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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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We present a flow-based system coupled with an optical interferometry setup for the extraction of single nanocrystal properties.
Systematic approach for predicting lignin extraction and studying mechanistic effects using computational chemistry and experimental correlations.
Artificial neural networks are used to extract three Flory-Huggins chi parameters from neutron scattering length density profiles, which paves a way towards automated analysis of neutron reflectivity data.
The synthesis of gold nanorods remains largely heuristically understood. Large language models provide a route for extracting their structured synthesis procedures from scientific articles to accelerate investigation into synthesis pathways.
Host-mediated liquid–liquid extraction is a convenient method for the separation of inorganic salts.
Correction for ‘Ion pair extractant selective for LiCl and LiBr’ by Nam Jung Heo et al. , Chem. Sci. , 2024, 15 , 13958–13965, https://doi.org/10.1039/D4SC03760J.
A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.
The rare earth elements (REEs) play an important role in many modern technologies, particularly those relevant to clean energy. Despite their increasing importance, obtaining them in elementally pure forms suitable for downstream applications is challenging due to their similar chemical properties. This problem has impeded efforts to efficiently and selectively extract them from end-of-life materials and electronic waste. Here, we report a cost-efficient acyclic picolinate-based chelator H 4 aapa. The REE stability constants (log K ML ) of this chelator were measured via pH potentiometric and UV-Visible spectrophotometric titrations, revealing it to preferably bind light over heavy REEs like many recently reported 18-membered macrocycles. Its REE complexes were characterized by X-ray crystallography and NMR spectroscopy, demonstrating that this chelator can attain different conformations. The unique properties of aapa were subsequently used to separate REEs via the dissolution of insoluble REE oxalate mixtures. This dissolution-based separation led to large separation factors, the most significant being that for the Ce 3+ /Lu 3+ pair (38.6) at pH 4. Leveraging the strong REE binding affinity of aapa, we further demonstrated this chelator can leach REEs from authentic end-of-life materials in the form of magnet waste and autocatalyst smelting (autocat) slag. With this approach, exposure of these materials to a 20 mM solution of aapa at neutral pH generates a metal-containing solution enriched in Nd 3+ and Dy 3+ by 56.9 wt% and 3.0 wt%, marking a 4-fold improvement over the use of 4 M HNO 3 .
Thermophotovoltaics have long been of interest as an energy conversion technology but suffer from low power density and low efficiency. Structured emitters designed to alter the emission spectrum and increase the efficiency are not stable at the necessary high emitter temperatures and also reduce the power density. Here, we propose a wavelength-selective thermal extraction device that mitigates these challenges and demonstrate a transfer-printing process needed to fabricate the device. The device consists of a ZnS solid hemisphere with a patterned thin film optical filter that passively increases the far-field radiated flux from an emitter within a wavelength band near the bandgap of a photovoltaic cell. Crucially, the device does not need to be in physical contact with the emitter and thus can be maintained at a lower temperature, circumventing the thermal stability challenge. Our work helps one to address long-standing issues with applications of thermophotovoltaics.
In this work, we derived S-parameter based expressions for the intrinsic quality factor of an arbitrary coupled multiport microwave cavity and a non-ideal test fixture. Practical accuracy limitations of the obtained expressions specifically for superconducting accelerator cavities were evaluated both analytically and with the simulation software. The resulting formulas can be used to extract the intrinsic quality factor of normal-conducting and superconducting cavities directly from the calibrated S-parameter measurements.
Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. Here, we discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.
We report experimental demonstrations of polarization-selection two-dimensional Vibrational-Electronic (2D VE) and 2D Electronic-Vibrational (2D EV) spectroscopies aim to map the magnitudes and spatial orientations of coupled electronic and vibrational coordinates in complex systems. The realization of that goal depends on our ability to connect spectroscopic observables with molecular structural parameters. In this paper, we use a model Hamiltonian consisting of two anharmonically coupled vibrational modes in electronic ground and excited states with linear and bilinear vibronic coupling terms to simulate polarization-selective 2D EV and 2D VE spectra. We discuss the relationships between the linear vibronic coupling and two-dimensional Huang–Rhys parameters and between the bilinear vibronic coupling term and Duschinsky mixing. We develop a description of the vibronic transition dipoles and explore how the Hamiltonian parameters and non-Condon effects impact their amplitudes and orientations. Using simulated polarization-selective 2D EV and 2D VE spectra, we show how 2D peak positions, amplitudes, and anisotropy can be used to measure parameters of the vibronic Hamiltonian and non-Condon effects. This paper, along with the first in the series, provides the reader with a detailed description of reading, simulating, and analyzing multimode, polarization-selective 2D EV and 2D VE spectra with an emphasis on extracting vibronic coupling parameters from complex spectra.
