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At least 181 records · Page 10

Linker Redox Mediated Control of Morphology and Properties in Semiconducting Iron‐Semiquinoid Coordination Polymers**

Abstract The emergence of conductive 2D and less commonly 3D coordination polymers (CPs) and metal–organic frameworks (MOFs) promises novel applications in many fields. However, the synthetic parameters for these electronically complex materials are not thoroughly understood. Here we report a new 3D semiconducting CP Fe 5 (C 6 O 6 ) 3 , which is a fusion of 2D Fe‐semiquinoid materials and 3D cubic Fe x (C 6 O 6 ) y materials, by using a different initial redox‐state of the C 6 O 6 linker. The material displays high electrical conductivity (0.02 S cm −1 ), broad electronic transitions, promising thermoelectric behavior ( S 2 σ=7.0×10 −9 W m −1 K −2 ), and strong antiferromagnetic interactions at room temperature. This material illustrates how controlling the oxidation states of redox‐active components in conducting CPs/MOFs can be a “pre‐synthetic” strategy to carefully tune material topologies and properties in contrast to more commonly encountered post‐synthetic modifications.

Wang, Lei↗

Phase diagram of magnetic shape memory alloy Ni 50 Mn $50–x$ In $x$ , 0 < $x$ , 25 from first principles, via spin cluster expansion and phonon vibrational entropies

The metamagnetic shape memory Heusler alloy Ni 50 Mn $50–x$ In $x$ exhibits a rich phase diagram featuring competing magnetic states, coupled magnetic–structural phase transitions, and strong compositional sensitivity. Existing first-principles approaches struggletocapturetheintertwinedchemical, magnetic, andvibrationaleffectsinthesealloys, necessitating a more integrated modeling framework. We develop a spin cluster expansion (spin-CE) framework augmented by a quasi-harmonic phonon model to capture both configurational (chemical and magnetic) and vibrational contributions to the free energy of Ni 50 Mn $50–x$ In $x$ over the full compositional range 0 ≤ x ≤25. The spin-CE includes both chemical clusters and composition-dependent Ising spin interactions, with parameters fit to a first-principles density functional theory (DFT) dataset. Using this approach, we predict the complete magnetostructural phase diagram and transformation temperatures of Ni 50 Mn $50–x$ In $x$ across the composition space. We find that vibrational entropy alone is insufficient to reproduce the martensitic transformation in the magnetic shape memory alloy regime, highlighting the essential role of magnetism. Incorporating both magnetic and vibrational contributions allows us to reproduce all experimentally known phases, including the disappearance of the stable martensite phase at a critical In concentration and the Curie temperature of the austenite phase. The method also captures the transition with increasing In in martensite from antiferromagnetic to ferromagnetic order and predicts re-entrant ferromagnetism, though the latter occurs at higher In content than reported experimentally. We discuss possible sources of this discrepancy and highlight the broader applicability of the method to other magnetostructurally complex materials, where it may offer mechanistic insight and predictive design capabilities.

