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DOE OSTI2024

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.

58 GEOSCIENCES↗

DOE OSTI2018

GoldenEye: stream-based network packet inspection using GPUs

High-performance packet analysis systems have attracted great interest as tools to deal with security concerns in high-speed networks. Recently, researchers have utilized GPUs to improve packet processing performance. However, most existing work has been targeted at per-packet analysis level. Flow-centric operations have been challenging for GPUs because they require sequential operations and large buffers for flow reassembly. In this work, we present the GoldenEye GPU Packet Processing System (GoldenEye), a deep packet inspection (DPI) system that tracks out-of-order TCP packets and provides streambased signature matching. When a batch of packets arrives, GoldenEye sorts packets into flow-reassembled streams and normalizes retransmission through a GPU-implemented reordering module. For signatures that straddle batch boundaries, GoldenEye couples a small set of metadata with a functionallyequivalent minimal regular expression retrieval algorithm to connect the partial matches. Results show that GoldenEye can reassemble tens of millions of packets/sec and conduct stateful DPI operations on TCP streams at multi-ten Gbit/sec rates.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Simulating large one-dimensional neutral-atom quantum systems

While abstract models of quantum computation assume a closed system of two-level states, practical quantum devices inevitably couple to the environment in some way, creating sources of noise. Understanding the tolerance to noise of specific quantum algorithms run on specific devices is important for determining the feasibility of quantum computing in the current noisy intermediate-scale quantum era. Of particular interest is understanding the noise sensitivity of these devices as more qubits are added to the system. Classical simulations are a useful tool to understand the effects of this noise, but direct classical simulations of open quantum systems are burdened by an exponentially growing cost in the number of qubits and a large local Hilbert space dimension. For onedimensional, shallow circuits, using tensor networks can replace this exponential cost with a linear one and simulate far wider systems than what would normally be available. In this paper, we describe a tensor network simulation of a neutral atom quantum system under the presence of noise, while introducing a purity-preserving truncation technique that compromises between the simplicity of the matrix product state and the positivity of the matrix product density operator. We apply this simulation to a near-optimized iteration of the quantum approximate optimization algorithm on a transverse field Ising model in order to investigate the influence of large system sizes on the performance of the algorithm. We find that while circuits with a large number of qubits fail more often under noise that depletes the qubit population, their outputs on a successful measurement are just as robust under Rydberg atom dissipation or qubit dephasing as smaller systems. However, such circuits might not perform as well under coherent multiqubit errors such as Rydberg atom crosstalk. We also find that the optimized parameters are especially robust to noise, suggesting that a noisier quantum system can be used to find the optimal parameters before switching to a cleaner system for measurements of observables.

Allen, James↗

DOE OSTI2025

Identification and Classification of Fungal GPCR Gene Families

G protein-coupled receptors (GPCRs) are transmembrane proteins crucial for signal transduction in eukaryotes, responding to diverse extracellular signals. Researchers have found and systematically summarized 14 distinct types of GPCRs in fungi but their distribution among numerous fungal species remained largely unexamined. Additionally, three families of mammalian homologs (Rhodopsin, Glutamate, and Frizzled) have been found in previous studies, but they are not included in the systematic classification of fungal GPCRs. Our study establishes a unified classification of 17 GPCR classes in fungi, combining 14 fungal and 3 mammalian previously recognized groups, and classifies 28,294 GPCRs across 1357 fungal species, significantly expanding the scale of GPCRs in fungi and demonstrating their broader distribution. We found that mammalian homologs are notably more prevalent in Early Diverging Fungi (EDF), whereas the previous 14 classes are predominantly found in Ascomycota and Basidiomycota. The most abundant class detected in fungi was Pth11-like GPCRs, exclusively found in Pezizomycotina and involved in fungal pathogenicity. Our analysis suggested that Pezizomycotina ancestor possessed an extensive array of Pth11-like GPCRs, but over time, some species underwent considerable reductions in these GPCRs in conjunction with genome contractions. Utilizing a custom-built convolutional neural network (CNN) for the identification of fungal GPCRs, we identified several putative novel fungal GPCRs. Predicted interactions between these prospective new GPCRs and G-alpha proteins, as simulated by AlphaFold Multimer, provided additional support for their functional relevance. In conclusion, our work defines the first large-scale, unified classification of fungal GPCRs, reveals lineage-specific expansions and contractions, and uncovers previously unrecognized GPCR candidates with potential functional roles in fungal signaling.

