Continuous Variation of Lattice Dimensions and Pore Sizes in Metal–Organic Frameworks
Not provided.
membrane, carbon capture, materials and chemistry ↗
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Results for “Synthesis Design”
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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Combinatorial Studies of Palladium-Based Oxygen Reduction Electrocatalysts for Alkaline Fuel Cells
Not provided.
catalysis (heterogeneous), electrocatalysis, hydro↗
Two-photon excited deep-red and near-infrared emissive organic co-crystals
Abstract Two-photon excited near-infrared fluorescence materials have garnered considerable attention because of their superior optical penetration, higher spatial resolution, and lower optical scattering compared with other optical materials. Herein, a convenient and efficient supramolecular approach is used to synthesize a two-photon excited near-infrared emissive co-crystalline material. A naphthalenediimide-based triangular macrocycle and coronene form selectively two co-crystals. The triangle-shaped co-crystal emits deep-red fluorescence, while the quadrangle-shaped co-crystal displays deep-red and near-infrared emission centered on 668 nm, which represents a 162 nm red-shift compared with its precursors. Benefiting from intermolecular charge transfer interactions, the two co-crystals possess higher calculated two-photon absorption cross-sections than those of their individual constituents. Their two-photon absorption bands reach into the NIR-II region of the electromagnetic spectrum. The quadrangle-shaped co-crystal constitutes a unique material that exhibits two-photon absorption and near-infrared emission simultaneously. This co-crystallization strategy holds considerable promise for the future design and synthesis of more advanced optical materials.
36 MATERIALS SCIENCE↗
A contorted nanographene shelter
Nanographenes have kindled considerable interest in the fields of materials science and supramolecular chemistry as a result of their unique self-assembling and optoelectronic properties. Encapsulating the contorted nanographenes inside artificial receptors, however, remains challenging. Herein, we report the design and synthesis of a trigonal prismatic hexacationic cage, which has a large cavity and adopts a relatively flexible conformation. It serves as a receptor, not only for planar coronene, but also for contorted nanographene derivatives with diameters of approximately 15 Å and thicknesses of 7 Å. A comprehensive investigation of the host-guest interactions in the solid, solution and gaseous states by experimentation and theoretical calculations reveals collectively an induced-fit binding mechanism with high binding affinities between the cage and the nanographenes. Notably, the photostability of the nanographenes is improved significantly by the ultrafast deactivation of their excited states within the cage. Encapsulating the contorted nanographenes inside the cage provides a noncovalent strategy for regulating their photoreactivity.
36 MATERIALS SCIENCE↗
Additive manufacturing of strong silica sand structures enabled by polyethyleneimine binder
Abstract Binder Jet Additive Manufacturing (BJAM) is a versatile AM technique that can form parts from a variety of powdered materials including metals, ceramics, and polymers. BJAM utilizes inkjet printing to selectively bind these powder particles together to form complex geometries. Adoption of BJAM has been limited due to its inability to form strong green parts using conventional binders. We report the discovery of a versatile polyethyleneimine (PEI) binder for silica sand that doubled the flexural strength of parts to 6.28 MPa compared with that of the conventional binder, making it stronger than unreinforced concrete (~4.5 MPa) in flexural loading. Furthermore, we demonstrate that PEI in the printed parts can be reacted with ethyl cyanoacrylate through a secondary infiltration, resulting in an increase in flexural strength to 52.7 MPa. The strong printed parts coupled with the ability for sacrificial washout presents potential to revolutionize AM in various applications including construction and tooling.
