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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Large Photothermal Effect in Sub-40 nm h-BN Nanostructures Patterned Via High-Resolution Ion Beam

The controlled nanoscale patterning of 2D materials is a promising approach for engineering the optoelectronic, thermal, and mechanical properties of these materials to achieve novel functionalities and devices. Herein, high-resolution patterning of hexagonal boron nitride (h-BN) is demonstrated via both helium and neon ion beams and an optimal dosage range for both ions that serve as a baseline for insulating 2D materials is identified. Through this nanofabrication approach, a grating with a 35 nm pitch, individual structure sizes down to 20 nm, and additional nanostructures created by patterning crystal step edges are demonstrated. Raman spectroscopy is used to study the defects induced by the ion beam patterning and is correlated to scanning probe microscopy. Photothermal and scanning near-field optical microscopy measure the resulting near-field absorption and scattering of the nanostructures. These measurements reveal a large photothermal expansion of nanostructured h-BN that is dependent on the height to width aspect ratio of the nanostructures. This effect is attributed to the large anisotropy of the thermal expansion coefficients of h-BN and the nanostructuring implemented. Finally, the photothermal expansion should be present in other van der Waals materials with large anisotropy and can lead to applications such as nanomechanical switches driven by light.

2D materials↗
DOE OSTI2018

Laccase-mediated functionalization of chitosan with 4-hexyloxyphenol enhances antioxidant and hydrophobic properties of copolymer

In this paper, an effective method to functionalize chitosan with 4-hexyloxyphenol (HP) under homogeneous reaction conditions was developed using laccase as the catalyst. The resulting copolymer was characterized for chemical structure, grafted-HP content, surface morphology, thermal stability, antioxidant capacity, hydrophobic properties and tensile strength. Solid-state 13 C NMR spectrum confirmed the incorporation of HP onto chitosan. X-ray diffraction (XRD) showed a decrease in the degree of crystallinity for laccase/HP treated chitosan compared to pure chitosan. The grafted-HP content in laccase/HP-treated chitosan first increased and then declined with increase of the initial HP/chitosan ratio. A heterogeneous surface with spherical particles on the laccase/HP treated chitosan was observed by environmental scanning electron microscopy (ESEM) and scanning probe microscopy (SPM). The laccase/HP treatment of chitosan improved the thermal stability of copolymer. More significantly, the HP functionalized chitosan showed greatly improved ABTS + and DPPH radicals scavenging capacity, compared with pure chitosan. The hydrophobicity property of the HP functionalized chitosan also significantly increased although its tensile strength decreased. Finally, this new type of composite with double functionalities (i.e., antioxidant and hydrophobic) could potentially be used as food packaging materials or coating agents.

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

Nanoscale Three-Dimensional Imaging of Degradation in Composite Si-Containing Anodes

The use of silicon (Si) in next-generation lithium-ion battery (LIB) anodes has the potential to dramatically improve electrochemical performance over current LIB graphite (Gr) anodes, due to silicon’s higher specific capacity.1 However, widespread implementation of Si-containing anodes is inhibited by issues such as significant Si volume expansion during lithiation and an unstable solid-electrolyte interphase (SEI), resulting in unreliable performance and poor cycle life. Currently, composite anodes with both Si and graphite active materials are used to increase capacity and mitigate some of the limitations associated with Si. In composite electrodes with a heterogeneous distribution of components with varying electrical properties (including Si, Gr, conductive carbon additive, and binder), it is important to understand the local distribution of each component to correlate with electrochemical processes, particularly localized degradation and heterogeneous aging, and to optimize performance. To investigate Si-containing composite anodes in the nanoscale, we use scanning spreading resistance microscopy (SSRM), a form of scanning probe microscopy (SPM) that probes local electronic resistivity. By examining the intrinsic electronic resistivity contrast between the anode components, separate phases can be distinguished and understood within the composite structure.2 This work studies the effect of electrochemical cycling in two different electrolytes on component distribution and aging by comparing the electrical and structural evolution of composite Si-graphite electrodes and SEI before and after charge-discharge cycling. 1. W. J. Zhang. A review of the electrochemical performance of alloy anodes for lithium-ion batteries J. Power Sources 196 13–24 (2011). 2. C. Stetson, Z. Huey, A. Downard, Z. Li, B. To, A. Zakutayev, C.-S. Jiang, M. Al-Jassim, D. Finegan, S.-D. Han and S. DeCaluwe: Three-Dimensional Mapping of Resistivity and Microstructure of Composite Electrodes for Lithium-Ion Batteries. ACS Nano Letters Accepted (2020).

