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Results for “Flexible Hybrid Electronics”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Light-Induced Decoupling of Electronic and Magnetic Properties in Manganites

The strongly correlated material La 0.7 ⁢Sr 0.3 ⁢Mn⁢O 3 (LSMO) exhibits metal-to-insulator and magnetic transition near room temperature. Although the physical properties of LSMO can be manipulated by strain, chemical doping, temperature, or magnetic field, they often require large external stimuli. Further, to include additional flexibility and tunability, we developed a hybrid optoelectronic heterostructure that uses photocarrier injection from cadmium sulfide (Cd⁢ S) to an LSMO layer to change its electrical conductivity. LSMO exhibits no significant optical response; however, the Cd ⁢S/LSMO heterostructures show an enhanced conductivity, with a resistance drop of about 37%, at the transition temperature under light stimuli. This enhanced conductivity in response to light is comparable to the effect of a 9 T magnetic field in pure LSMO. Surprisingly, the optical and magnetic responses of Cd⁢ S/LSMO heterostructures are decoupled and exhibit different effects when both stimuli are applied. This unexpected behavior shows that heterostructuring strongly correlated oxides may require a new understanding of the coupling of physical properties across the transitions and provide the means to implement new functionalities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Codebase release r0.3 for SmoQyDQMC.jl

We introduce the SmoQyDQMC.jl package, a Julia implementation of the determinant quantum Monte Carlo algorithm. SmoQyDQMC.jl supports generalized tight-binding Hamiltonians with on-site Hubbard and generalized electron-phonon ( e e -ph) interactions, including non-linear e e -ph coupling and anharmonic lattice potentials. Our implementation uses hybrid Monte Carlo methods with exact forces for sampling the phonon fields, enabling efficient simulation of low-energy phonon branches, including acoustic phonons. The SmoQyDQMC.jl package also uses a flexible scripting interface, allowing users to adapt it to different workflows and interface with other software packages in the Julia ecosystem. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDQMC.jl or installed using the Julia package manager. The online documentation, including examples, can be obtained from our document page at https://smoqysuite.github.io/SmoQyDQMC.jl/stable/.

Cohen-Stead, Benjamin (ORCID:0000000279156280)↗
DOE OSTI2021

Nature of Oxygen Adsorption on Defective Carbonaceous Materials

Here, plane-wave density functional theory has been used to study oxygen adsorption on graphene, graphite, and (12,0) zigzag single-walled carbon nanotubes with and without Stone–Wales (SW) and single-vacancy (SV) defects to understand the role of defects on carbonaceous material reactivity. Atomic oxygen adsorption leads to the formation of an epoxide on defect-free graphene and graphite and an ether on the exterior wall of carbon nanotubes and SW-defected materials. O 2 chemisorption is endothermic on defect-free graphene and graphite and slightly exothermic on defect-free nanotubes. O 2 chemisorption energies are predicted to be –1.1 to –1.4 eV on an SW defect and –6.0 to –8.0 eV on an SV defect. An SW defect lowers the energy barriers by 0.90 and 0.50 eV for O 2 chemisorption on graphene and nanotubes, respectively. The formation of a C–O–O–C group is important for O 2 dissociation on defect-free and SW-defected materials. The energy barrier is less than 0.30 eV on an SV defect. The more reactive SW defect toward O adsorption on graphene is mostly due to the strained defective carbon atoms being able to donate more electrons to an O to form an ether. The larger 2s character in the hybrid orbitals in an ether than in an epoxide makes the ether C–O bond stronger. Stronger C–O binding on an SW-defective carbon nanotube than on a defect-free nanotube is in part due to more flexibility of the defect to release the epoxide ring strain to form an ether.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Hybrid Modeling for Complex Systems Health Management

The research work presents application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. The powertrain model consists of Li-ion batteries, electronic speed controller with pulse-width modulation, and brush-less DC motor with connected propeller. Results obtained from combination of laboratory and simulation tests are discussed in this work.