36 MATERIALS SCIENCE↗
Solvent-driven fractional crystallization for atom-efficient separation of metal salts from permanent magnet leachates
Abstract This work reports a dimethyl ether-driven fractional crystallization process for separating rare earth elements and transition metals. The process has been successfully applied in the treatment of rare earth element-bearing permanent magnet leachates as an atom-efficient, reagent-free separation method. Using ~5 bar pressure, the solvent was dissolved into the aqueous system to displace the contained metal salts as solid precipitates. Treatments at distinct temperatures ranging from 20–31 °C enable crystallization of either lanthanide-rich or transition metal-rich products, with single-stage solute recovery of up to 95.9% and a separation factor as high as 704. Separation factors increase with solution purity, suggesting feasibility for eco-friendly solution treatments in series and parallel to purify aqueous material streams. Staged treatments are demonstrated as capable of further improving the separation factor and purity of crystallized products. Upon completion of a crystallization, the solvent can be recovered with high efficiency at ambient pressure. This separation process involves low energy and reagent requirements and does not contribute to waste generation.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Thermodynamics-guided alloy and process design for additive manufacturing
In conventional processing, metals go through multiple manufacturing steps including casting, plastic deformation, and heat treatment to achieve the desired property. In additive manufacturing (AM) the same target must be reached in one fabrication process, involving solidification and cyclic remelting. The thermodynamic and kinetic differences between the solid and liquid phases lead to constitutional undercooling, local variations in the solidification interval, and unexpected precipitation of secondary phases. These features may cause many undesired defects, one of which is the so-called hot cracking. The response of the thermodynamic and kinetic nature of these phenomena to high cooling rates provides access to the knowledge-based and tailored design of alloys for AM. Here, we illustrate such an approach by solving the hot cracking problem, using the commercially important IN738LC superalloy as a model material. The same approach could also be applied to adapt other hot-cracking susceptible alloy systems for AM.
36 MATERIALS SCIENCE↗
Observation of formation and local structures of metal-organic layers via complementary electron microscopy techniques
Abstract Metal-organic layers (MOLs) are highly attractive for application in catalysis, separation, sensing and biomedicine, owing to their tunable framework structure. However, it is challenging to obtain comprehensive information about the formation and local structures of MOLs using standard electron microscopy methods due to serious damage under electron beam irradiation. Here, we investigate the growth processes and local structures of MOLs utilizing a combination of liquid-phase transmission electron microscopy, cryogenic electron microscopy and electron ptychography. Our results show a multistep formation process, where precursor clusters first form in solution, then they are complexed with ligands to form non-crystalline solids, followed by the arrangement of the cluster-ligand complex into crystalline sheets, with additional possible growth by the addition of clusters to surface edges. Moreover, high-resolution imaging allows us to identify missing clusters, dislocations, loop and flat surface terminations and ligand connectors in the MOLs. Our observations provide insights into controllable MOL crystal morphology, defect engineering, and surface modification, thus assisting novel MOL design and synthesis.
36 MATERIALS SCIENCE↗
Accurate additive manufacturing of lightweight and elastic carbons using plastic precursors
Despite groundbreaking advances in the additive manufacturing of polymers, metals, and ceramics, scaled and accurate production of structured carbons remains largely underdeveloped. This work reports a simple method to produce complex carbon materials with very low dimensional shrinkage from printed to carbonized state (less than 4%), using commercially available polypropylene precursors and a fused filament fabrication-based process. The control of macrostructural retention is enabled by the inclusion of fiber fillers regardless of the crosslinking degree of the polypropylene matrix, providing a significant advantage to directly control the density, porosity, and mechanical properties of 3D printed carbons. Using the same printed plastic precursors, different mechanical responses of derived carbons can be obtained, notably from stiff to highly compressible. This report harnesses the power of additive manufacturing for producing carbons with accurately controlled structure and properties, while enabling great opportunities for various applications.
36 MATERIALS SCIENCE↗
Grayscale projection two-photon lithography using sub-diffraction motifs for ultrafast and precise nanoscale 3D printing
Rapid and high-fidelity nanoscale 3D printing is highly desirable, but it is difficult due to the tradeoff between speed and accuracy. Although optical projection techniques can massively scale up printing, fidelity is compromised due to the difficulty in precisely controlling the light dosage over the entire field. This challenge is typically addressed by using multiple projections, but it slows down printing. Here, we present grayscale projection two-photon lithography to overcome this tradeoff. Despite using a binary mask, it enables projecting more than 15,000 focal spots, each with independently tunable intensity. It advantageously leverages constraints imposed by optical diffraction to achieve grayscale tuning over the entire field at once. By directly tuning the focal spot intensities, we demonstrate suppression of proximity effects, compensation of non-uniform illumination, compensation of stitching artefacts, and rapid 3D printing with a single femtosecond pulse per layer. We demonstrate printing of nanowires as thin as 55 nm and achieve rates of 1.7 billion voxels/s and 215 mm 3 /hr.