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Tunneling Mode of Scanning Electrochemical Microscopy: Probing Electrochemical Processes at Single Nanoparticles

Abstract Electrochemical experiments at individual nanoparticles (NPs) can provide new insights into their structure–activity relationships. By using small nanoelectrodes as tips in a scanning electrochemical microscope (SECM), we recently imaged individual surface‐bound 10–50 nm metal NPs. Herein, we introduce a new mode of SECM operation based on tunneling between the tip and a nanoparticle immobilized on the insulating surface. The obtained current vs. distance curves show the transition from the conventional feedback response to electron tunneling between the tip and the NP at separation distances of less than about 3 nm. In addition to high‐resolution imaging of the NP topography, the tunneling mode enables measurement of the heterogeneous kinetics at a single NP without making an ohmic contact with it. The developed method should be useful for studying the effects of nanoparticle size and geometry on electrocatalytic activity in real‐world applications.

Sun, Tong↗
DOE OSTI2023

Combining variational autoencoders and physical bias for improved microscopy data analysis *

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as electron energy loss spectroscopy or 4D scanning transmission electron microscope, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of variational autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO 3 , and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The customized codes of the required functions and classes to develop phyVAE is available at https://github.com/arpanbiswas52/phy-VAE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Probing phonon softening in ferroelectrics by scanning probe microwave spectroscopy

Microwave measurements have recently been successfully applied to measure ferroelectric materials on the nanoscale, including detection of polarization switching and ferroelectric domain walls. In this study we discuss the question of whether scanning probe microscopy operating at microwave frequency can identify the changes associated with the soft phonon dynamics in a ferroic. The analytical expressions for the electric potential, complex impedance, and dielectric losses are derived and analyzed, since these physical quantities are linked to experimentally measurable properties of the ferroic. As a ferroic we consider virtual or proper ferroelectric with an optic phonon mode that softens at a Curie point. We also consider a decay mechanism linked to the conductance of the ferroic, thus manifesting itself as the dielectric loss in the material. Our key finding is that the influence of the soft phonon dispersion on the surface potential distribution, complex impedance, and dielectric losses are evidently strong in the vicinity (~10–30 K) of the Curie temperature. Additionally, we quantified how the spatial distribution and frequency spectra of the complex impedance and the dielectric losses react on the dynamics of the soft phonons near the Curie point. These results set the stage for characterization of polar phase transitions with nanoscale microwave measurements, providing a complementary approach to well established electromechanical measurements for fundamental understanding of ferroelectric properties as well as their applications in telecommunication and computing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments

Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS 2 ANE (Integrated Novelty Score−Strategic Autonomous Non-Smooth Exploration), to enhance the discovery of novel phenomena in autonomous microscopy experimentation. Our method integrates two key components: (1) a novelty scoring system that evaluates the uniqueness of experimental results and (2) a strategic sampling mechanism that promotes exploration of under-sampled regions even if they appear less promising by conventional criteria. We validate this approach on a preacquired data set with a known ground truth comprising of image−spectral pairs. We further implement the process on autonomous scanning probe microscopy experiments. INS 2 ANE significantly increases the diversity of explored phenomena in comparison to conventional optimization routines, enhancing the likelihood of discovering previously unobserved phenomena. These results demonstrate the potential for autonomous microscopy experiments to enhance the scientific discovery by navigating complex experimental spaces to uncover novel phenomena.