PINNS↗
NASA NTRS2004

Ultrasensitive Label-free Electronic Chip for DNA Analysis Using Carbon Nanotube Nanoelectrode Arrays

There is a strong need for faster, cheaper, and simpler methods for nucleic acid analysis in today s clinical tests. Nanotechnologies can potentially provide solutions to these requirements by integrating nanomaterials with biofunctionalities. Dramatic improvement in the sensitivity and multiplexing can be achieved through the high-degree miniaturization. Here, we present our study in the development of an ultrasensitive label-free electronic chip for DNA/RNA analysis based on carbon nanotube nanoelectrode arrays. A reliable nanoelectrode array based on vertically aligned multi-walled carbon nanotubes (MWNTs) embedded in a SiO2 matrix is fabricated using a bottom-up approach. Characteristic nanoelectrode behavior is observed with a low-density MWNT nanoelectrode array in measuring both the bulk and surface immobilized redox species. The open-end of MWNTs are found to present similar properties as graphite edge-plane electrodes, with a wide potential window, flexible chemical functionalities, and good biocompatibility. A BRCA1 related oligonucleotide probe with 18 bases is covalently functionalized at the open ends of the MWNTs and specifically hybridized with an oligonucleotide target as well as a PCR amplicon. The guanine bases in the target molecules are employed as the signal moieties for the electrochemical measurements. Ru(bpy)3(2+) mediator is used to further amplify the guanine oxidation signal. This technique has been employed for direct electrochemical detection of label-free PCR amplicon through specific hybridization with the BRCAl probe. The detection limit is estimated to be less than approximately 1000 DNA molecules, approaching the limit of the sensitivity by laser-based fluorescence techniques in DNA microarray. This system provides a general electronic platform for rapid molecular diagnostics in applications requiring ultrahigh sensitivity, high-degree of miniaturization, simple sample preparation, and low- cost operation.

Li, Jun↗
DOE OSTI2021

Effect of heterostructure engineering on electronic structure and transport properties of two-dimensional halide perovskites

Organic-inorganic halide perovskite solar cells have attracted much attention due to their low-cost fabrication, flexibility, and high-power conversion efficiency. More recent efforts show that the reduction from three- to two-dimensions (2D) of organic–inorganic halide perovskites promises an exciting opportunity to tune their electronic properties. Here, we explore the effect of reduced dimensionality and heterostructure engineering on the intrinsic material properties, such as energy stability, bandgap and transport properties of 2D hybrid organic–inorganic halide perovskites using first-principles density functional theory. We show that the energy stability of engineered perovskite heterostructures is significantly enhanced. The heterostructures with improved stability also show excellent transport properties similar to their bulk counterparts. Furthermore, these layered chemistries demonstrate the advantage of a broad range of tunable bandgaps and high-absorption coefficient in the visible spectrum. The proposed 2D heterostructured material holds potential for nano-optoelectronic devices as well as for effective photovoltaics.

36 MATERIALS SCIENCE↗
NASA NTRS1973

The development and demonstration of hybrid programmable attitude control electronics

In the course of extended life attitude control system (ELACS) research sponsored by NASA a hybrid programable attitude control electronics (HYPACE) concept was developed and demonstrated. The wide variety of future planetary missions demanded a new control approach to accommodate the automatic fault tolerance and long the life requirements of such missions. HYPACE provides an adaptable, analog/digital design approach that permits preflight and in-flight accommodation of mission changes, component performance variations, and spacecraft changes, through programing. This enabled broad multimission flexibility of application in a cost effective manner. Previously, flight control computers have not been not flown on planetary missions because of weight and power problems. These problems were resolved in the design of HYPACE. The HYPACE design, which was demonstrated in breadboard form on a single-axis gas-bearing spacecraft simulation, uses a single control channel to perform the attitude control functions sequentially, thus significantly reducing the number of component parts over hard-wired designs.

