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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Programming Amphiphilic Peptoid Oligomers for Hierarchical Assembly and Inorganic Crystallization

Natural organisms make a wide variety of exquisitely complex, nano-, micro-, and macroscale structured materials in an energy-efficient and highly reproducible manner. During these processes, the information-carrying biomolecules (e.g., proteins, peptides, and carbohydrates) enable (1) hierarchical organization to assemble scaffold materials and execute high-level functions and (2) exquisite control over inorganic materials synthesis, generating biominerals whose properties are optimized for their functions. Inspired by nature, significant efforts have been devoted to developing functional materials that can rival those natural molecules by mimicking in vivo functions using engineered proteins, peptides, DNAs, sequence-defined synthetic molecules (e.g., peptoids), and other biomimetic polymers. Among them, peptoids, a new type of synthetic mimetics of peptides and proteins, have received particular attention because they combine the merits of both synthetic polymers (e.g., high chemical stability and efficient synthesis) and biomolecules (e.g., sequence programmability and biocompatibility). The lack of both chirality and hydrogen bonds in their backbone results in a highly designable peptoid-based system with reduced structural complexity and side chain-chemistry-dominated properties. Here in this Account, we present our recent efforts in this field by programming amphiphilic peptoid sequences for (1) the controlled self-assembly into different hierarchically structured nanomaterials with favorable properties and (2) manipulating inorganic (nano)crystal nucleation, growth, and assembly into superstructures. First, we designed a series of amphiphilic peptoids with controlled side chain chemistries that self-assembled into 1D highly stiff and dynamic nanotubes, 2D membrane-mimetic nanosheets, hexagonally patterned nanoribbons, and 3D nanoflowers. These crystalline nanostructures exhibited sequence-dependent properties and showed promise for different applications. The corresponding peptoid self-assembly pathways and mechanisms were also investigated by leveraging in situ atomic force microscopy studies and molecular dynamics simulations, which showed precise sequence dependency. Second, inspired by peptide- and protein-controlled formation of hierarchical inorganic nanostructures in nature, we developed peptoid-based biomimetic approaches for controlled synthesis of inorganic materials (e.g., noble metals and calcite), in which we took advantage of the substantial side chain chemistry of peptoids and investigated the relationship between the peptoid sequences and the morphology and growth kinetics of inorganic materials. For example, to overcome the challenges (e.g., complexity of protein- and peptide-folding, poor thermal and chemical stabilities) facing the area of protein- and peptide-controlled synthesis of inorganic materials, we recently reported the design of sequence-defined peptoids for controlled synthesis of highly branched plasmonic gold particles. Moreover, we developed a rule of thumb for designing peptoids that predictively enabled the morphological evolution from spherical to coral-shaped gold nanoparticles (NPs). With this Account, we hope to stimulate the research interest of chemists and materials scientists and promote the predictive synthesis of functional and robust materials through the design of sequence-defined synthetic molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Orthogonal luminescence lifetime encoding by intermetallic energy transfer in heterometallic rare-earth MOFs

Lifetime-encoded materials are particularly attractive as optical tags, however examples are rare and hindered in practical application by complex interrogation methods. Here, we demonstrate a design strategy towards multiplexed, lifetime-encoded tags via engineering intermetallic energy transfer in a family of heterometallic rare-earth metal-organic frameworks (MOFs). The MOFs are derived from a combination of a high-energy donor (Eu), a low-energy acceptor (Yb) and an optically inactive ion (Gd) with the 1,2,4,5 tetrakis(4-carboxyphenyl) benzene (TCPB) organic linker. Precise manipulation of the luminescence decay dynamics over a wide microsecond regime is achieved via control over metal distribution in these systems. Demonstration of this platform’s relevance as a tag is attained via a dynamic double encoding method that uses the braille alphabet, and by incorporation into photocurable inks patterned on glass and interrogated via digital high-speed imaging. This study reveals true orthogonality in encoding using independently variable lifetime and composition, and highlights the utility of this design strategy, combining facile synthesis and interrogation with complex optical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
DOE OSTI2022

The Conventional Gas Diffusion Electrode May Not Be Resistant to Flooding during CO 2 /CO Reduction