Materials↗
DOE OSTI2022

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Structural variability in Cu(I) and Ag(I) coordination polymers with a flexible dithione ligand: Synthesis, crystal structure, microbiological and theoretical studies

Two new compounds namely [Cu(SCN)(µ-L)]{sub n} (1) and ([Ag (µ{sub 2}-L)](ClO{sub 4})){sub n} (2) have been synthesized at room temperature by one-pot reactions between the 1,1-(1,4-butanediyl)bis(1,3-dihydro-3-methyl-1H-imidazole- 2-thione) (L) and appropriate copper(I) and silver(I) salts. These polymers have been characterized by single crystal X-ray diffraction, XRPD, TGA, elemental analysis, infrared spectroscopy, antibacterial activity and scanning probe microscopy studies. In the crystal structure of 1, copper atoms have a distorted trigonal planar geometry with a CuS{sub 2}N coordination environment. Each of the ligands in the structure of 1 acting as a bidentate S-bridging ligand to form a 1D chain structure. Additionally, the adjacent 1D chains are interconnected by the intermolecular C-H…S interactions to create a 2D network structure. In contrast to 1, in the cationic 3D structure of 2 each of the silver atoms exhibits an AgS{sub 4} tetrahedral geometry with 4-membered Ag{sub 2}S{sub 2} rings. In the structure of 2, the flexible ligand adopts two different conformations; gauche-anti-gauche and anti-anti-anti. The antibacterial studies of these polymers showed that polymer 2 is more potent antibacterial agent than 1. Scanning probe microscopy (SPM) study of the treated bacteria was carried out to investigate the structural changes cause by the interactions between the polymers and target bacteria. Theoretical study of polymer 1 investigated by the DFT calculations indicates that observed transitions at 266 nm and 302 nm in the UV–vis spectrum could be attributed to the π→π* and MLCT transitions, respectively. - Graphical abstract: Two new Cu(I) and Ag(I) coordination polymers have been have been synthesized by one-pot reactions. Copper complex has a 2D non-covalent structure, but silver compound is a 3D coordination compound. These compounds have effective antibacterial activity. - Highlights: • Cu(I) and Ag(I) based coordination polymers have different network structures. • Ag(I) polymer has more antibacterial activity than Cu(I) polymer. • DFT calculations of Cu(I) polymer has been investigated. • Cu(I) and Ag(I) polymers can destroy the structure of chromosomal and plasmid DNA.

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

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accelerated Nano-Optical Imaging through Sparse Sampling

The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, in this study, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (α-RuCl 3 ) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probing Activities of Individual Catalytic Nanoflakes by Tunneling Mode of Scanning Electrochemical Microscopy

The tunneling mode of scanning electrochemical microscopy (SECM) was developed recently and applied to studies of charge-transfer reactions at single metal nanoparticles (NPs). When an SECM tip is brought within the tunneling distance from a conductive NP, the particle begins to act as a part of the nanoelectrode. Herein, we demonstrate the possibility of using carbon nanoelectrodes with a very thin insulating sheath for electrochemical tunneling experiments at flat samples. In this way, electrocatalytic activity, conductivity, and charging properties of and faradaic processes in layered nanomaterials can be characterized by single-nanoflake voltammetry without making direct ohmic contact with them. A broad applicability of tunneling SECM experiments is demonstrated by probing nanomaterials with different size, geometry, and electrocatalytic properties, including metallic/pseudo-metallic (1T/1T') and semiconducting (2H) MoS 2 nanoflakes, N-doped porous carbon catalyst, and MXene nanosheets. Further, the Tafel plots for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) at individual nanoflakes are compared to analogous measurements for an ensemble of flakes attached to the surface of a macroscopic electrode. Moreover, we observed variations in catalytic activities of individual MXene flakes toward HER and OER caused by non-uniform doping.