Cluster expansion↗

Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable, when interacting with its surroundings. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined law, the entropy production due to internal processes was not included, and the 2nd law was thus practically removed from the Gibbs combined law, so it is only applicable to systems under equilibrium, thus commonly termed as equilibrium or Gibbs thermodynamics. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a system in the later 1800's and early 1900's. With the quantum mechanics (QM) developed in 1920's, the QM-based statistical thermodynamics was established and connected to classical statistical thermodynamics at the classical limit as shown by Landau in the 1940's. In 1960's the development of density functional theory (DFT) by Kohn and co-workers enabled the QM prediction of properties of the ground state of a system. On the other hand, the entropy production due to internal processes in non-equilibrium systems was studied separately by Onsager in 1930's and Prigogine and co-workers in the 1950's. In 1960's to 1970's the digitization of thermodynamics was developed by Kaufman in the framework of the CALculation of PHAse Diagrams (CALPHAD) modeling of individual phases with internal degrees of freedom. CALPHAD modeling of thermodynamics and atomic transport properties has enabled computational design of complex materials in the last 50 years. Our recently termed zentropy theory integrates DFT and statistical mechanics through the replacement of the internal energy of each individual configuration by its DFT-predicted free energy. The zentropy theory is capable of accurately predicting the free energy of individual phases, transition temperatures and properties of magnetic and ferroelectric materials with free energies of individual configurations solely from DFT-based calculations and without fitting parameters, and is being tested for other phenomena including superconductivity, quantum criticality, and black holes. Those predictions include the singularity at critical points with divergence of physical properties, negative thermal expansion, and the strongly correlated physics. Furthermore, those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of the materials genome®. This has the potential to shift the paradigm of CALPHAD modeling from being heavily dependent on experimental inputs to becoming fully predictive with inputs solely from DFT-based calculations and machine learning models built on those calculations and existing experimental data through newly developed and future open-source tools. Furthermore, through the combined law of thermodynamics including the internal entropy production, it is shown that the kinetic coefficient matrix of independent internal processes is diagonal with respect to the conjugate potentials in the combined law, and the cross phenomena that the phenomenological Onsager flux and reciprocal relationships are due to the dependence of the conjugate potential of a molar quantity on nonconjugate molar quantities and other potentials, which can be predicted by the zentropy theory and CALPHAD modeling.

42 ENGINEERING↗

Water-induced surface ordering facilitating the microcutting of ductile metals

Metal cutting is a crucial process in modern manufacturing. Enhancing the machinability of metals can significantly improve their production efficiency and surface integrity. Coating surface-active media (SAM) on the free surface of the metals before cutting is an easy method to improve machinability, which usually pertains to the category of the renowned Rehbinder effect. However, the existing SAM is usually hazardous and complex materials. Besides, the effect of SAM on the local structure of the metal surface remains unclear. In this study, water is employed as a simple yet often overlooked SAM in the microcutting of copper. Using water as SAM also allows the employment of X-ray absorption fine structure spectroscopy (XAFS) to study the local structure of copper with and without water coating. Results show that water coating on the free surface of copper can significantly reduce the cutting force and chip thickness, and improve the surface finish. Interestingly, removing the water coating enables the recovery of the cutting force, demonstrating a reversible effect. Based on the XAFS results and molecular dynamics simulation, a water-induced surface ordering mechanism is proposed to explain the findings from the microcutting experiments. This mechanism suggests that water molecules can induce surface ordering in copper, resulting in reduced surface energy and fracture toughness of copper, thus enhancing machinability. In conclusion, this work provides valuable insights into the comprehension of the Rehbinder effect and shows that picometer-scale modifications of the surface atom arrangement can considerably alter the deformation mode of metals, paving the way for the development of new manufacturing processes.

36 MATERIALS SCIENCE↗

A stress-sensitive precipitate nucleation model beyond classical nucleation theory

The dynamic evolution of precipitates and second phases dictates the strength and stability of most engineering alloys. By design, or as a consequence of thermo-mechanical aging, engineering metals and alloys often form precipitates of second phases when subjecting to diverse thermal and mechanical loads. Precipitation is governed by several factors, including the alloy’s composition, processing/operating temperature, and stresses — either as a result of external loads or from residual stresses. However, state-of-the-art models for precipitate nucleation (i.e., classical nucleation theory) typically lacks consistent method to capture the effects of externally applied and/or internal stresses on nucleation; thereby severely limiting the applicability of these models to complex materials systems and to representative loading scenarios. Here, in this work, we extend upon classical nucleation theory to account for the effect of stresses on precipitation kinetics and thermodynamics. This is achieved via the use of an Eshelbian micromechanics framework keeping track of (i) the stress build up resulting from second phase formation as a function of mechanical load and, (ii) the effects of dislocations on precipitate formation. This new model is applied to σ precipitate in Fe–Cr binary alloys and M 23 C 6 precipitate in 316H stainless steel (SS). Simulations demonstrate the important role of both the remotely applied loads and dislocation pile ups on precipitate nucleation.