G protein-coupled receptors↗

DOE OSTI2022

Coupling of multiscale imaging analysis and computational modeling for understanding thick cathode degradation mechanisms

Here, using a thick NMC811 (LiNi 0.8 Mn 0.1 Co 0.1 O 2 ) electrode as an example, we present a macro- to nanoscale 2D and 3D imaging analysis approach coupled with 4D (space + time) computational modeling to probe its degradation mechanism in a lithium-ion battery cell. Particle cracking increases and contact loss between particles and carbon-binder domain are observed to correlate with the cell degradation. This study unravels that the reaction heterogeneity within the thick cathode caused by the unbalanced electron conduction is the main cause of the battery degradation over cycling. The increased heterogeneity in the system will entail more cathode regions where the degree of active material utilization is uneven, leading to higher probabilities of particle cracking. These findings shed light on the crucial role of the electronic and ionic transportation networks in the performance deterioration of the thick cathode. They also provide guidance for cathode architecture optimization and performance improvement.

25 ENERGY STORAGE↗

DOE OSTI2020

Tailoring a Global Iron Regulon to a Uropathogen

Pathogenicity islands and plasmids bear genes for pathogenesis of various Escherichia coli pathotypes. Although there is a basic understanding of the contribution of these virulence factors to disease, less is known about variation in regulatory networks in determining disease phenotypes. Here, we dissected a regulatory network directed by the conserved iron homeostasis regulator, ferric uptake regulator (Fur), in uropathogenic E. coli (UPEC) strain CFT073. Comparing anaerobic genome-scale Fur DNA binding with Fur-dependent transcript expression and protein levels of the uropathogen to that of commensal E. coli K-12 strain MG1655 showed that the Fur regulon of the core genome is conserved but also includes genes within the pathogenicity/genetic islands. Unexpectedly, regulons indicative of amino acid limitation and the general stress response were also indirectly activated in the uropathogen fur mutant, suggesting that induction of the Fur regulon increases amino acid demand. Using RpoS levels as a proxy, addition of amino acids mitigated the stress. In addition, iron chelation increased RpoS to the same levels as in the fur mutant. The increased amino acid demand of the fur mutant or iron chelated cells was exacerbated by aerobic conditions, which could be partly explained by the O 2 -dependent synthesis of the siderophore aerobactin, encoded by an operon within a pathogenicity island. Taken together, these data suggest that in the iron-poor environment of the urinary tract, amino acid availability could play a role in the proliferation of this uropathogen, particularly if there is sufficient O 2 to produce aerobactin. IMPORTANCE Host iron restriction is a common mechanism for limiting the growth of pathogens. We compared the regulatory network controlled by Fur in uropathogenic E. coli (UPEC) to that of nonpathogenic E. coli K-12 to uncover strategies that pathogenic bacteria use to overcome iron limitation. Although iron homeostasis functions were regulated by Fur in the uropathogen as expected, a surprising finding was the activation of the stringent and general stress responses in the uropathogen fur mutant, which was rescued by amino acid addition. This coordinated global response could be important in controlling growth and survival under nutrient-limiting conditions and during transitions from the nutrient-rich environment of the lower gastrointestinal (GI) tract to the more restrictive environment of the urinary tract. The coupling of the response of iron limitation to increased demand for amino acids could be a critical attribute that sets UPEC apart from other E. coli pathotypes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

The Necessity for Iteration in the Application of Numerical Simulation to EGS: Examples from the EGS Collab Test Bed 1