36 MATERIALS SCIENCE↗
Reliable and explainable machine-learning methods for accelerated material discovery
Abstract Despite ML’s impressive performance in commercial applications, several unique challenges exist when applying ML in materials science applications. In such a context, the contributions of this work are twofold. First, we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material data. Specifically, we show that with imbalanced data, standard methods for assessing quality of ML models break down and lead to misleading conclusions. Furthermore, we find that the model’s own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-purpose explainable and reliable machine-learning framework. Specifically, we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material properties. We also propose a transfer learning technique and show that the performance loss due to models’ simplicity can be overcome by exploiting correlations among different material properties. A new evaluation metric and a trust score to better quantify the confidence in the predictions are also proposed. To improve the interpretability, we add a rationale generator component to our framework which provides both model-level and decision-level explanations. Finally, we demonstrate the versatility of our technique on two applications: (1) predicting properties of crystalline compounds and (2) identifying potentially stable solar cell materials. We also point to some outstanding issues yet to be resolved for a successful application of ML in material science.
97 MATHEMATICS AND COMPUTING↗
A classical equation that accounts for observations of non-Arrhenius and cryogenic grain boundary migration
Observations of microstructural coarsening at cryogenic temperatures, as well as numerous simulations of grain boundary motion that show faster migration at low temperature than at high temperature, have been troubling because they do not follow the expected Arrhenius behavior. This work demonstrates that classical equations, that are not simplified, account for all these oddities and demonstrate that non-Arrhenius behavior can emerge from thermally activated processes. According to this classical model, this occurs when the intrinsic barrier energies of the processes become small, allowing activation at cryogenic temperatures. Additional thermal energy then allows the low energy process to proceed in reverse, so increasing temperature only serves to frustrate the forward motion. This classical form is shown to reconcile and describe a variety of diverse grain boundary migration observations.
36 MATERIALS SCIENCE↗
Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes
Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.
36 MATERIALS SCIENCE↗
Hardware-accelerated inference for real-time gravitational-wave astronomy
The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Overall, our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Regio- and diastereoselective intermolecular [2+2] cycloadditions photocatalysed by quantum dots
Not provided.
catalysis (homogeneous), solar (photovoltaic), bio↗
Formation of two-dimensional transition metal oxide nanosheets with nanoparticles as intermediates
According to the article, two-dimensional (2D) materials have attracted significant interest because of their large surface-to-volume ratios and electron confinement. Compared to common 2D materials such as graphene or metal hydroxides, with their intrinsic layered atomic structures, the formation mechanisms of 2D metal oxides with a rocksalt structure are not well understood. Here, we report the formation process for 2D cobalt oxide and cobalt nickel oxide nanosheets, after analysis by in situ liquid-phase transmission electron microscopy. Our observations reveal that three-dimensional (3D) nanoparticles are initially formed from the molecular precursor solution and then transform into 2D nanosheets. Ab initio calculations show that a small nanocrystal is dominated by positive edge energy, but when it grows to a certain size, the negative surface energy becomes dominant, driving the transformation of the 3D nanocrystal into a 2D structure. Uncovering these growth pathways, including the 3D-to-2D transition, provides opportunities for future material design and synthesis in solution.
Chemistry↗
Kinetic pathways of crystallization at the nanoscale
Not provided.
catalysis (homogeneous), solar (photovoltaic), bio↗
Eliminating dissolution of platinum-based electrocatalysts at the atomic scale
Deployment of proton-exchange membrane fuel cells is limited by the durability of Pt-nanoscale catalysts during cathodic oxygen reduction reactions. Dissolution processes on single crystalline and thin film surfaces are now correlated leading to the design of PtAu catalysts with suppressed dissolution. A remaining challenge for the deployment of proton-exchange membrane fuel cells is the limited durability of platinum (Pt) nanoscale materials that operate at high voltages during the cathodic oxygen reduction reaction. In this work, atomic-scale insight into well-defined single-crystalline, thin-film and nanoscale surfaces exposed Pt dissolution trends that governed the design and synthesis of durable materials. A newly defined metric, intrinsic dissolution, is essential to understanding the correlation between the measured Pt loss, surface structure, size and ratio of Pt nanoparticles in a carbon (C) support. It was found that the utilization of a gold (Au) underlayer promotes ordering of Pt surface atoms towards a (111) structure, whereas Au on the surface selectively protects low-coordinated Pt sites. Finally, this mitigation strategy was applied towards 3 nm Pt 3 Au/C nanoparticles and resulted in the elimination of Pt dissolution in the liquid electrolyte, which included a 30-fold durability improvement versus 3 nm Pt/C over an extended potential range up to 1.2 V.
36 MATERIALS SCIENCE↗