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Structural features of xylan dictate reactivity and functionalization potential for bio-based materials
Plant-based materials have the potential to replace some petroleum-based products, offering compostability and biodegradability as critical advantages. Xylan-rich biomass sources are gaining recognition due to their abundance and underutilization in current industrial applications. Research of potential xylan applications has been complicated by the complex and heterogeneous structure that varies for different xylan feedstocks. Acylation is a broadly used reaction in functionalization of polysaccharides at an industrial scale. However, the efficiency of this reaction varies with the xylan source. To optimize xylan valorization, a systematic understanding of structure–reactivity relationships is essential. This study explores, characterizes, and compares various xylan feedstocks in the acylation process. Xylan feedstocks were analyzed for their chemical composition, degree of polymerization, branching, solubility, and presence of impurities. These features were correlated with xylan glycotypes’ reactivity toward functionalization with succinic anhydride in an optimized DMSO/KOH condition, achieving carboxyl contents of up to 1.46. We used principal component analysis and hierarchical clustering to identify key structural features of xylan that promote its reactivity. Our findings reveal that xylans with higher xylose content and lower degrees of branching exhibit enhanced reactivity, achieving higher carboxyl content and yields. Structural analyses confirmed successful modification, and light scattering analyses showed dramatic changes in the solution properties. Succinylation improves the solubility and film-forming properties of native xylans. This study shows key structure–reactivity relationships in xylan succinylation, establishing that low branching, high xylose content, and reduced lignin impurity enhance chemical functionalization. The results offer a framework for selecting optimal biomass feedstocks and support future efforts in genetic and synthetic biology to design plants with tunable xylan architectures. These findings advance the hemicellulose valorization for applications in coatings and packaging.
Kinetic response study in chemiresistive gas sensor based on carbon nanotube surface functionalized with substituted phthalocyanines
A kind of hybrid material is prepared by functionalizing multi-wall carbon nanotubes (MWCNTs-COOH) with substituted copper phthalocyanine and the formation of CuPcOC{sub 8}/MWCNTs-COOH hybrid is confirmed by scanning electron microscopy and transmission electron microscopy. The results indicated that on the surface of nanotubes substituted CuPcOC{sub 8} derivatives has been successfully anchored through π-π stacking interaction. The gas sensing application of the fabricated hybrid material is tested upon exposure to different hazardous species, specifically NO{sub 2}, NO, Cl{sub 2} and NH{sub 3} at operating temperature of 150°C. It has been demonstrated that for Cl{sub 2} minimum detection limit of CuPcOC{sub 8}/MWCNTs-COOH hybrid is 100 ppb. The response of hybrid sensor is found to be increased with increase in the concentration of Cl{sub 2}.
Verification of 2-D Detonation Shock Dynamics in conjunction with Los Alamos Lagrangian hydrocode
As the latest version of the fast-tube Detonation Shock Dynamics (DSD) solver is linked with the Los Alamos Lagrangian hydrocode, verification problems from a 2006 DSD report (LA-14277) have been duplicated with some of the verification criteria changed to more quantitative ones. The observed error convergence is as good as or better than reported in, quite possibly due to the careful treatment of floating point numbers to ensure that their precision level is maintained throughout the code. This report duplicates the three sample verification problems in LA-14277 using the Los Alamos ASC Lagrangian hydrocode (FLAG), official release of 3.2 Alpha6 with a few modifications. This version of FLAG is linked with the latest fast-tube Detonation Shock Dynamics (DSD) version beta 2 solver released in 2011 as part of the LanlDSD software product. New verification criteria are used for the arcwave problem where two specific locations are chosen for burn arrival time comparison. For this report FLAG's internal driver code prepares the distance function ({Psi}) and material ID fields from its hydro setup, instead of the stand-alone driver that is being utilized by the other LANL hydrocodes currently interfaced to LanlDSD. As it is implemented in version 3.2 Alpha6, the {Psi} and material ID fields and other parameters are passed from FLAG to the DSD solver directly, and the burn table is directly passed back to FLAG as part of the calling arguments. The burn-front arrival time 'exact' solutions, mentioned in the sequel for the rate-stick and 'arc-wave' problems, are computed using a pair of special-purpose Fortran codes provided by Aslam. In each case an ansatz for the form of the solution is made in which the radius from the detonator center point is used as the independent space coordinate. This leads to a simplified, problem-specific, 1D form of the governing equation. This equation is solved using 2nd-order spatial differencing and the forward Euler method on a very fine temporal and geometric mesh. The boundary conditions are handled exactly at the correct location, with second order accuracy. Care has been taken to ensure that this solution is fully converged.
