landau equations. A bulk superconductor, superconductor with the inhomogeneous surface disorder (S-S), and multilayered surface (S-I-S) have been thoroughly investigated in this work. Our result showed that S-S and S-I-S structures can enhance the superheating field well above their clean limit. In this work extensive numerical simulation of the power dissipated by an oscillating vortex segment driven by the surface ac Meissner currents was performed. Our simulations take into account the nonlinear vortex line tension, vortex mass, Bardeen?Stephen viscous vortex drag applicable at low fields, and nonlinear Larkin-Ovchinnikov (LO) viscous drag coefficient ?(v) at high fields and pinning force. We showed that the LO decrease of ?(v) with the vortex velocity v could radically change the field dependence of the surface resistance Ri(H) caused by trapped vortices. At low frequencies Ri(H) exhibits a conventional increase with H. However, as frequency increases, the surface resistance becomes a nonmonotonic function of H which decreases with H at higher fields irrespective of the pinning distribution. Overheating can mask the descending field dependence of Ri(H) as frequency increases. Our numerical simulations also show that the LO effect can cause a vortex bending instability at high field amplitudes and frequencies, giving rise to the formation of dynamic kinks along with the vortex when a vortex is pinned strongly to one end. Nonlinear losses of trapped vortices in thick films under high-amplitude RF fields as functions of frequency, mean free path and pinning characteristics have been calculated.

Pathirana, Walive↗
DOE OSTI2024

Convolutional Non-Homogeneous Poisson Process and its Application to Wildfire Ignition Risk Quantification for Power Delivery Networks

To quantify wildfire ignition risks on power delivery networks, the current practice predominantly relies on the empirically calculated fire danger indices, which may not well capture the effects of dynamically changing environmental factors. This article proposes a spatio-temporal point process model, known as the Convolutional Non-homogeneous Poisson Process (cNHPP), and applies the model to quantify wildfire ignition risks for power delivery networks. The proposed model captures both the current (i.e., instantaneous) and cumulative (i.e., historical) effects of key environmental processes (i.e., covariates) on wildfire risks, as well as the spatio-temporal dependency among different segments of the power delivery network. The computation and interpretation of the intensity function are thoroughly investigated. We apply the proposed approach to estimate wildfire ignition risks on major transmission lines in California, using historical fire data, meteorological and vegetation data obtained from the National Oceanic and Atmospheric Administration and National Aeronautics and Space Administration. Here, a comprehensive comparison study is performed to show the applicability and predictive capability of the proposed approach.

Non-homogeneous Poisson Process↗
DOE OSTI2020

Understanding the origin of softness in structurally tailored and engineered macromolecular (STEM) gels: A DPD study

Structurally tailored and engineered macromolecular (STEM) gels are polymer networks containing a primary network (the “STEM-0” gel) and side chains that are anchored to this network (forming the “STEM-1” gel). The ability to vary the features of the side chains provides a robust means of tailoring the macroscopic properties of the material. We use dissipative particle dynamics (DPD) simulations to determine the mechanical response of STEM-1 gels to uniaxial compression for various values of the side chain length, n sc . While the Young’s modulus of the material is significantly decreased with increasing n sc (at a fixed density of grafted side chains), above a certain saturation value, increasing n sc does not lead to any further softening of the sample. Using the simulations, we calculate the relevant stresses, which are related to the Young’s modulus and the number of entanglements in the network. We show that the backbone chains become more spread out upon addition of sidechains, leading to a decrease in the physical entanglements between backbone segments. This observation accounts for the decrease of stress in the backbone network and the softening of the networks through the addition of sidechains. For long chains, however, the number density of physical crosslinks between side chains is independent of side chain length; this observation explains the observed stress saturation in the STEM-1 gels. Our approach allows us to correlate the molecular architecture of the gels to the resultant macroscopic mechanical behavior and provide guidelines for fabricating STEM gels with well-defined mechanical properties, which allow the materials to be used for a variety of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ion Transport in 2D Nanostructured $\pi$-Conjugated Thieno[3,2- b ]thiophene-Based Liquid Crystal