36 MATERIALS SCIENCE↗

Predicting phase behavior in high entropy and chemically complex alloys

The interest in high entropy alloys and other metallic compounds with four or more elements at near-equiatomic ratios has drawn attention to the ability to rapidly predict phase behavior of these complex materials, particularly where existing thermodynamic data are lacking. In this paper, we discuss aspects of this from the point of view of predicting without utilizing (or fitting) experimental data. Of particular interest are heuristic approaches that provide prediction of single-phase compositions, more rigorous approaches that tackle the thermodynamics from a more fundamental point of view, and simulation approaches that provide further insight into the behaviors. This paper covers cases of all three of these, in order to examine the strengths and weaknesses of each approach, and to indicate directions where these may be utilized and improved upon. Of particular interest is moving beyond “which composition may form a solid solution,” to recognizing the importance of underlying thermodynamic realities that affect the temperature- and composition-dependent transformations of these materials.

36 MATERIALS SCIENCE↗

Macroscale property assessment and indentation characteristics of thick section friction stir welded AA 5083

The spatial gradients in microstructure have been proven to be effective in optimizing the performance of components, and several techniques have been developed to achieve this microstructure. Nevertheless, the local microstructural modification methods inevitably result in the formation of gradient microstructure leading to a variation in the property. Herein this work systematically investigates the effect of gradient microstructure formed during thick section friction stir weld Al5083 alloy on its mechanical properties. A novel macro-indentation technique, profilometer-based indentation plastometry (PIP), is utilized to capture the variation in the mechanical properties in the feedstock (rolled plate) and weld region. The feedstock exhibited a gradient microstructure owing to different stress states during rolling. Additionally, the stirring of the tool resulted in a complex material flow pattern producing a microstructure gradient in the weld region. The aspect ratio of the grains was identified as the primary factor causing variation in indentation response in the feedstock. In contrast, variation in the grain size in the weld region resulted in property variation. Overall, the stir zone has a lower yield strength (158±4MPa) than the base material (173±3MPa). In addition, microstructure heterogeneity in the stir zone resulted in lower elongation than the base material.

36 MATERIALS SCIENCE↗

Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale

The evolution of the ω phase in titanium–vanadium (Ti–V) alloys is critical for their mechanical properties, particularly in aerospace and biomedical applications. Here, this study employs a Rapid Artificial Neural Network (RANN) potential to model the ω phase evolution at the atomistic level, demonstrating a high degree of consistency with experimental observations, unlike the Modified Embedded Atom Method (MEAM), which fails to capture this phase transformation accurately. RANN simulations replicate key phenomena such as the nucleation of α precipitates at ω/β interfaces and accurate lattice orientations, enhancing our understanding of phase stability and transformation kinetics. The findings affirm that RANN potentials can significantly improve the prediction accuracy of complex material behaviors, offering a powerful tool for designing advanced materials with tailored properties such as solute effect in various stacking fault energies. This approach not only bridges the gap between theoretical predictions and empirical data but also sets a new direction for future research in materials science, emphasizing the integration of machine learning techniques in the development and optimization of new alloys.

36 MATERIALS SCIENCE↗

Low Temperature Activation of Methane on Metal-Oxides and Complex Interfaces: Insights from Surface Science

The abundance of inexpensive, natural gas has transformed the energy landscape, whereby revealing new possibilities for sustainable chemical technologies or impacting those that have relied on traditional fossil fuels. The primary component, methane, is underutilized and wastefully exhausted, leading to anthropogenic global warming. Historically, the manipulation of methane remained “ clavis aurea ,” an insurmountable yet rewarding challenge and thus the focus of intense research. This is primarily due to an inability to dissociate C–H bonds in methane selectively, which requires a high energy penalty and is an essential prerequisite for the direct conversion of methane into a large set of value-added products. The discovery of such processes would promise an energy gainful use of natural gas benefiting several essential chemical processes associated with C1 chemistry. This first C–H bond dissociation step of the methane molecule appears in numerous catalytic mechanisms as the rate-determining step or most essential barrier sequence for all subsequent steps that follow in the production of C–C, C–O, or C x –H y –O z bonds found in value added products. A main goal is to catalytically reduce the energy barrier for the first C–H bond dissociation to be able to achieve the activation of methane at low or moderate temperatures. As such there is great value in understanding the fundamental nature of the active sites responsible for bond breaking or formation and thus be able to facilitate better control of this chemistry, leading to the development of new technologies for fuel production and chemical conversion. Surface science studies offer enhanced perspectives for a careful manipulation of bonds over the last layer atoms of catalyst surfaces, an essential factor for the design of atomically precise catalysts and unravelling of the reaction mechanism. With the advent of new surface imaging, spectroscopy, and in situ tools, it has been possible to decipher the surface chemistry of complex materials systems and further our understanding of atomic active sites on the surfaces of metals, oxides, and carbides or metal–oxide and metal–carbide interfaces. The once considered near impossible step of C–H bond activation is now observed at low temperatures with high propensity over a collection of oxide, metal–oxide, and metal–carbide systems in a conventional or inverse configuration (oxide or carbide on metal). The enabling of C–H activation at low temperature has opened interesting possibilities for the specific production of chemicals such as methanol directly from methane, a step toward facile synthesis of liquid fuels. We highlight the most recent of these results and present the key aspects of active site configurations engineered from surface science studies which enable such a simple reactive event through careful manipulation of the last surface layer of atoms found in the catalyst structure. New concepts which help in the activation and conversion of methane are discussed.