Neutron scattering study of water confined in periodic mesoporous organosilicas
A series of quasi-elastic neutron scattering measurements were performed using IN6 at the Institute Laue Langevin for a mesoporous organosilica material with phenyl functions, called phenyltriethoxysilane (PTES). The aim of the experiment was to study the diffusion dynamics of nano-scale water clusters inside the hydrophobic pores as a function of temperature and hydration. By fitting the Debye-Waller factor, the data show clearly the different behavior between water, both inside and outside the hydrophobic pores, which resembles bulk water. The mean thermal displacement <u{sup 2}> of the external water increases with T almost linearly up to 353 K, while the internal water quickly reaches the maximum at T{approx}323 K, indicating the confinement by an averaged pore diameter of the porous organosilica. - Graphical abstract: This paper concerns measurement of water dynamics associated within hydrophobic mesoporous organosilica material with phenyl functions as investigated using the quasielastic neutron scattering technique. By fitting the quasielastic neutron scattering results with Debye Waller factor, it show clearly the different behavior between water h inside and outside the hydrophobic pores.
Evaluation and prediction of long-term environmental effects of nonmetallic materials, second phase
Changes in the functional properties of a number of nonmetallic materials were evaluated experimentally as a function of simulated space environments and to use such data to develop models for accelerated test methods useful for predicting such behavioral changes. The effects of changed particle irradiations on candidate space materials are evaluated.
2D materials-assisted heterogeneous integration of semiconductor membranes toward functional devices
Heterogeneous integration techniques allow the coupling of highly lattice-mismatched solid-state membranes, including semiconductors, oxides, and two-dimensional materials, to synergistically fuse the functionalities. The formation of heterostructures generally requires two processes: the combination of crystalline growth and a non-destructive lift-off/transfer process enables the formation of high-quality heterostructures. Although direct atomic interaction between the substrate and the target membrane ensures high-quality growth, the strong atomic bonds at the substrate/epitaxial film interface hinder the non-destructive separation of the target membrane from the substrate. Alternatively, a 2D material-coated compound semiconductor substrate can transfer the weakened (but still effective) surface potential field of the surface through the 2D material, allowing both high-quality epitaxial growth and non-destructive lift-off of the grown film. This Perspective reviews 2D/3D heterogeneous integration techniques, along with applications of III–V compound semiconductors and oxides. Here, the advanced heterogeneous integration methods offer an effective method to produce various freestanding membranes for stackable heterostructures with unique functionalities that can be applied to novel electrical, optoelectronic, neuromorphic, and bioelectronic systems.
Layered Double Perovskites
Successful strategies for the design of crystalline materials with useful function are frequently based on the systematic tuning of chemical composition within a given structural family. Perovskites with the formula ABX 3 , perhaps the best-known example of such a family, have a vast range of elements on A, B, and X sites, which are associated with a similarly vast range of functionality. Layered double perovskites (LDPs), a subset of this family, are obtained by suitable slicing and restacking of the perovskite structure, with the additional design feature of ordered cations and/or anions. In addition to inorganic LDPs, we also discuss hybrid (organic-inorganic) LDPs here, where the A-site cation is a protonated organic amine. Several examples of inorganic LDPs are presented with a discussion of their ferroic, magnetic, and optical properties. The emerging area of hybrid LDPs is particularly rich and is leading to exciting discoveries of new compounds with unique structures and fascinating optoelectronic properties. We provide context for what is important to consider when designing new materials and conclude with a discussion of future opportunities in the broad LDP area.
A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties
Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.
