Carbon-based artificial SEI layers for aqueous lithium-ion battery anodes
Artificial SEI layers passing lithium ions but blocking water molecules for long-lasting aqueous lithium-ion batteries.
Subramanya, Usha↗
SEARCH · Engineering Papers
Results for “artificial”
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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Preparation and characterization of crosslinked poly(vinylimidazolium) anion exchange membranes for artificial photosynthesis
The dependence of small molecule transport on the water content of ion exchange materials frustrates the development of membranes with both high ionic conductivity and low alcohol permeability for artificial photosynthesis devices.
Carter, Blaine M.↗
Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) – such as metamaterials, photonic crystals, and plasmonics – to achieve some desired scattering properties (e.g., transmission or reflection spectrum). DIMs are deep neural networks (i.e., deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. Furthermore, we publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Enabling stable and high-rate cycling of a Ni-rich layered oxide cathode for lithium-ion batteries by modification with an artificial Li⁺-conducting cathode-electrolyte interphase
Ni-rich LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811) cathodes are investigated to realize high energy density Li ion batteries for long life electric vehicle applications. However, capacity decay and thermal instability due to cathode-electrolyte interfacial degradation remain challenges that require sophisticated surface stabilization methods to address. Here, we propose a strategy, for the first time, to form an artificial Li + -conducting cathode-electrolyte interphase (ALCEI) on the NCM811 cathode surface using a nucleophilic reaction between polysulfides and vinylene carbonate (VC). Furthemore, the as-formed ALCEI layer simultaneously protects the NCM particles from electrolyte corrosion and facilitates Li + ion transport, thus enabling stable and high rate cycling of NCM811. As a result, the ALCEI-modified NCM811 cathode exhibits a high capacity (211.6 mA h g -1 at 0.1C), notable rate capability (134 mA h g -1 at 10C), and superior cycle stability (94.2% over 200 cycles at 1C). These results underscore the use of interfacial engineering in high voltage cathode material development and provide a feasible strategy for stabilizing Ni-rich cathode interfaces in practical Li ion battery applications.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Ultrathin electron and proton-conducting membranes for nanoscale integrated artificial photosystems
Reducing the thickness of separation membranes without compromising their selectivity and robustness is the most effective way of maximizing the areal conductivity. This is especially important for the integration of visible light-driven water oxidation and carbon dioxide (or proton) reduction into a complete artificial photosystem on the shortest possible length scale – the nanoscale – because of the efficiency advantages over macroscale photosystems. In addition to their excellent separation property, ultrathin membranes of 10 nm thickness or less need to exhibit sufficient electrical and proton conductivity in order for the photocatalytic rates to keep up with the photon flux at maximum solar intensity. Furher, two materials, graphene and amorphous silica with embedded molecular wires, have emerged as promising ultrathin membranes for the development of nanoscale integrated solar-fuel systems. Moreover, electrically conducting metal–organic or covalent–organic frameworks can be used to fabricate high surface area-supports that enable the use of molecular catalysts and/or light absorbers at an adequate areal density for nanoscale integration with graphene membranes. Following an overview of the electron and proton conductivity of these ultrathin materials and recent examples of photoelectrocatalytic applications that take advantage of some but not all the properties that constitute a complete functional membrane, the status and future opportunities for complete nanoscale integrated photosystems featuring an ultrathin membrane are discussed.
59 BASIC BIOLOGICAL SCIENCES↗
First Results of an “Artificial Retina” Processor Prototype
We report on the performance of a specialized processor capable of reconstructing charged particle tracks in a realistic LHC silicon tracker detector, at the same speed of the readout and with sub-microsecond latency. The processor is based on an innovative pattern-recognition algorithm, called “artificial retina algorithm”, inspired from the vision system of mammals. A prototype of the processor has been designed, simulated, and implemented on Tel62 boards equipped with high-bandwidth Altera Stratix III FPGA devices. The prototype is the first step towards a real-time track reconstruction device aimed at processing complex events of high-luminosity LHC experiments at 40 MHz crossing rate.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence
Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition algorithms like the Kalman filter, but ML techniques, such as neural networks, graph neural networks (GNNs), and recurrent neural networks (RNNs), offer improved accuracy and scalability. By learning from simulated and real detector data, ML models can identify and classify tracks, predict trajectories, and handle ambiguities caused by overlapping or missing hits. Moreover, ML-based approaches can process data in near-real-time, enhancing the efficiency of experiments at large-scale facilities like the Large Hadron Collider (LHC) and Jefferson Lab (JLAB). As detector technologies and computational resources evolve, ML-driven charged track reconstruction continues to push the boundaries of precision and discovery in nuclear physics. In these proceedings, we highlight advancements in charged track identification leveraging Artificial Intelligence within the CLAS12 detector, achieving a notable enhancement in experimental statistics compared to traditional methods. Additionally, we showcase real-time event reconstruction capabilities, including the inference of charged particle properties, such as momentum, direction, and species identification, at speeds matching data acquisition rates. These innovations enable the extraction of physics observables directly from the experiment in real-time.