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

Scanning Electrochemical Microscopy: An Evolving Toolbox for Revealing the Chemistry within Electrochemical Processes

The parallel development of ultramicroelectrodes (UMEs) and groundbreaking scanning probe microscopy techniques in the late 1980s led to the development of the scanning electrochemical microscope. Scanning electrochemical microscopy (SECM) was born from the idea of using a tiny electrode to measure the local electrochemical behavior at operating electrodes. From its foundations, the technique displayed an inherent versatility in measuring sample properties beyond topography. It allowed experimenters to measure and map chemical reactions occurring at diverse interfaces, from inspecting the reversibility of redox mediators at metal electrodes, to detecting the hallmarks of cellular respiration on living plant leaves. Related but distinct electrochemical scanning probe techniques, such as electrochemical atomic force microscopy (EC-AFM), scanning ion conductance microscopy (SICM), and scanning electrochemical cell microscopy (SECCM) have developed in parallel. These techniques have demonstrated exquisite spatial resolution down to the nanoscale regime. However, it is the proposition of this review that SECM remains unmatched at revealing the chemical aspects of electrochemistry. Furthermore, it is our intention to review and demonstrate that the versatile architecture of SECM continues to evolve and address fundamental and emerging challenges in the fields of energy storage and conversion, chemical biology, materials science, and environmental chemistry, among others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Emergence of Rapid Oxygen Surface Exchange Kinetics during in Situ Crystallization of Mixed Conducting Thin Film Oxides

The oxygen surface exchange kinetics of mixed ionic and electronic conducting oxides (MIECs) play a vital role in the efficiency of intermediate-to-high-temperature electrochemical devices. While there is increasing interest in low-temperature preparation of MIEC thin films, the impact of the resultant varied degrees of crystallinity on the surface exchange kinetics has not been widely investigated. Herein, we probe the effect of crystallization on oxygen surface exchange kinetics in situ, by applying an optical transmission relaxation (OTR) approach during annealing of amorphous films. OTR enables contact-free, in situ, and continuous quantification of the oxygen surface exchange coefficient ( k chem ); we previously applied it to Pr x Ce 1– x O 2-δ and SrTi 1– x Fe x O 3-δ thin films. In this work, the OTR approach was successfully extended to other mixed conducting thin film compositions for the first time (i.e., perovskite SrTi 0.65 Co 0.35 O 3-δ and Ruddlesden–Popper Sr 2 Ti 0.65 Fe 0.35 O 4±δ ), as well as to Pr 0.1 Ce 0.9 O 2-δ , enabling quantification of the k chem of their native surfaces and comparison of the behavior of films with different final crystal structures. All thin films were prepared by pulsed laser deposition at 25 or 700–800 °C and subject to subsequent thermal treatments with simultaneous OTR monitoring of k chem . The surface roughness, grain size, and crystallinity were evaluated by scanning probe microscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Fluorite Pr 0.1 Ce 0.9 O 2-δ films grown at 25 °C did not exhibit an increase in k chem after annealing, as they were already crystalline as grown at 25 °C. For all other compositions, OTR enabled in situ observation of both the crystallization process and the emergence of rapid surface exchange kinetics immediately upon crystallization. Perovskite SrTi 0.65 Co 0.35 O 3-δ and Ruddlesden–Popper Sr 2 Ti 0.65 Fe 0.35 O 4±δ thin films grown at 25 °C exhibited at least 1–2 orders of magnitude enhanced k chem after annealing compared with highly crystalline thin films grown at 800 °C, indicating the benefits of in situ crystallization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Reward based optimization of resonance-enhanced piezoresponse spectroscopy

Dynamic spectroscopies in scanning probe microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, electrochemical reactions, and ionic dynamics. However, the practical implementation of these measurements is constrained by the need to balance imaging time and data quality. Signal to noise requirements favor long acquisition times and high frequencies to improve signal fidelity. However, these are limited on the low end by contact resonant frequency and photodiode sensitivity and on the high end by the time needed to acquire high-resolution spectra or the propensity for sample degradation under high field excitation over long times. The interdependence of key parameters such as instrument settings, acquisition times, and sampling rates makes manual tuning labor-intensive and highly dependent on user expertise, often yielding operator-dependent results. These limitations are prominent in techniques like dual amplitude resonance tracking in piezoresponse force microscopy that utilize multiple concurrent feedback loops for topography and resonance frequency tracking. Here, a reward-driven workflow is proposed that automates the tuning process, adapting experimental conditions in real time to optimize data quality. Furthermore, this approach significantly reduces the complexity and time required for manual adjustments and can be extended to other SPM spectroscopic methods, enhancing overall efficiency and reproducibility.

47 OTHER INSTRUMENTATION↗