The United States Department of Energy, Geothermal Technologies Office (GTO) is funding a collaborative investigation of enhanced geothermal systems (EGS) processes at the meso-scale. This study, referred to as the EGS Collab project, is a unique opportunity for scientists and engineers to investigate the creation of fracture networks and circulation of fluids across those networks under in-situ stress conditions. The EGS Collab project is envisioned to comprise three experiments and the site for the first experiment is on the 4850 Level in phyllite of the Precambrian Poorman formation, at the Sanford Underground Research Facility, located at the former Homestake Gold Mine, in Lead, South Dakota. Principal objectives of the project are to develop a number of intermediate-scale field sites and to conduct well-controlled in situ experiments focused on rock fracture behavior and permeability enhancement. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO's flagship EGS research effort. As noted, outcomes from the EGS Collab project experiments will serve as benchmarks for computer code verification, but numerical simulation additionally plays an essential role in designing these meso-scale experiments. This paper reviews specific numerical simulations supporting the design of experiments within Test Bed 1, a volume of phyllite rock under in-situ stress conditions off the western side of the West Access Drift on the 4850 Level, near Governor's Corner. Numerical simulations were executed prior to the start of hydraulic stimulation activities within Test Bed 1 following standard practices, using best estimates of principal stress conditions, thermal conditions, and the rock petrophysical properties, including geomechanical properties. These simulations indicated notching of the borehole would promote the initiation of transverse fractures, seismic magnitudes during the hydraulic stimulation would be below 0.1 magnitude on the Richter scale, a rock thermal conductivity of 5.0 W/m K yielded agreement with kISMET borehole temperature logs, back pressure on the production borehole would increase circulation across the test bed, and the production borehole would serve to halt propagation of the hydraulic fracture to the drift. Pre-existing natural fractures, heterogeneities in the rock properties, monitoring boreholes, and overlooked mine elements have prompted a second look at numerically modeling stimulation, fluid circulation, tracer migration, and thermal breakthrough. Numerical simulation is an invaluable tool for providing insight and understanding to complex physical processes. The success of simulations, however, often depends on including all of the salient features of the system in the founding conceptual model. This paper takes a retrospective look at examples where the conceptual model and simulation was sufficient to provide accurate forecasts and those where elements were missing, necessitating rethinking of the simulation.

15 GEOTHERMAL ENERGY↗

DOE OSTI2019

The Necessity for Iteration in the Application of Numerical Simulation to EGS: Examples from the EGS Collab Test Bed 1

The United States Department of Energy, Geothermal Technologies Office (GTO) is funding a collaborative investigation of enhanced geothermal systems (EGS) processes at the meso-scale. This study, referred to as the EGS Collab project, is a unique opportunity for scientists and engineers to investigate the creation of fracture networks and circulation of fluids across those networks under in-situ stress conditions. The EGS Collab project is envisioned to comprise three experiments and the site for the first experiment is on the 4850 Level in phyllite of the Precambrian Poorman formation, at the Sanford Underground Research Facility, located at the former Homestake Gold Mine, in Lead, South Dakota. Principal objectives of the project are to develop a number of intermediate-scale field sites and to conduct well-controlled in situ experiments focused on rock fracture behavior and permeability enhancement. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO’s flagship EGS research effort. As noted, outcomes from the EGS Collab project experiments will serve as benchmarks for computer code verification, but numerical simulation additionally plays an essential role in designing these meso-scale experiments. This paper reviews specific numerical simulations supporting the design of experiments within Test Bed 1, a volume of phyllite rock under in-situ stress conditions off the western side of the West Access Drift on the 4850 Level, near Governor’s Corner. Numerical simulations were executed prior to the start of hydraulic stimulation activities within Test Bed 1 following standard practices, using best estimates of principal stress conditions, thermal conditions, and the rock petrophysical properties, including geomechanical properties. These simulations indicated notching of the borehole would promote the initiation of transverse fractures, seismic magnitudes during the hydraulic stimulation would be below 0.1 magnitude on the Richter scale, a rock thermal conductivity of 5.0 W/m K yielded agreement with kISMET borehole temperature logs, back pressure on the production borehole would increase circulation across the test bed, and the production borehole would serve to halt propagation of the hydraulic fracture to the drift. Pre-existing natural fractures, heterogeneities in the rock properties, monitoring boreholes, and overlooked mine elements have prompted a second look at numerically modeling stimulation, fluid circulation, tracer migration, and thermal breakthrough. Numerical simulation is an invaluable tool for providing insight and understanding to complex physical processes. The success of simulations, however, often depends on including all of the salient features of the system in the founding conceptual model. This paper takes a retrospective look at examples where the conceptual model and simulation was sufficient to provide accurate forecasts and those where elements were missing, necessitating rethinking of the simulation.