The electrochemical CO 2 or CO reduction to chemicals and fuels using renewable energy is a promising way to reduce anthropogenic carbon emissions. The gas diffusion electrode (GDE) design enables low-carbon manufacturing of target products at a current density (e.g., 500 mA cm –2 ) relevant to industrial requirements. However, the long-term stability of the GDE is restricted by poor water management and flooding, resulting in a significant hydrogen evolution reaction (HER) within almost an hour. The optimization of water management in the GDE demands a thorough understanding of the role of the gas diffusion layer (GDL) and the catalyst layer (CL) distinctively. In this work, the hydrophobicity of the GDL and CL is independently adjusted to investigate their influence on gas transport efficiency and water management. The gas transport efficiency is more enhanced with the increase in hydrophobicity of the GDL than the CL. Direct visualization of water distribution by optical microscope and micro-computed tomography demonstrates that the water flow pattern transfers from the stable displacement to capillary fingering as GDL hydrophobicity increases. Unfortunately, only increasing the hydrophobicity is not sufficient to prevent flooding. A revolutionary change in the design of the GDE structure is essential to maintain the long-term stability of CO 2 /CO reduction.

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

Damage of prismatic lithium‐ion cells subject to bending: Test, model, and detection

Abstract The mechanically induced internal short circuit (ISC) is one of the major safety concerns of lithium‐ion batteries. Mechanical abuse tests are often performed to evaluate the integrity and safety of lithium‐ion batteries under mechanical loadings. Except for the widely explored compression‐dominated indentation tests, bending is another typical real‐world loading condition that is tension‐dominated. To investigate the mechanical damage and ISC behavior of batteries under bending, we carried out controlled three‐point bending tests in four progressive steps on prismatic battery cells with maximum deflections ranging from 38% to 76% of the cell thickness. None of the tested cells experienced an ISC. We then conducted 3D X‐ray computed tomography (CT) scanning on the bent cells after unloading. X‐ray CT images showed three out of the four tested cells have extensive cracking in the electrode layers at the bottom side (opposite to the loading head). This indicates that cracking does not necessarily lead to an ISC under bending. Electrochemical impedance spectroscopy was also measured on the bent cells and substantial changes were observed. Both the bulk resistance and charge‐transfer resistance increased significantly after bending, which could influence the battery performance and lifespan. We then developed a detailed finite (FE) element model to further investigate the mechanical deformation and failure mechanisms. The FE model successfully predicts the load–displacement response and reproduces the deformation patterns. The findings and the FE model developed in the present study provide useful insights and tools for the battery structure and crash safety design. image

25 ENERGY STORAGE↗
DOE OSTI2024

The impact of urban configuration types on urban heat islands, air pollution, CO 2 emissions, and mortality in Europe: a data science approach

The world is becoming increasingly urbanized. As cities around the world continue to grow, it is important for urban planners and policymakers to understand how different urban configuration patterns affect the environment and human health. We aimed at identifying European urban configuration types, based on the Local Climate Zones categories and street design variables from Open Street Map, and evaluating their association with motorized traffic flows, Surface Urban Heat Island (SUHI) intensities, tropospheric nitrogen dioxide (NO 2 ), CO 2 per capita emissions and age-standardized mortality. We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal–Wallis test followed by a post-hoc Dunn's test. We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low-density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low-density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO 2 exposure were significantly higher in compact high density and open low rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low-density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO 2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types. Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Patchy nuclear chain reactions

Stochastic fluctuations of the neutron population within a nuclear reactor are typically prevented by operating the core at a sufficient power, since a deterministic (i.e., exactly predictable) behavior of the neutron population is required by automatic safety systems to detect unwanted power excursions. In order to characterize the reactor operating conditions at which the fluctuations vanish, an experiment was designed and took place in 2017 at the Rensselaer Polytechnic Institute Reactor Critical Facility. This experiment however revealed persisting fluctuations and striking patchy spatial patterns in neutron spatial distributions. Here we report these experimental findings, interpret them by a stochastic modeling based on branching random walks, and extend them using a “numerical twin” of the reactor core. Consequences on nuclear safety will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗
DOE OSTI2023

Exploration of non-resonant divertor features on the Compact Toroidal Hybrid

Abstract Non-resonant divertors (NRDs) separate the confined plasma from the surrounding plasma facing components (PFCs). The resulting striking field line intersection pattern on these PFCs is insensitive to plasma equilibrium effects. However, a complex scrape-off layer (SOL), created by chaotic magnetic topology in the plasma edge, connects the core plasma to the PFCs through varying magnetic flux tubes. The Compact Toroidal Hybrid (CTH) serves as a test-bed to study this by scanning across its inductive current. Simulations observe a significant change of the chaotic edge structure and an effective distance between the confined plasma and the instrumented wall targets. The intersection pattern is observed to be a narrow helical band, which we claim is a resilient strike line pattern. However, signatures of finger-like structures, defined as heteroclinic tangles in chaotic domains, within the plasma edge connect the island chains to this resilient pattern. The dominant connection length field lines intersecting the targets are observed via heat flux modeling with EMC3-EIRENE. At low inductive current levels, the excursion of the field lines resembles a limited plasma wall scenario. At high currents, a private flux region is created in the area where the helical strike line pattern splits into two bands. These bands are divertor legs with distinct SOL parallel particle flow channels. The results demonstrate the NRD strike line pattern resiliency within CTH, but also show the underlying chaotic edge structure determining if the configuration is diverted or limited. This work supports future design efforts for a mechanical structure for the NRD.