Smith, L. S.↗
NASA NTRS1995

Performance and operational considerations in the design of vehicle antennas for mobile satellite communications

This paper examines the vehicle antenna requirements for mobile satellite systems. The antenna parameters are discussed in the light of the requirements and the limitations in performance imposed by the physical constraints of antenna and by vehicle geometries. Measurements of diffraction and antenna noise temperature in an operational environment are examined, as well as their effects on system margins. Mechanical versus electronic designs are compared with regards to performance, cost, reliability, and design complexity. Comparisons between open-loop and close-loop tracking systems are made and the effects of bandwidth, sidelobe levels, operational constraints, vehicle angular velocity, and acceleration are discussed. Some consideration is given to the use of hybrid systems employing both open and closed-loop tracking. Changes to antenna/terminal specifications are recommended which will provide greater design flexibility and increase the likelihood of meeting the performance and operational requirements.

Milne, R.↗
DOE OSTI2026

Advancing density functional tight-binding method for large organic molecules through equivariant neural networks

Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett., 2021, 11, 16], we advanced the third-order semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN rep to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN rep , we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body Δ TB potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules—for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.

Medrano Sandonas, Leonardo [Technische Universität↗
DOE OSTI2023

How Accurate Can Crystal Structure Predictions Be for High-Energy Molecular Crystals?

Molecular crystals have shallow potential energy landscapes, with multiple local minima separated by very small differences in total energy. Predicting molecular packing and molecular conformation in the crystal generally requires ab initio methods of high accuracy, especially when polymorphs are involved. We used dispersion-corrected density functional theory (DFT-D) to assess the capabilities of an evolutionary algorithm (EA) for the crystal structure prediction (CSP) of well-known but challenging high-energy molecular crystals (HMX, RDX, CL-20, and FOX-7). While providing the EA with the experimental conformation of the molecule quickly re-discovers the experimental packing, it is more realistic to start instead from a naïve, flat, or neutral initial conformation, which reflects the limited experimental knowledge we generally have in the computational design of molecular crystals. By doing so, and using fully flexible molecules in fully variable unit cells, we show that the experimental structures can be predicted in fewer than 20 generations. Nonetheless, one must be aware that some molecular crystals have naturally hindered evolutions, requiring as many attempts as there are space groups of interest to predict their structures, and some may require the accuracy of all-electron calculations to discriminate between closely ranked structures. To save resources in this computationally demanding process, we showed that a hybrid xTB/DFT-D approach could be considered in a subsequent study to push the limits of CSP beyond 200+ atoms and for cocrystals.

42 ENGINEERING↗
DOE OSTI2025

..delta..-Learning of High-Fidelity Electronic Structure Using Graph Neural Networks with Modified Node-Level Features

In this work, we present a ..delta..-learning approach for predicting the eigenvalues calculated with the hybrid functional HSE06 (..epsilon..nkHSE) for a set of metal and nitrogen doped graphene catalysts (MNCs) from Perdew-Burke-Ernzerhof (PBE) inputs. The model presented here incorporates electronic scalar features along with structural information in a graph neural network (GNN). In particular, the PBE eigenvalues for different bands and k-points and orbital-resolved projectors are combined with the applied potential as node-level features along with structural information within the Atomistic Line Graph Neural Network (ALIGNN) architecture. These features enable flexibility for systems with electrified interfaces, such as in electrocatalysts and achieves mean absolute error (MAE) of less than 0.1 eV. The machine learning model reported here achieves a strong generalization to left-out adsorbates (MAE = 0.074 eV) and leave-one-chemical-space-out (MAE = 0.08 eV) and completely left-out metals (MAE = 0.072 eV), confirming the robustness of the machine learning (ML) model in predicting ..epsilon..nkHSE.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Carbon Nanotube Nanoelectrode Array as an Electronic Chip for Ultrasensitive Label-free DNA Detection