Gavalian, Gagik (ORCID:0000000267385457)↗
Real Time implementation of Artificial Intelligence compression algorithm for High-Speed Streaming Readout signals
The new generation of high-energy physics experiments plans to acquire data in streaming mode. With this approach, it is possible to access the information of the whole detector (organized in time slices) for optimal and lossless triggering of data acquisitions. With this approach, data rates, especially in large detectors, are often very high, and the network is likely to be the bottleneck for the entire Streaming Read Out system. The aim of this work is to study the implementation of a lossy compression algorithm based on Artificial Intelligence: an Autoencoder. With Machine Learning it is possible to achieve a high compression ratio and fast inference time with only a small degradation of the signals, almost negligible for the specific application. This work explores different configurations of the Autoencoder and the implementation on different hardware. Different Autoencoder configurations are explored to find the best trade-off between compression ratio and reconstruction loss, both for signals and energy spectrum. Different hardware implementations are also explored to find the best platform to achieve real-time performance for the specific application.
Rossi, Fabio (ORCID:0009000385713885)↗
Water oxidation reaction in natural and artificial photosynthetic systems
Understanding the structure and mechanism of water oxidation catalysts is an essential component for developing artificial photosynthetic devices. In the natural water oxidation catalyst, the geometric and electronic structure of its inorganic core, the Mn{sub 4}CaO{sub 5} cluster, has been studied by spectroscopic and diffraction measurements. In inorganic systems, metal oxides seem to be good candidates for water oxidation catalysts. Understanding the reaction mechanism in both natural and oxide-based catalysts will helpin further developing efficient and robust water oxidation catalysts.
36 MATERIALS SCIENCE↗
Magnetic vortex crystal formation in the antidot complement of square artificial spin ice
We have studied ferromagnetic nickel thin films patterned with square lattices of elongated antidots that are negative analogues of square artificial spin ice. Micromagnetic simulations and direct current magnetic moment measurements reveal in-plane anisotropy of the magnetic hysteresis loops, and the formation of a dense array of magnetic vortices with random polarization and chirality. These multiply-connected antidot arrays could be superior to lattices of disconnected nanodisks for investigations of vortex switching by applied electric current.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Nonstochastic magnetic reversal in artificial quasicrystalline spin ice
We have measured the isothermal DC magnetization of Penrose P2 tilings (P2T) composed of wire segments of permalloy thin film. Micromagnetic simulations reproduce the coercive fields and “knee anomalies” observed in experimental data and show magnetic shape anisotropy constrains segments to be single-domain (Ising spins) at low fields, similar to artificial spin ice (ASI). Mirror symmetry controls the initial reversal of individual segments oriented parallel to the applied field, followed by complex switching of multiple adjacent segments (“avalanches”) of various orientations such that closed magnetization loops (“vortices”) are favored. Ferromagnetic P2T differ from previously studied ASI systems due to their aperiodic translational symmetry and numerous inequivalent pattern vertices, which drive nonstochastic switching of segment polarizations.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Note: Artificial neural networks for the automated analysis of force map data in atomic force microscopy
Force curves recorded with the atomic force microscope on structured samples often show an irregular force versus indentation behavior. An analysis of such curves using standard contact models (e.g., the Sneddon model) would generate inaccurate Young's moduli. A critical inspection of the force curve shape is therefore necessary for estimating the reliability of the generated Young's modulus. We used a trained artificial neural network to automatically recognize curves of “good” and of “bad” quality. This is especially useful for improving the analysis of force maps that consist of a large number of force curves.