Physics↗
DOE OSTI2024

MetaPoL: Immersive VR based Indoor Patterns of Life (PoL) and Anomalies Data Generation for Insider Threat Modeling in Nuclear Security

Insider threats are perhaps the most serious challenges that nuclear and radiological security systems face. Insiders pose such a great threat due to their access, authority, and knowledge, granting them opportunities to bypass dedicated nuclear and radiological security elements. For example, in one of the latest major insider threat incidents to nuclear security, the Doel-4 nuclear powerplant in Belgium suffered a shutdown, the threat of nuclear materials diversion, and long-term loss of tens of millions of dollars. Seven years of investigation concluded that it was an inside job and attempted sabotage. In this regard, there is an immediate need for R&D and technology integration in the domain of modeling indoor Patterns-of-Life (PoL) and anomaly detection. This can be achieved by using datasets of facility users’ mobility and activity, which can support the design of algorithms for insider threat modeling and detection. However, due to classification, privacy, sensitivity, and safety protocols, such datasets from real physical nuclear reactor facilities are not only hard to share, but also not always feasible to deploy and collect. Aiming to find an alternate solution, our proposed demonstration work - MetaPoL, is the first-ever (for the application space) immersive VR (virtual reality) environment of a real-world secure facility and allows users to move-and-stay through the designed indoor physical layout and also encounter NPCs (non-player characters) that emulate other facility users. In the MetaPoL an interactive user performs realistic spatio-temporal movement, dwelling and activities using a Meta Quest Pro VR headset, and that generates high-frequency (in time) high-resolution (in space) indoor spatial-temporal datasets that are valuable for PoL modeling and anomaly detection research specifically for insider threat modeling and detection mission. Such generated realistic, rich in context, and mission specific datasets can boost AI/Machine Learning based research for modeling and detecting insider threats in nuclear security and nonproliferation.

Gunaratne, Chathika↗
DOE OSTI2020

Shaped Beams from Diamond Field-Emitter Array Cathodes

Diamond field-emitter arrays (DFEAs) are arrays of diamond pyramids with exquisitely sharp tips and micrometer-scale bases that produce high current densities. These arrays can be fabricated in arbitrary shapes, ranging from single tips to many millions of tips, so that they produce an inherently shaped electron beam. Each tip emits a modest current, but the large dense array can produce many Amps. We are investigating these cathodes for use in dielectric wakefield accelerators; however, they may also be applicable to vacuum microwave tubes. Recently, shaped beam production and transport have been demonstrated in the 1.3-GHz RF gun at the Argonne Cathode Test Stand at the Argonne National Laboratory. The charge was measured on a Faraday cup and the beam imaged on a YAG screen with peak electric field gradients on the cathode ranging from 12 to 35 MV/m. Three cathode geometries were tested: one 1-mm equilateral triangle with 7-mu m base pyramids and 10-mu m pitch, one 1-mm equilateral triangle with 10-mu m base and 25-mu m pitch, and one sparse 5 x 5 square array with 20-mu m base and 400-mu m pitch. The two triangular arrays emitted 35 nC in an RF macropulse at 35 MV/m and 13-nC charge at 27 MV/m, respectively, while the sparse array emitted 0.060-nC charge at 15 MV/m. This article presents the results of the triangular array experiments, including damage due to breakdown in the RF gun and initial models of tip-to-tip shielding.

Cathodes↗
DOE OSTI2024

Self-aligned fabrication of vertical, fin-based structures

Modern power devices rely on complex, three-dimensional, vertical designs to increase their power density, ease their thermal management, and improve their reliability. However, fabrication techniques have historically relied on 2D processes for patterning lateral features. This work presents a new technology that uses multiple steps of angled depositions to fabricate self-aligned vertical, fin-based devices that avoid fundamental lithography resolution and alignment limitations. The fabrication flows of two devices, the self-aligned vertical finFET and the high-κ dielectric fin diode, are presented to demonstrate how angled depositions can readily achieve transistors with submicrometer, vertical gates in a source-first process and also create high-aspect ratio GaN fins with a record 70:1 aspect ratio.