36 MATERIALS SCIENCE↗

Thermal Gradient Effects on Redox Evolution and Volatility-Driven Fractionation in Ternary U/Ce/Cs Condensates

Understanding how thermal history influences redox evolution and chemical fractionation is essential for characterizing high-temperature condensation in complex materials, including nuclear debris. Here, we tested the hypothesis that distinct thermal regimes in a plasma flow reactor influence redox pathways and elemental partitioning in ternary U/Ce/Cs systems. A configurable plasma flow reactor was modified with an external tube furnace to impose two distinct thermal gradients: continuous ambient cooling and a furnace-assisted thermal hold-up near 1400 K followed by rapid cooling. Transmission electron microscopy characterized phase identity, morphology, and nanoscale element distributions, while inductively coupled plasma-mass spectrometry quantified bulk elemental ratios. Across both thermal regimes, uranium and cerium condensed as UO 2 and CeO 2 as dominant refractory oxide products. Uranium partially oxidized to α-UO 3 during extended ambient cooling, while furnace-assisted hold-up preserved UO 2 and produced partial reduction of cerium to Ce 2 O 3 . Cesium remained volatile upstream and condensed later in the reactor, forming Cs 2 O and Cs-uranate phases with the highest incorporation after thermal hold-up. Bulk ICP-MS measurements supported these observations. U/Ce ratios remained comparatively stable and Cs displayed delayed and apparent transient enrichment that matched the nanoscale measurements. This integrated approach provides a quantitative method for linking thermal gradients to redox evolution and volatility-driven fractionation. These results show how the plasma flow reactor can identify where equilibrium descriptions remain adequate and where kinetic effects from residence time and temperature history must be considered when interpreting condensation behavior in multicomponent systems.

and nuclear chemistry↗

Understanding the Effect of Sample Geometry on Temperature Distribution during Optical Floating Zone Crystal Growth in Vacuum Environment through Heat Transfer Modeling

Optical floating zone furnaces (OFZ) have had a transformative impact on fundamental science due to their ability to rapidly produce large single crystals of a wide variety of complex materials. However, a quantitative understanding of the OFZ growth environment is generally lacking due to the difficulty of measuring the local sample temperatures during OFZ growth, as well as to the general lack of information about the temperature-dependent physical parameters needed to model heat transfer. To overcome these challenges, we apply a physics-based heat transfer model, parametrized by measurements from synchrotron experiments and a machine-learning (ML) algorithm, to simulate the temperature distributions of samples heated in an OFZ furnace in a vacuum environment. This model is used to quantitatively understand how the sample maximum temperature and temperature gradient (key parameters that influence the success of crystal growth) are affected by the rod size, rod shape, and heat-zone position on the rod. The results of this study can be applied to make informed decisions on how crystal growth parameters can be tuned to modify temperature profiles and to optimize crystal growth outcomes even when data on internal sample temperature profiles (e.g., those obtained through in situ synchrotron experiments) are not accessible.