Accurate characterization of the intrinsic contact resistance is important for the optimization of organic thin-film transistors (OTFTs), especially in devices with high gate capacitance density. This study reports on a method for the extraction of the intrinsic, or gate-voltage independent, contact resistance from OTFTs. In contrast to the transfer length method (TLM), this method can be applied to devices with a single geometry. The method is an extension of the previously reported Y-function method and combines the model proposed by S. Luan et al. to analyze the effect of the gate voltage on the contact resistance. Here, this method is first applied to OTFTs with a bottom-gate top-contact geometry having varying gate capacitance density values. The intrinsic contact resistance is found to decrease with increased gate capacitance density with values ranging between 4 kΩcm and under 1 kΩcm when the gate capacitance is changed from 36.6 nF/cm 2 to 231.7 nF/cm 2 , respectively. These values are in good agreement with those obtained using TLM experiments. The new method is also shown to be applicable to OTFTs with top-gate bottom contact geometry.
We apply Frenkel exciton theory to model the entire Q-band of a tightly bound chlorophyll dimer inspired by the photosynthetic reaction center of photosystem II. The potential of broadband two-dimensional electronic spectroscopy experiment spanning the Qx and Qy regions to extract the parameters of the model dimer Hamiltonian is examined through theoretical simulations of the experiment. We find that the local nature of Qx excitation enables identification of molecular properties of the delocalized Qy excitons. Specifically, we demonstrate that the cross-peak region, where excitation energy is resonant with Qy while detection is at Qx, contains specific spectral signatures that can reveal the full real-space molecular Hamiltonian, a task that is impossible by considering the Qy transitions alone. System–bath coupling and site energy disorder in realistic systems may limit the resolution of these spectral signatures due to spectral congestion.
Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of nanoscale systems. However, inferring optical constants from the measured near-field signal can be challenging because of a complicated and highly nonlinear interaction between the scanned probe and the sample. Conventional fitting methods applied to this problem often suffer from the lack of convergence or require human intervention. Here, we develop an alternative approach where the optical parameter extraction is automated by a deep learning network. The network provides an initial estimate that is subsequently refined by a traditional fitting algorithm. In conclusion, we show that this method demonstrates superior accuracy, stability against noise, and computational speed when applied to simulated near-field spectra.
We present an extension of the [Takeda et al., J. Opt. Soc. Am. 72, 156 (1982)] phase extraction method to heterodyne photonic Doppler velocimetry applications. The method yields results equivalent to those obtained by the short-time Fourier transform (STFT), while offering potential improvements in time resolution. Unlike STFT, which relies on window functions, such as the Hamming window, that emphasize central data points and diminish the influence of edges, the extended Takeda method utilizes all data uniformly. This uniform treatment allows for the derivation of empirical equations that directly relate velocity error to the actual time resolution rather than to the local analysis duration. The established equation provides a useful metric for both optimizing hardware configuration and guiding data analysis. Simulation and experimental results confirm that, for a given dataset, specifying a target time resolution yields consistent velocity errors for both methods. These findings underscore the Takeda method’s advantages, particularly its potential higher time resolution and reduced computational burden, making it a valuable tool for high-throughput applications such as laser dynamic compression experiments.
Hot carrier-based energy conversion systems could double the efficiency of conventional solar energy technology or drive photochemical reactions that would not be possible using fully thermalized, “cool” carriers, but current strategies require expensive multijunction architectures. Using an unprecedented combination of photoelectrochemical and in situ transient absorption spectroscopy measurements, we demonstrate ultrafast (<50 fs) hot exciton and free carrier extraction under applied bias in a proof-of-concept photoelectrochemical solar cell made from earth-abundant and potentially inexpensive monolayer (ML) MoS 2 . Our approach facilitates ultrathin 7 Å charge transport distances over 1 cm 2 areas by intimately coupling ML-MoS 2 to an electron-selective solid contact and a hole-selective electrolyte contact. Our theoretical investigations of the spatial distribution of exciton states suggest greater electronic coupling between hot exciton states located on peripheral S atoms and neighboring contacts likely facilitates ultrafast charge transfer. Our work delineates future two-dimensional (2D) semiconductor design strategies for practical implementation in ultrathin photovoltaic and solar fuel applications.
Despite much effort, antibody therapies for Alzheimer’s disease (AD) have shown limited efficacy. Challenges to the rational design of effective antibodies include the difficulty of achieving specific affinity to critical targets, poor expression, and antibody aggregation caused by buried charges and unstructured loops. To overcome these challenges, we grafted previously determined sequences of fibril-capping amyloid inhibitors onto a camel heavy chain antibody scaffold. These sequences were designed to cap fibrils of tau, known to form the neurofibrillary tangles of AD, thereby preventing fibril elongation. The nanobodies grafted with capping inhibitors blocked tau aggregation in biosensor cells seeded with postmortem brain extracts from AD and progressive supranuclear palsy (PSP) patients. The tau capping nanobody inhibitors also blocked seeding by recombinant tau oligomers. Another challenge to the design of effective antibodies is their poor blood–brain barrier (BBB) penetration. In this study, we also designed a bispecific nanobody composed of a nanobody that targets a receptor on the BBB and a tau capping nanobody inhibitor, conjoined by a flexible linker. We provide evidence that the bispecific nanobody improved BBB penetration over the tau capping inhibitor alone after intravenous administration in mice. Our results suggest that the design of synthetic antibodies that target sequences that drive protein aggregation may be a promising approach to inhibit the prion-like seeding of tau and other proteins involved in AD and related proteinopathies.