numerical simulation, EGS Collab, enhanced geother↗

DOE OSTI2018

Numerical Simulation Applications in the Design of EGS Collab Experiment 1

The United States Department of Energy, Geothermal Technologies Office (GTO) is funding a collaborative investigation of enhanced geothermal systems (EGS) processes at the meso-scale. This study, referred to as the EGS Collab project, is a unique opportunity for scientists and engineers to investigate the creation of fracture networks and circulation of fluids across those networks under in-situ stress conditions. The EGS Collab project is envisioned to comprise three experiments and the site for the first experiment is on the 4850 Level in phyllite of the Precambrian Poorman formation, at the Sanford Underground Research Facility, located at the former Homestake Gold Mine, in Lead, South Dakota. Principal objectives of the project are to develop a number of intermediate-scale field sites and to conduct well-controlled in situ experiments focused on rock fracture behavior and permeability enhancement. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO’s flagship EGS research effort. As noted, outcomes from the EGS Collab project experiments will serve as benchmarks for computer code verification, but numerical simulation additionally plays an essential role in designing these meso-scale experiments. This paper describes specific numerical simulations supporting the design of Experiment 1, a field test involving hydraulic stimulation of two fractures from notched sections of the injection borehole and fluid circulation between sub-horizontal injection and production boreholes in each fracture individually and collectively, including the circulation of chilled water. Whereas the mine drift allows for accurate and close placement of monitoring instrumentation to the developed fractures, active ventilation in the drift cooled the rock mass within the experimental volume. Numerical simulations were executed to predict seismic events and magnitudes during stimulation, initial fracture orientations for smooth horizontal wellbores, pressure requirements for fracture initiation from notched wellbores, fracture propagation during stimulation between the injection and production boreholes, tracer travel times between the injection and production boreholes, and produced fluid temperatures with chilled water injections, and pressure limits on fluid circulation to avoid fracture growth.

Numerical Simulation, EGS Collab, EGS, Sanford Und↗

DOE OSTI2017

The Role of Numerical Simulation in the Design of Stimulation and Circulation Experiments for the EGS Collab Project

Numerical Simulation, EGS Collab, EGS, Sanford Und↗

DOE OSTI2021

Evaluate the System-Level Impact of Connected and Automated Vehicles Coupled with Shared Mobility: An Agent-based Simulation Approach

With the rapid growth of information and communication technologies, Connected and Automated Vehicles (CAVs) are deemed to be disruptive with the potential to significantly improve overall transportation system efficiency, however, may bring Vehicle Miles Traveled (VMT) increase or other issues. Further, shared mobility systems are another disruptive force that is reshaping our travel patterns. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance and energy efficiency, we develop a mesoscopic simulation-based framework for mobility and energy efficiency evaluation considering the disruptive transportation technologies. Under this framework, we develop novel models for energy intensity and modal activity, and evaluated a variety of energy scenarios for different combinations of CAV applications, various levels of automation, roadway characteristics, and traffic conditions, while also varying different vehicle types and fuel/powertrain technologies. Based applying this modeling suite to a calibrated BEAM simulation network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes.