Leveraging the self-assembling behavior of liquid crystals designed for controlling ion transport is of both fundamental and technological significance. Here, we have designed and prepared a liquid crystal that contains (2,5-bis(5-(2,5,8,11-tetraoxatridecan-13-yl)thiophen-2yl)thieno[3,2-b]thiophene (BTTT) as mesogenic core and conjugated segment and symmetric tetra(ethylene oxide) (EO4) as polar side chains for ionic conducting regions. Driven by the crystallization of the BTTT cores, BTTT/dEO4 exhibits well-ordered smectic phases below 71.5 °C as confirmed by differential scanning calorimetry, polarized optical microscope, temperature dependent wide-angle X-ray scattering and grazing incidence wide-angle X-ray scattering (GIWAXS). We adopted a combination of experimental GIWAXS and molecular dynamics (MD) simulations to better understand the molecular packing of BTTT/dEO4 films, particularly when loaded with the ion conducting salt, lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Ionic conduction of BTTT/dEO4 is realized by the addition of LiTFSI, with the material able to maintain smectic phases up to r = [Li + ]/[EO] = 0.1. In this work, The highest ionic conductivity of 8 × 10 -3 S/cm was attained at an intermedium salt concentration of r = 0.05. It was also found that ion conduction in BTTT/dEO4 is enhanced by forming a smectic layered structure with irregular interfaces between the BTTT and EO4 layers and by the lateral film expansion upon salt addition. This can be explained by the enhancement of the misalignment and configurational entropy of the side chains, which increase their local mobility and that of the solvated ions. Our molecular design thus illustrates how, beyond the favorable energetic interactions that drive the assembly and ion solvating domains, modulation of entropic effects can also be favorably harnessed to improve ion conduction.

thin film electrochemical impedance spectroscopy↗
DOE OSTI2020

Molecular Structure of Sphingomyelin in Fluid Phase Bilayers Determined by the Joint Analysis of Small-Angle Neutron and X-ray Scattering Data

In this work, we have determined the fluid bilayer structure of palmitoyl sphingomyelin (PSM) and stearoyl sphingomyelin (SSM) by simultaneously analyzing small-angle neutron and X-ray scattering data. Using a newly developed scattering density profile (SDP) model for sphingomyelin lipids, we report structural parameters including the area per lipid, total bilayer thickness, and hydrocarbon thickness, in addition to lipid volumes determined by densitometry. Unconstrained all-atom simulations of PSM bilayers at 55 °C using the C36 CHARMM force field produced a lipid area of 56 Å 2 , a value that is 10% lower than the one determined experimentally by SDP analysis (61.9 Å2). Furthermore, scattering form factors calculated from the unconstrained simulations were in poor agreement with experimental form factors, even though segmental order parameter (S CD ) profiles calculated from the simulations were in relatively good agreement with S CD profiles obtained from NMR experiments. Conversely, constrained area simulations at 61.9 Å 2 resulted in good agreement between the simulation and experimental scattering form factors, but not with S CD profiles from NMR. We discuss possible reasons for the discrepancies between these two types of data that are frequently used as validation metrics for molecular dynamics force fields.

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

Generative learning for slow manifolds and bifurcation diagrams

In dynamical systems characterized by separation of time scales, the approximation of so called “slow manifolds”, on which the long term dynamics lie, is a useful step for model reduction. Initializing on such slow manifolds is a useful step in modeling, since it circumvents fast transients, and is crucial in multiscale algorithms (like the equation-free approach) alternating between fine scale (fast) and coarser scale (slow) simulations. In a similar spirit, when one studies the infinite time dynamics of systems depending on parameters, the system attractors (e.g., its steady states) lie on bifurcation diagrams (curves for one-parameter continuation, and more generally, on manifolds in state parameter space. Sampling these manifolds gives us representative attractors (here, steady states of ODEs or PDEs) at different parameter values. Algorithms for the systematic construction of these manifolds (slow manifolds, bifurcation diagrams) are required parts of the “traditional” numerical nonlinear dynamics toolkit. In more recent years, as the field of Machine Learning develops, conditional score-based generative models (cSGMs) have been demonstrated to exhibit remarkable capabilities in generating plausible data from target distributions that are conditioned on some given label. It is tempting to exploit such generative models to produce samples of data distributions (points on a slow manifold, steady states on a bifurcation surface) conditioned on (consistent with) some quantity of interest (QoI, observable). In this work, we present a framework for using cSGMs to quickly (a) initialize on a low-dimensional (reduced-order) slow manifold of a multi-time-scale system consistent with desired value(s) of a QoI (a “label”) on the manifold, and (b) approximate steady states in a bifurcation diagram consistent with a (new, out-of-sample) parameter value. This conditional sampling can help uncover the geometry of the reduced slow-manifold and/or approximately “fill in” missing segments of steady states in a bifurcation diagram. Finally, the quantity of interest, which determines how the sampling is conditioned, is either known a priori or identified using manifold learning-based dimensionality reduction techniques applied to the training data.