A reliable nanoelectrode array based on vertically aligned multi-walled carbon nanotubes (MWNTs) embedded in SiO2 is used for ultrasensitive DNA detection. Characteristic nanoelectrode behavior is observed using low-density MWNT arrays for measuring both bulk and surface immobilized redox species such as K4Fe(CN)6 and ferrocene derivatives. The open-end of MWNTs are found to present similar properties as graphite edge-plane electrodes with wide potential window, flexible chemical functionalities, and good biocompatibility. BRCA1 related oligonucleotide probes with 18 bp are selectively functionalized at the open ends of the nanotube array and specifically hybridized with oligonucleotide targets incorporated with a polyG tag. The guanine groups are employed as the signal moieties in the electrochemical measurements. R(bpy)(sup 2+, sub 3) mediator is used to further amplify the guanine oxidation signal. The hybridization of sub-attomoles of DNA targets is detected electrochemically by combining the MWNT nanoelectrode array with the R(bpy)(sup 2+, sub 3) amplification mechanism. This technique was employed for direct electrochemical detection of label-free PCR amplicon from a healthy donor through specific hybridization with the BRCA1 probe. The detection limit is estimated to be less than 1000 DNA molecules since abundant guanine bases in the PCR amplicon provides a large signal. This system provides a general platform for rapid molecular diagnostics in applications requiring ultrahigh sensitivity, high-degree of miniaturization, and simple sample preparation, and low-cost operation.

Li, Jun↗
DOE OSTI2025

Uncovering Structure–Conductivity Relationships in Anion Exchange Membranes (AEMs) Using Interpretable Machine Learning

Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well as SOM, which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. Among graph-based approaches, the graph convolutional (GCN) achieved strong predictive performance, while the Hybrid Graph Autoencoder-Regressor Ensemble (HGARE) achieved the highest accuracy. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs.

Naghshnejad, Pegah [Department of Chemical Enginee↗
DOE OSTI2022

Novel self-assembled two-dimensional layered oxide structure incorporated with Au nanoinclusions towards multifunctionalities

Two-dimensional (2D) layered oxides have recently attracted wide attention owing to the strong coupling among charges, spins, lattice, and strain, which allows great flexibility and opportunities in structure designs as well as multifunctionality exploration. In parallel, plasmonic hybrid nanostructures exhibit exotic localized surface plasmon resonance (LSPR) providing a broad range of applications in nanophotonic devices and sensors. A hybrid material platform combining the unique multifunctional 2D layered oxides and plasmonic nanostructures brings optical tuning into the new level. Here, a novel self-assembled Bi 2 MoO 6 (BMO) 2D layered oxide incorporated with plasmonic Au nanoinclusions has been demonstrated via one-step pulsed laser deposition (PLD) technique. Comprehensive microstructural characterizations, including scanning transmission electron microscopy (STEM), differential phase contrast imaging (DPC), and STEM tomography, have demonstrated the high epitaxial quality and particle-in-matrix morphology of the BMO-Au nanocomposite film. DPC-STEM imaging clarifies the magnetic domain structures of BMO matrix. Three different BMO structures including layered supercell (LSC) and superlattices have been revealed which is attributed to the variable strain states throughout the BMO-Au film. Owing to the combination of plasmonic Au and layered structure of BMO, the nanocomposite film exhibits a typical LSPR in visible wavelength region and strong anisotropy in terms of its optical and ferromagnetic properties. This study opens a new avenue for developing novel 2D layered complex oxides incorporated with plasmonic metal or semiconductor phases showing great potential for applications in multifunctional nanoelectronics devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tunable 3D Aerosol Jet Printing of Low‐Power Redox‐Gated Transistors with Multicomponent Inks