42 ENGINEERING↗

DOE OSTI2019

Initial Steps in Forming the Electrode–Electrolyte Interface: H 2 O Adsorption and Complex Formation on the Ag(111) Surface from Combining Quantum Mechanics Calculations and Ambient Pressure X-ray Photoelectron Spectroscopy

The interaction of water with metal surfaces is at the heart of electrocatalysis. But there remain enormous uncertainties about the atomistic interactions at the electrode-electrolyte interface (EEI). As the first step toward an understanding of the EEI, we report here the details of the initial steps of H 2 O adsorption and complex formation on a Ag(111) surface, based on coupling quantum mechanics (QM) and ambient-pressure X-ray photoelectron spectroscopy (APXPS) experiments. We find a close and direct comparison between simulation and experiment, validated under various isotherm and isobar conditions. We identify five observable oxygen-containing species whose concentrations depend sensitively on temperature and pressure: chemisorbed O* and OH*, H 2 O* stabilized by hydrogen bond interactions with OH* or O*, and multilayer H 2 O*. Furthermore, we identify the species experimentally by their O 1s core-level shift that we calculate with QM along with the structures and free energies as a function of temperature and pressure. This leads to a chemical reaction network (CRN) that we use to predict the time evolution of their concentrations over a wide range of temperature (298-798 K) and pressure conditions (10-6-1 Torr), which agree well with the populations determined from APXPS. This multistep simulation CRN protocol should be useful for other heterogeneous catalytic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Lunar accelerometer network gravitational observatory (LANGO)

With ground-based interferometers detecting hundreds of gravitational-wave (GW) events, GW astronomy has continued to blossom. U.S. and European scientists are developing plans to construct third-generation ground-based interferometers. ESA has proceeded through the mission formulation phase of space-based interferometer in a lower-frequency band, 10 –4 –0.1 Hz. Despite all these exciting developments, there is still a missing frequency band, 0.1–10 Hz. This mid-frequency band is rich with interesting astrophysical events. Coalescence and merger of intermediate-mass black holes (IMBHs) will occur in this frequency band. Coalescing stellar-mass BHs will pass through this frequency band days before they reach the frequency band of LIGO and Virgo. Detection of such signals would enable a mid-frequency detector to issue an advance notice to the high-frequency GW detectors, as well as to optical, x-ray and γ-ray telescopes. We propose Lunar Accelerometer Network Gravitational Observatory (LANGO) to detect GWs in this frequency band. In the first phase (LANGO 1), we propose to deploy four ambient-temperature (250 K) accelerometers in the tetrahedral or in a square configuration on the hemisphere facing the Earth. After successful operation at 250 K, LANGO would be upgraded to a cryogenic (4 K) version with over two orders of magnitude increased sensitivity (LANGO 2), with coherent rejection of seismic noise implemented. LANGO is a full-tensor detector, capable of determining the source direction and wave polarization. Each test mass (TM) is suspended as a pendulum with resonance frequency ∼ 0.01 Hz, thus is only weakly coupled to the lunar surface horizontally. LANGO is designed to detect the relative motion of globally separated, nearly free, TMs by using the Moon as a large quiet platform. LANGO 1 and 2 accelerometers aim at sensitivities ⩽ 10 –11 m s –2 Hz –1/2 and ⩽ 10 –13 m s –2 Hz –1/2 in the horizontal axes over the frequency band of 1 mHz–10 Hz, which yield GW sensitivities 1.1 x 10 -21 Hz -1/2 and 3.8 x 10 -24 Hz -1/2 at 1 Hz, respectively. The LANGO accelerometers will be 10 3 –10 5 times more sensitive than Apollo seismometers. With such sensitivity, LANGO will also make great contribution to the advancement of lunar geophysics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Long-lived particles and the quiet Sun