36 MATERIALS SCIENCE↗

Insights into Copper Sulfide Formation from Cu and S K edge XAS and DFT studies

An understanding of the fundamentals of the reaction between CuO with trace amounts of H 2 S to form CuS products is critical for the optimal utilization of this process in sulfur removal applications. Unfortunately, CuS is a complex material, featuring various Cu 2-x S compounds (with 0 ≤ x ≤ 1), distorted crystal phases, and varying electronic structures and coordination environments of Cu and S ions. In this work, we combine ex situ and in situ X-ray absorption spectroscopy (XAS) at S and Cu K edges, fixed bed sorption experiments, DFT simulations, and other characterization techniques to speciate the CuS products formed at different temperatures (298–383 K) and from CuO sorbents with different crystallite sizes (2.8–40 nm). The results of our analysis identify the formation of a distorted CuS layer at the surface of CuO crystals with disulfide groups with shorter Cu–S bonds and higher delocalization of the positive charge of the Cu center into (S 1– )2. This distorted CuS layer dominates the XAS signal at lower temperatures (298–323 K) and at the initial stages of sulfidation at higher temperatures (353 and 383 K) where conversion is low (<40%). First-principles atomistic simulations confirm the thermodynamic favorability of the formation of surface (S 1– )2 on both CuO (111) and ($\bar{1}11$) surfaces, providing further support for our experimental observations. Furthermore, these simulations reveal that the presence of disulfide bonds stabilized surface hydroxyl groups, leading to lower Gibbs Free Energies of their surface migration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Crystallization of the P 3 Sn 4 Phase upon Cooling P 2 Sn 5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

We performed molecular dynamics simulations to study the crystallization of the P3Sn4 phase from P 2 Sn 5 liquid using a machine learning (ML) interatomic potential with desirable efficiency and accuracy. Our results capture the liquid properties of P 2 Sn 5 at 1300 K, which is well above the melting temperature. The phase separation and crystallization are observed when P 2 Sn 5 liquid is cooled down below 832 and 505 K, respectively. The simulation results are in good agreement with the experimentally observed phase transformation behaviors and provide useful insights into the complex nucleation and crystallization process at the details of atomistic scale. Our work also demonstrated that ML interatomic potentials based on neural network deep learning are robust and capable of accurately describing the energetics and kinetics of complex materials through molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Phase Control of Nanocrystalline Inclusions in Bioprecipitated Titania with a Panel of Mutant Silica-Binding Proteins

The biomimetic route to inorganic synthesis presents an opportunity to produce complex materials with superior properties under ambient conditions and from nontoxic precursors. While there has been significant progress in using solid-binding peptides (SBPs), proteins, and organisms to produce a variety of inorganic and hybrid structures, it has been more challenging to understand the interplay of solution conditions and solid-binding peptide (SBP) sequence, structure, and self-association on synthetic outcomes. Here, we show that fusing the Car9 silica-binding peptide—but not the silaffin-derived R5 peptide—to superfolder green fluorescent protein (sfGFP) enhances the ability of micromolar concentrations of protein to induce rapid titania (TiO 2 ) precipitation from acidified solutions of tetrakis(di-lactato)-oxo-titanate (TiBALDH). TiO 2 is produced stoichiometrically and although predominantly amorphous, contains nanosized anatase and monoclinic TiO 2 (B) inclusions. Remarkably, the phase of these nanocrystallites can be tuned from about 80% TiO 2 (B) to about 65% anatase by using Car9 mutants impaired in their ability to drive the formation of higher-order sfGFP-Car9 oligomers. Our results suggest that the presentation of multiple basic side chains in an extended plane formed by SBP self-association is critical to template the formation of monoclinic crystallites and underscore the subtle influence that single or dual substitutions in dodecameric SBPs can exert on the yield and crystallinity of biomineralized inorganics.

mutagenesis↗

Deep Learning Analysis of Polaritonic Wave Images

Deep learning (DL) is an emerging analysis tool across the sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nanoscale deeply subdiffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a time scale that is at least 3 orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.

97 MATHEMATICS AND COMPUTING↗

Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors

Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and “intelligent” probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. Therefore, we verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS 3 . This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