Printed hybrid electronics (PHE) offer a promising alternative for microelectronics fabrication, addressing some limitations of traditional subtractive manufacturing. Despite the versatility of PHE, particularly in the customization of printing inks, these devices have not yet matched the performance of silicon-based electronics due to challenges in gating mechanisms and operational stability. However, the potential of low-voltage redox-gating to achieve significant carrier modulations in correlated metal oxides remains unexplored in PHE. This study systematically investigates vanadium dioxide (VO 2 ) nanoparticles and redox inks, linking their organization in solution to their morphology, phase state, and properties in solid films and multilayered structures. Using an aerosol jet printer (AJP), a solid-state VO 2 transistor is fabricated, operating at just 0.4 V gating voltage. The printed VO 2 films demonstrate redox-modulated conductivity and consistent transistor behavior. The solid-state redox gating materials also provide long-term stability, with the device maintaining performance over 6000 cycles without degradation. These results highlight the potential of redox gating to enhance the application of functional nanoparticles in printed hybrid microelectronics, especially for flexible, low-voltage, and energy-efficient devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Assessing the roles of Cu- and Ag-deficient layers in chalcopyrite-based solar cells through first principles calculations

Chalcopyrites are a demonstrated material platform for realizing efficient thin-film photovoltaics, with the most well known Cu(In,Ga)Se 2 (CIGS)-based solar cells exceeding 23%. Several factors, including flexibility in tuning the absorber bandgap, enhanced surface treatments, and the electrically benign nature of common defects are responsible for the existing high performance and future promise in chalcopyrite-based photovoltaic devices. The introduction of Cu-poor phases (also known as ordered-vacancy compounds or OVCs) between the absorber and buffer layers in CIGS solar cells is known to enhance device performance; however, the overall properties and role of OVCs remain poorly understood. Using first principles calculations based on the density functional theory with screened hybrid functionals, here we explore the electronic structure and stability of OVCs and their band offsets with defect-free chalcopyrite layers in Cu- and Ag-based compounds (ABX 2 where A=Cu, Ag; B=In, Ga, Al; and X=S, Se). Using AB 3 X 5 and AB 5 X 8 stoichiometries as model OVC systems, we report on the variation of the bandgap with the A/B ratio and discuss the trends in other Cu- and Ag-based chalcopyrites beyond CuInSe 2 . We find that the valence and conduction bands are lower in energy in OVCs with respect to the parent ABX 2 chalcopyrite owing to a reduced p–d interaction between X and A atoms. We additionally perform device-level simulations to assess the implications of the results, finding that the valence band offsets of OVCs are favorable, while the conduction band offsets of chalcopyrites beyond CuInSe 2 -based absorbers may be detrimental in conventional solar cell device designs.

14 SOLAR ENERGY↗
DOE OSTI2024

Structural determination of a full-length plant cellulose synthase informed by experimental and in silico methods

Three-dimensional structure determination and prediction of proteins with intrinsically disordered regions, unstructured regions, conformational flexibility, and lacking homologous structures are challenging. We previously predicted and refined an in silico structure of a plant cellulose synthase from cotton (GhCESA1), and more recently, cryo-electron microscopy (cryo-EM) has resolved a majority of the lengths of two CESA structures from poplar (PttCESA8) and cotton (GhCESA7). However, 26–30% of these cryo-EM structures remain unresolved, including the N-terminal domain, half of the class-specific region, the gating loop region, and the C-terminal domain. Here, we describe the generation and evaluation of a full-length hybrid GhCESA1 model based on this cryo-EM PttCESA8 structure, with unresolved regions completed using this in silico refined GhCESA1 model. All-atom molecular dynamics simulations and subsequent energy minimizations were performed for the in silico and hybrid GhCESA1 models in a lipid bilayer-water-ion environment, and structural stability, dynamics, energetics, contacts, and quality were evaluated. The unresolved regions were found to be the most dynamic, in agreement with their poor electron density with cryo-EM. The hybrid model exhibited a higher total secondary structure content, more favorable intra-protein and protein-lipid interaction energies, and improved quality metrics. Moreover, hydrogen bonding was revealed to be a primary mechanism for intra-protein and protein-lipid contacts. These results demonstrate that in silico structure prediction and refinement may be useful to augment experimental structure determination, especially for disordered and unstructured regions. Furthermore, this hybrid model can serve as a steppingstone to derive full-length homology models of other CESAs found in more experimentally tractable organisms.

59 BASIC BIOLOGICAL SCIENCES↗