The nuclear reaction network within the interior of the Sun is an efficient MeV physics factory and can produce long-lived particles generic to dark sector models. In this work we consider the sensitivity of satellite instruments, primarily the RHESSI spectrometer, that observe the quiet Sun in the MeV regime where backgrounds are low. We find that quiet Sun observations offer a powerful and complementary probe in regions of parameter space, where the long-lived particle decay length is longer than the radius of the Sun and shorter than the distance between the Sun and Earth. We comment on connections to recent model-building work on heavy neutral leptons coupled to neutrinos and high-quality axions from mirror symmetries.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Machine learning prediction of self-diffusion in Lennard-Jones fluids

In this work, different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

Designing the Protocols for Programmable Ammonia Catalysis

Programmable catalysis can provide a more energy-efficient and cost-effective route to enhancing commercial ammonia production, a key process in the advancement of renewable energy technologies and the manufacture of fertilizers and basic chemicals. This work explores the computational discovery of optimal forcing protocols to drive such dynamic catalysis models. By employing matrix-free time-stepper methods, coupled with an optimization approach, that integrates Bayesian optimization with a Bayesian continuation strategy to efficiently discover the periodic steady states of such periodically forced systems, we enable the discovery of complex optimal catalyst strain waveforms, while ensuring robust solver convergence. We demonstrate the flexibility of our approach to discover optimized forcing protocols under varying physical constraints on strain modulation or other catalyst operating parameters. We show that these can have a temporal structure more complex than simple step functions. In order to detect undesirable catalytic loops that may correlate with overall reduced performance, we perform a study using graph-theoretical analysis to investigate the dynamics of catalytic kinetic networks formed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Small footprint optoelectrodes using ring resonators for passive light localization

The combination of electrophysiology and optogenetics enables the exploration of how the brain operates down to a single neuron and its network activity. Neural probes are in vivo invasive devices that integrate sensors and stimulation sites to record and manipulate neuronal activity with high spatiotemporal resolution. State-of-the-art probes are limited by tradeoffs involving their lateral dimension, number of sensors, and ability to access independent stimulation sites. Here, we realize a highly scalable probe that features three-dimensional integration of small-footprint arrays of sensors and nanophotonic circuits to scale the density of sensors per cross-section by one order of magnitude with respect to state-of-the-art devices. For the first time, we overcome the spatial limit of the nanophotonic circuit by coupling only one waveguide to numerous optical ring resonators as passive nanophotonic switches. With this strategy, we achieve accurate on-demand light localization while avoiding spatially demanding bundles of waveguides and demonstrate the feasibility with a proof-of-concept device and its scalability towards high-resolution and low-damage neural optoelectrodes.

42 ENGINEERING↗

DOE OSTI2023

Role of aggregates and microstructure of mixed-ionic–electronic-conductors on charge transport in electrochemical transistors

Synthetic efforts have delivered a library of organic mixed ionic–electronic conductors (OMIECs) with high performance in electrochemical transistors. The most promising materials are redox-active conjugated polymers with hydrophilic side chains that reach high transconductances in aqueous electrolytes due to volumetric electrochemical charging. Current approaches to improve transconductance and device stability focus mostly on materials chemistry including backbone and side chain design. However, other parameters such as the initial microstructure and microstructural rearrangements during electrochemical charging are equally important and are influenced by backbone and side chain chemistry. In this study, we employ a polymer system to investigate the fundamental electrochemical charging mechanisms of OMIECs. We couple in situ electronic charge transport measurements and spectroelectrochemistry with ex situ X-ray scattering electrochemical charging experiments and find that polymer chains planarize during electrochemical charging. Our work shows that the most effective conductivity modulation is related to electrochemical accessibility of well-ordered, interconnected aggregates that host high mobility electronic charge carriers. Electrochemical stress cycling induces microstructural changes, but we find that these aggregates can largely maintain order, providing insights on the structural stability and reversibility of electrochemical charging in these systems. Finally, this work shows the importance of material design for creating OMIECs that undergo structural rearrangements to accommodate ions and electronic charge carriers during which percolating networks are formed for efficient electronic charge transport.

36 MATERIALS SCIENCE↗