Dynamical systems↗
DOE OSTI2021

Advances in Multimodal Characterization of Structural Materials

The myriad detectors and instruments now available for materials characterization provide researchers with an ever-growing suite of tools to probe material behavior. Progress in the development of instrumentation and workflows that enable the collection, and leverage the potential, of various data modalities have provided novel insights into material behavior. Using data across multiple length scales, or performing complementary analyses of in situ and ex situ data, can help reveal a more complete picture of dynamic processes or material structure. However, the accurate combination, or fusion, of these disparate data modalities presents new challenges. Differences in resolution, as well as the varying length scales at which physical phenomena are exploited to generate these data, necessitate novel approaches to accurately interpret and combine these data. Furthermore, the papers within this special topic focus on the collection and fusion of multimodal data to better understand structural materials. From new frameworks and workflows for data segmentation and analysis, process monitoring, enhancing simulations, or interrogating mechanical response, these papers reveal the potential benefits of utilizing multimodal data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Calibration of the underground muon detector of the Pierre Auger Observatory

To obtain direct measurements of the muon content of extensive air showers with energy above 10 16.5 eV, the Pierre Auger Observatory is currently being equipped with an underground muon detector (UMD), consisting of 219 10 m 2 -modules, each segmented into 64 scintillators coupled to silicon photomultipliers (SiPMs). Direct access to the shower muon content allows for the study of both of the composition of primary cosmic rays and of high-energy hadronic interactions in the forward direction. As the muon density can vary between tens of muons per m 2 close to the intersection of the shower axis with the ground to much less than one per m 2 when far away, the necessary broad dynamic range is achieved by the simultaneous implementation of two acquisition modes in the read-out electronics: the binary mode, tuned to count single muons, and the ADC mode, suited to measure a high number of them. In this work, we present the end-to-end calibration of the muon detector modules: first, the SiPMs are calibrated by means of the binary channel, and then, the ADC channel is calibrated using atmospheric muons, detected in parallel to the shower data acquisition. The laboratory and field measurements performed to develop the implementation of the full calibration chain of both binary and ADC channels are presented and discussed. The calibration procedure is reliable to work with the high amount of channels in the UMD, which will be operated continuously, in changing environmental conditions, for several years.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Influence of NaCl on shape deformation of polymersomes

Polymersomes frequently appear in the literature as promising candidates for a wide range of applications from targeted drug delivery to nanoreactors. From a cell mimetic point of view, it is important to understand the size and shape changes of the vesicles in the physiological environment since that can influence the drug delivery mechanism. In this work we studied the structural features of polymersomes consisting of poly(ethylene glycol)–poly(dimethylsiloxane)–poly(ethylene glycol) at the nanoscopic length scale in the presence of NaCl, which is a very common molecule in the biotic aqueous environment. Here, we used dynamic light scattering (DLS), cryo-TEM, small angle neutron scattering (SANS) and small angle X-ray scattering (SAXS). We observed transformation of polymersomes from spherical to elongated vesicles at low salt concentration and into multivesicular structures at high salt concentration. Model fitting analysis of SANS data indicated a reduction of vesicle radius up to 47% and from the SAXS data we observed an increase in membrane thickness up to 8% and an increase of the PDMS hydrophobic segment up to 11% indicating stretching of the membrane due to osmotic imbalance. Also, from the increase in the interlamellar repeat distance up to 98% under high salt concentrations, we concluded that the shape and structural changes observed in the polymersomes are a combined result of osmotic pressure change and ion–membrane interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Using the Gerchberg-Saxton algorithm to reconstruct nonmodulated pyramid wavefront sensor measurements