Perozek, Joshua (ORCID:0000000251519131)↗
DOE OSTI2020

Scalable Asynchronous Domain Decomposition Solvers

We discuss how parallel implementations of linear iterative solvers generally alternate between phases of data exchange and phases of local computation. Increasingly large problem sizes and more heterogeneous compute architectures make load balancing and the design of low latency network interconnects that are able to satisfy the communication requirements of linear solvers very challenging tasks. In particular, global communication patterns such as inner products become increasingly limiting at scale. We explore the use of asynchronous communication based on one-sided Message Passing Interface primitives in the context of domain decomposition solvers. In particular, a scalable asynchronous two-level Schwarz method is presented. We discuss practical issues encountered in the development of a scalable solver and show experimental results obtained on a state-of-the-art supercomputer system that illustrate the benefits of asynchronous solvers in load balanced as well as load imbalanced scenarios. Using the novel method, we can observe speedups of up to four times over its classical synchronous equivalent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Closing the Gap Between Modeling and Experiments in the Self-assembly of Biomolecules at Interfaces and in Solution

Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design

Sampath, Janani↗
DOE OSTI2021

Self-organization of nanoparticles and molecules in periodic Liesegang-type structures

Chemical organization in reaction-diffusion systems offers a strategy for the generation of materials with ordered morphologies and structural hierarchy. Periodic structures are formed by either molecules or nanoparticles. On the premise of new directing factors and materials, an emerging frontier is the design of systems in which the precipitation partners are nanoparticles and molecules. We show that solvent evaporation from a suspension of cellulose nanocrystals (CNCs) and L -(+)-tartaric acid [ L -(+)-TA] causes phase separation and precipitation, which, being coupled with a reaction/diffusion, results in rhythmic alternation of CNC-rich and L -(+)-TA–rich rings. The CNC-rich regions have a cholesteric structure, while the L -(+)-TA–rich bands are formed by radially aligned elongated bundles. The moving edge of the pattern propagates with a finite constant velocity, which enables control of periodicity by varying film preparation conditions. This work expands knowledge about self-organizing reaction-diffusion systems and offers a strategy for the design of self-organizing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stabilizing Ni-rich layered cathode for high-voltage operation through hierarchically heterogeneous doping with concentration gradient

High-nickel LiNi x Mn y Co 1-x-y O 2 (NMC) cathodes have demonstrated superior energy density, yet their stability is compromised under high voltage conditions. To address this, here we propose a strategy of heterogeneous doping with a concentration gradient, specifically through Sr–Zr co-modification. We synthesized Ni-rich NMC particles featuring several micron-sized secondary particles composed of micron-sized primary grains. This design aims to harness the structural robustness of single-crystalline grains and the favorable diffusion kinetics of polycrystalline secondary particles. Systematic characterization using a combination of electrochemical measurements and synchrotron analytics reveals an intriguing pattern of hierarchically heterogeneous Sr–Zr co-doping. It demonstrates a depth-dependent concentration gradient at the secondary particle level and competing dopant segregation over the buried grain boundaries. This unique characteristic creates opportunities for enhancing battery performance, particularly by optimizing precursors and implementing advanced modulation techniques. We also investigate the dissolution and precipitation of the cathode's transition metal cations upon high-voltage cycling. These insights suggest that a tailored compositional variation can be a viable approach to effectively design the next-generation high-Ni NMC cathode materials for high-voltage lithium batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Organizing bacterial vortex lattices by periodic obstacle arrays

Recent experiments have shown that the complex spatio-temporal vortex structures emerging in active fluids are susceptible to weak geometrical constraints. This observation poses the fundamental question of how boundary effects stabilize a highly ordered pattern from seemingly turbulent motion. Here we show, by a combination of continuum theory and experiments on a bacterial suspension, how artificial obstacles guide the flow profile and reorganize topological defects, which enables the design of bacterial vortex lattices with tunable properties. To this end, the continuum model is extended by appropriate boundary conditions. Beyond the stabilization of square and hexagonal lattices, we also provide a striking example of a chiral, antiferromagnetic lattice exhibiting a net rotational flow, which is induced by arranging the obstacles in a Kagome-like array. Recent experiments showed that weak geometrical constraints can organize topological defects in turbulent bacterial suspensions. Here, the authors use a continuum model to study the connection between symmetry and stability of emergent vortex patterns and the geometry of constraining pillar arrays.

59 BASIC BIOLOGICAL SCIENCES↗