Material Properties and Temperature Effects Quantified for Slurry Wear - 16141
Literature indicates that the hardness of solid particles relative to the target material hardness and temperature influence erosion. Investigations of temperature effects on erosion also show that erosion resistance is a function of target material's homologous temperature to melting point temperature. While relative hardness effects on erosion and temperature effects on erosion have been investigated independently, their combined effect has not been evaluated. This paper summarizes a review of relevant literature on relative hardness and temperature effects on erosion and via probabilistic calculations, quantifies the combined effect of relative hardness and temperature on liquid-particle induced wear, or slurry wear. These insights can assist material selection and plant operations at waste processing facilities. (authors)
Basic Research Needs Workshop on Synthesis Science for Energy Relevant Technology
The technology that lies at our fingertips becomes more powerful each day. Smartphones connect us instantly to family, friends, and co-workers around the globe; give us access to a limitless stream of information; control the heating in our homes; and serve as our cameras, calculators, flashlights, music players, boarding passes and, on occasion, our phones. Cars are ever more fuel-efficient, safer, semi-autonomous, and have more computing power than the systems that guided humankind to the moon. LED lighting and solar panels are becoming commonplace, replacing less efficient technologies and expanding the energy options available worldwide. Novel polymers and nanoparticles are playing a crucial role in enhanced oil recovery. None of these advances would have been possible without the discovery and development of, and ability to create, new materials and chemical processes. Now imagine what our world would be like if we could accelerate those discoveries a thousandfold. What if the only limit to synthesizing new forms of matter were the imagination? We could build complex assemblies of atoms and molecules with architectures and capabilities far exceeding those of materials found in nature—for example, develop catalysts that turn garbage into fuels, design solar cells to power our homes directly from sunlight, make batteries with the energy density of gasoline, and create one- and two-dimensional solids that transport charge hundreds of times faster than silicon or allow us to build quantum bits based on the spins of electrons or photons to realize the promise of “beyond Moore’s law” computing. Advances in synthesis science are required to bring about this future—we not only must know how to design new molecules and materials with desired functions and properties through theory and computational techniques; we also must be able to make the materials we envision. New approaches to discovering as yet unimagined matter require a sea change in the way we think about the science of synthesis. Chemical and materials sciences have traditionally focused on understanding structure–function relationships with the goal of predicting where the atoms should be placed to achieve a targeted property or process. Much less effort has been directed toward a predictive science of synthesis—understanding how to get the atoms where they need to go to achieve the desired structure. This report, which is the result of the Basic Energy Sciences Workshop on Basic Research Needs for Synthesis Science for Energy Technologies, lays out the scientific challenges and opportunities in synthesis science. The workshop was attended by more than 100 leading national and international scientific experts. Its five topical and two crosscutting panels identified four priority research directions (PRDs) for realizing the vision of predictive, science-directed synthesis.
Cold quarks in medium: An equation of state
We derive a compact, semialgebraic expression for the cold-quark-matter equation of state (EoS) in a covariant model that exhibits coincident deconfinement and chiral symmetry restoring transitions in medium. In doing so we obtain algebraic expressions for the number- and scalar-density distributions in both the confining Nambu and the deconfined Wigner phases and the vacuum-pressure difference between these phases, which defines a bag constant. Our qualitative study illustrates that a confining interaction can materially alter distribution functions from those of a Fermi gas and impact significantly on a system's thermodynamic properties, possibilities that are apparent in the EoS.
Nanoconfinement of Molecular Magnesium Borohydride Captured in a Bipyridine-Functionalized Metal-Organic Framework
The lower limit of metal hydride nanoconfinement is demonstrated through the coordination of a molecular hydride species to binding sites inside the pores of a metal-organic framework (MOF). Magnesium borohydride, which has a high hydrogen capacity, is incorporated into the pores of UiO-67bpy (Zr6O4(OH)4(bpydc)6 with bpydc2- = 2,2'-bipyridine-5,5'- dicarboxylate) by solvent impregnation. The MOF retained its long-range order, and transmission electron microscopy and elemental mapping confirmed the retention of the crystal morphology and revealed a homogeneous distribution of the hydride within the MOF host. Notably, the B-, N-, and Mg-edge XAS data confirm the coordination of Mg(II) to the N atoms of the chelating bipyridine groups. In situ 11B MAS NMR studies helped elucidate the reaction mechanism and revealed that complete hydrogen release from Mg(BH4)2 occurs as low as 200 °C. Sieverts and thermogravimetric measurements indicate an increase in the rate of hydrogen release, with the onset of hydrogen desorption as low as 120 °C, which is approximately 150 °C lower than that of the bulk material. Furthermore, density functional theory calculations support the improved dehydrogenation properties and confirm the drastically lower activation energy for B-H bond dissociation.
Perspectives on multi-material additive manufacturing
The last two decades have seen enormous gains in industrial adoption of additive manufacturing (AM) technologies. Its layer-wise approach to fabrication offers designers the opportunity to create structures with unique performance advantages over their traditionally manufactured counterparts, and have created new manufacturing business models and supply chains. While today’s AM technologies have enabled the creation of new geometries, future AM systems that offer simultaneous processing of multiple materials in a single build open opportunities for new product functionality that cannot be achieved by traditional manufacturing methods. Advances in multi-material additive manufacturing, which integrate dissimilar material into a complex, three-dimensional object, is emerging but the advances have been sporadic. Moving beyond homogenous materials, adding multi-materials, gradient, functional and responsive materials, and materials with heterogeneous and graded properties means that a single additive process based on either energy delivery or material deposition alone may not be suitable. Finally, this perspective gives a brief overview of the current status, challenges, and future recommendations for multi-material additive manufacturing. The authors aim to expand the notion of multi-material additive manufacturing beyond combining materials with dissimilar properties, to combinations of materials at different length scales, material classes as well as multiple functionalities.