Adaptive optics (AO) is a technique to improve the resolution of ground-based telescopes by correcting, in real-time, optical aberrations due to atmospheric turbulence and the telescope itself. With the rise of Giant Segmented Mirror Telescopes (GSMT), AO is needed more than ever to reach the full potential of these future observatories. One of the main performance drivers of an AO system is the wavefront sensing operation, consisting of measuring the shape of the above mentioned optical aberrations. Aims. The non-modulated pyramid wavefront sensor (nPWFS) is a wavefront sensor with high sensitivity, allowing the limits of AO systems to be pushed. The high sensitivity comes at the expense of its dynamic range, which makes it a highly non-linear sensor. We propose here a novel way to invert nPWFS signals by using the principle of reciprocity of light propagation and the Gerchberg-Saxton (GS) algorithm. We test the performance of this reconstructor in two steps: the technique is first implemented in simulations, where some of its basic properties are studied. Then, the GS reconstructor is tested on the Santa Cruz Extreme Adaptive optics Laboratory (SEAL) testbed located at the University of California Santa Cruz. This new way to invert the nPWFS measurements allows us to drastically increase the dynamic range of the reconstruction for the nPWFS, pushing the dynamics close to a modulated PWFS. The reconstructor is an iterative algorithm requiring heavy computational burden, which could be an issue for real-time purposes in its current implementation. However, this new reconstructor could still be helpful in the case of many wavefront control operations. In conclusion, this reconstruction technique has also been successfully tested on the Santa Cruz Extreme AO Laboratory (SEAL) bench where it is now used as the standard way to invert nPWFS signal.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Prismatic-to-basal plastic slip transition in zirconium

Here, we model three-dimensional dislocation ensembles in Zr pillars using newly developed mobility laws for dislocations on prismatic and basal planes. The effects of the loading orientation and temperature on the micro-scale mechanical response of single crystals are systematically explored. Easy dislocation glide is observed on prismatic planes, while hard glide occurs on basal planes. By selection of sample temperature and loading orientation, we show that the transition from prismatic to basal glide of dislocation ensembles can be controlled. We also show that the presence of dislocation cross-slip strongly promotes prismatic glide as a result of prismatic/basal cross-slip energy asymmetry. Cross-slip does not alter the occurrence of plastic slip transition but only induces a shift towards higher temperatures. The preponderance of plastic slip on basal or prismatic planes is found to be mediated by a transitional dislocation, which is composed of glissile segments on parallel prismatic planes connected by glissile super-jogs on basal planes. These findings provide a mechanistic understanding of experiments and highlight the significance of transitional prismatic-basal dislocations on the macroscopic characteristics of plasticity in HCP crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Work of Mechanical Degradation in Elongating Polymer Melts

Molecular dynamics simulations are used to study the mechanical degradation of well-entangled polymer melts during uniaxial extensional flow. Simulations measure the transient rise in extensional stresses and relate them to the molecular alignment and scission of chain backbones. Intermolecular entanglements couple chain scission in space and time, making degradation sensitive to deformation history and strain rate in ways not displayed by dilute polymer solutions. The rate of chain scission is nonmonotonic and peaks at strains corresponding to the maximum extensibility of entanglement segments but prior to the full extension of chain backbones. We measure a specific work per scission event w* and decompose it into separate contributions associated with chain alignment, chemical bond breaking, and scission-induced plasticity. We find chain scission in melts requires activating plastic dissipation that is multiple orders of magnitude larger than the chemical work required to break a covalent backbone bond. Our findings underscore the critical need to consider bulk polymer mechanics and rheology in designing efficient mechanical degradation and mechanochemical processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Complex water networks visualized by cryogenic electron microscopy of RNA

The stability and function of biomolecules are directly influenced by their myriad interactions with water. Here we investigated water through cryogenic electron microscopy (cryo-EM) on a highly solvated molecule: the Tetrahymena ribozyme. By using segmentation-guided water and ion modelling (SWIM), an approach combining resolvability and chemical parameters, we automatically modelled and cross-validated water molecules and Mg 2+ ions in the ribozyme core, revealing the extensive involvement of water in mediating RNA non-canonical interactions. Unexpectedly, in regions where SWIM does not model ordered water, we observed highly similar densities in both cryo-EM maps. In many of these regions, the cryo-EM densities superimpose with complex water networks predicted by molecular dynamics, supporting their assignment as water and suggesting a biophysical explanation for their elusiveness to conventional atomic coordinate modelling. Our study demonstrates an approach to unveil both rigid and flexible waters that surround biomolecules through cryo-EM map densities, statistical and chemical metrics, and molecular dynamics simulations.

59 BASIC BIOLOGICAL SCIENCES↗