42 ENGINEERING↗
Nanoscale topographical replication of graphene architecture by artificial DNA nanostructures
Despite many studies on how geometry can be used to control the electronic properties of graphene, certain limitations to fabrication of designed graphene nanostructures exist. Here, we demonstrate controlled topographical replication of graphene by artificial deoxyribonucleic acid (DNA) nanostructures. Owing to the high degree of geometrical freedom of DNA nanostructures, we controlled the nanoscale topography of graphene. The topography of graphene replicated from DNA nanostructures showed enhanced thermal stability and revealed an interesting negative temperature coefficient of sheet resistivity when underlying DNA nanostructures were denatured at high temperatures.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Communication: Separable potential energy surfaces from multiplicative artificial neural networks
We present a potential energy surface fitting scheme based on multiplicative artificial neural networks. It has the sum of products form required for efficient computation of the dynamics of multidimensional quantum systems with the multi configuration time dependent Hartree method. Moreover, it results in analytic potential energy matrix elements when combined with quantum dynamics methods using Gaussian basis functions, eliminating the need for a local harmonic approximation. Scaling behavior with respect to the complexity of the potential as well as the requested accuracy is discussed.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Towards bio-silicon interfaces: Formation of an ultra-thin self-hydrated artificial membrane composed of dipalmitoylphosphatidylcholine (DPPC) and chitosan deposited in high vacuum from the gas-phase
The recent combination of nanoscale developments with biological molecules for biotechnological research has opened a wide field related to the area of biosensors. In the last years, device manufacturing for medical applications adapted the so-called bottom-up approach, from nanostructures to larger devices. Preparation and characterization of artificial biological membranes is a necessary step for the formation of nano-devices or sensors. In this paper, we describe the formation and characterization of a phospholipid bilayer (dipalmitoylphosphatidylcholine, DPPC) on a mattress of a polysaccharide (Chitosan) that keeps the membrane hydrated. The deposition of Chitosan (∼25 Å) and DPPC (∼60 Å) was performed from the gas phase in high vacuum onto a substrate of Si(100) covered with its native oxide layer. The layer thickness was controlled in situ using Very High Resolution Ellipsometry (VHRE). Raman spectroscopy studies show that neither Chitosan nor DPPC molecules decompose during evaporation. With VHRE and Atomic Force Microscopy we have been able to detect phase transitions in the membrane. The presence of the Chitosan interlayer as a water reservoir is essential for both DPPC bilayer formation and stability, favoring the appearance of phase transitions. Our experiments show that the proposed sample preparation from the gas phase is reproducible and provides a natural environment for the DPPC bilayer. In future work, different Chitosan thicknesses should be studied to achieve a complete and homogeneous interlayer.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Impact of artificial lateral quantum confinement on exciton-spin relaxation in a two-dimensional GaAs electronic system
We demonstrate the effect of artificial lateral quantum confinement on exciton-spin relaxation in a GaAs electronic system. GaAs nanodisks (NDs) were fabricated from a quantum well (QW) by top-down nanotechnology using neutral-beam etching aided by protein-engineered bio-nano-templates. The exciton-spin relaxation time was 1.4 ns due to ND formation, significantly extended compared to 0.44 ns for the original QW, which is attributed to weakening of the hole-state mixing in addition to freezing of the carrier momentum. The temperature dependence of the spin-relaxation time depends on the ND thickness, reflecting the degree of quantum confinement.
36 MATERIALS SCIENCE↗
Note: A simple method to suppress the artificial noise for velocity map imaging spectroscopy
A simple method has been proposed to suppress artificial noise from the counts with respect to the central line (or point) for the reconstructed 3D images with cylindrical symmetry in the velocity-map imaging spectroscopy. A raw 2D projection around the z-axis (usually referred to as central line) for photodetachment, photoionization, or photodissociation experiments is pre-processed via angular tailored method to avoid the signal counts distributed near the central line (or point). Two types of photoelectron velocity-map imaging (O{sup −} and Au{sup −} ⋅ NH{sub 3}) are demonstrated to give rise to the 3D images with significantly reduced central line noise after pre-processing operation. The major advantages of the pre-operation are the ability of suppression of central-line noise to resolve weak structures or vibrational excitation in atoms or molecules near photon threshold.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