Adaptive Interface-PINNs (AdaI-PINNs) for inverse problems: Determining material properties for heterogeneous systems
Here, we determine spatially varying discontinuous material properties using a domain-decomposition based physics-informed neural networks (PINNs) framework named the Adaptive Interface-PINNs or AdaI-PINNs (Roy et al., 2024). We propose the use of distinct neural networks for the field variables and material properties within each material, utilizing adaptive activation functions. While the neural networks across different materials share the same weights and biases, their activation functions are uniquely tailored using a hyperparameter that influences the slope of the activation function. The proposed framework is tested on several one-dimensional and two-dimensional benchmark examples, and its performance is compared with conventional PINNs and existing domain-decomposition PINNs frameworks, namely, the Multi-domain physics-informed neural network (M-PINN), and the eXtended physics-informed neural networks (XPINNs). The results demonstrate that the proposed approach can determine randomly distributed discontinuous material properties with an L 2 error of $\mathscr{O}$ (10 -3 ) for the material property and the root-mean-square error of $\mathscr{O}$ (10 -3 ) for the primary variable while the other approaches yield errors that are approximately two orders of magnitude larger (that is, $\mathscr{O}$ (10 -1 )). Moreover, the spatial distribution of material properties obtained using the proposed framework is in close agreement with the true distribution, whereas the other approaches fare much worse. Additionally, the proposed approach is approximately 40% faster than its competitors, indicating its potential as a robust alternative for solving inverse problems in heterogeneous materials.
Controlling Nanoparticle Orientations in the Self-Assembly of ‘Patchy’ Quantum Dot-Gold Heterostructural Nanocrystals
Self-assembly of nanocrystals is a promising route for creating macroscale materials that derive function from the properties of their nanoscale building blocks. While much progress has been made assembling nanocrystals into different superlattices, controlling the relative orientations of nanocrystals in those lattices remains a challenge. Here, we combine experiments with computer simulations to study the self-assembly of patchy heterostructural nanocrystals (HNCs), consisting of near-spherical quantum dots decorated with regular arrangements of small gold satellites, into close-packed superlattices with pronounced orientational alignment of HNCs. Our simulations indicate that the orientational alignment is caused by van der Waals interactions between gold patches and is sensitive to the interparticle distance in the superlattice. We demonstrate experimentally that the degree and type of orientational alignment can be controlled by changing ligand populations on HNCs. This study provides guidance for the design and fabrication of nanocrystal superlattices with enhanced structural control.
High-Voltage Phosphate Cathodes for Rechargeable Ca-Ion Batteries
Calcium-ion batteries (CIBs) are under investigation as next-generation energy storage devices due to their theoretically high operating potentials and lower costs tied to the high natural abundance of calcium. However, the development of CIBs has been limited by the lack of available positive electrode materials. In this study, for the first time, we report two functional polyanionic phosphate materials as high-voltage cathodes for CIBs at room temperature. NaV 2 (PO 4 ) 3 electrodes were found to reversibly intercalate 0.6 mol of Ca 2+ (81 mA h g –1 ) near 3.2 V (vs Ca 2+ /Ca) with stable cycling performance at a current density of 3.5 mA g –1 . The olivine framework material FePO 4 reversibly intercalates 0.2 mol of Ca 2+ (72 mA h g –1 ) near 2.9 V (vs Ca 2+ /Ca) at a current density of 7.5 mA g –1 in the first cycle. Structural, electronic, and compositional changes are consistent with reversible Ca 2+ intercalation into these two materials.
Deep learning the properties of inorganic perovskites
We report the ability to accurately and quickly predict the stability of materials and their structural and electronic properties remains a grand challenge in materials science. Density functional theory is widely used as a means of predicting these material properties, but is known to be computationally expensive and scales as the cube of the number of electrons in the material’s unit cell. In this article, for a previously published dataset of inorganic perovskites, we show that a single neural network model using only the elemental properties of the compounds’ constituents can predict lattice constants to within 0.1 Å, heat of formation to within 0.2 eV, and band gaps to within 0.7 eV RMSE. We also compare the performance of the trained network to two widely used regression techniques, namely random forest and Kernel ridge regression, and find that the neural network’s predictions are more accurate for each of the properties. The simultaneous accurate prediction of multiple key properties of technologically relevant materials is promising for rational design and optimization in known and novel chemical spaces.