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DOE OSTI2025

Self-Sensing Composites via an Embedded 3D-Printed PVDF-MoS 2 Nanosensor for Structural Health Monitoring

Carbon fiber (CF)-reinforced epoxy composites are widely used in vehicle applications, where early damage detection is crucial for reliability and safety. To address this need, we developed a self-sensing epoxy/CF composite by embedding a PVDF-MoS 2 nanosensor via an embedded 3D printing method. By harnessing the intrinsic curing kinetics of epoxy, we tailored its rheological properties to optimize the embedded printing process, enabling precise and reliable support for sensor filaments without compromising the composite’s structural and functional integrity. Through comprehensive rheological and kinetic analysis, we established a quantitative relationship among curing temperature, conversion rate, and resulting yield modulus─defining a narrow processing window essential for successful sensor integration. Specifically, we identified that an epoxy yield modulus range of 180–294 Pa and a conversion rate below 10% are critical to support the PVDF-MoS 2 filament architecture. Here, this embedded 3D printing method produces complex and multimaterial PVDF-MoS 2 sensors within an epoxy matrix with minimal deformation and reduced postprocessing, which is scalable and adaptable for industrial applications. Under cyclic loading, the embedded sensors exhibited stable signals under constant loads and increased voltage signals in response to crack formation (17–35% higher) and catastrophic failure (1 order of magnitude higher), effectively capturing structural changes in real time. This study demonstrates the potential of PVDF-MoS 2 nanocomposite sensor materials for real-time structural health monitoring in epoxy–CF composite systems, enabling early detection of defects and stress anomalies, significantly reducing the risk of unexpected failures, and enhancing structural reliability.

PVDF-MoS2 sensor↗

DOE OSTI2022

Synthesis, Crystal and Electronic Structures, Nonlinear Optical Properties, and Magnetic Properties of Two Thiophosphates: KInP 2 S 7 and KCrP 2 S 7

Two thiophosphates, KInP 2 S 7 and KCrP 2 S 7 , were structurally characterized without investigating any optical properties. Herein in this work, KInP 2 S 7 and KCrP 2 S 7 were revisited to investigate their optical and magnetic properties, respectively. Pure polycrystalline samples and crystals of KInP 2 S 7 and KCrP 2 S 7 were grown by high temperature solid state reactions, where mm-sized crystals of KCrP 2 S 7 were collected. KCrP 2 S 7 is isostructural to KInP 2 S 7 , which features a layered structure. KInP 2 S 7 and KCrP 2 S 7 possess close relationship to the layered thiophosphate M 2 P 2 S 6 (M = Fe, Co, Zn, etc.). The bonding pictures of KInP 2 S 7 were studied using the electron localization function (ELF) coupled with crystal orbital Hamilton population (COHP) calculations. The intrinsically distorted [PS 4 ] tetrahedra and [InS 6 ] octahedra are made by strong covalent P-S interactions and ionic In-S interactions, respectively. Electronic structure analysis confirmed that the optical properties of KInP 2 S 7 are mainly contributed to by [PS 4 ] tetrahedra together with small amounts of the contributions coming from [InS 6 ] octahedra. Magnetic measurement on mm-sized crystals of KCrP 2 S 7 verified that there is an antiferromagnetic transition around 21 K, and the Cr atoms are trivalent. KInP 2 S 7 is predicated to be an indirect bandgap semiconductor of 2.38 eV, which is confirmed by the UV-Vis measurement of 2.4(1) eV. KInP 2 S 7 is not a type-I phase-matching material and exhibits moderate second harmonic generation (SHG) response (0.51 × AgGaS 2 , sample of particle size of 100 µm). The laser damage threshold (LDT) of KInP 2 S 7 is very high of 5.2 × AgGaS 2 . Bandgap engineering were undergone to enhance the SHG response of KInP 2 S 7 .

36 MATERIALS SCIENCE↗

DOE OSTI2022

Fully Distributed Multi-Material Magnetic Sensing Structures for Multiparameter DAS Applications

This dissertation demonstrates the first of its kind distributed magnetic field sensor based on a fiber optic distributed acoustic sensing (DAS) scheme. Ferromagnetic nickel and Metglas® were dispersed internally within a fiber optic preform and then drawn on an in-house fiber optic draw tower to lengths in the kilometers. Due to the close proximity of the ferromagnetic metals and fiber optic core, the magnetostrictive strain response of the ferromagnetic materials when exposed to a magnetic field would perturbate within the fiber cladding and transfer that strain, internally, to the fiber optic core. Strain resulting from the magnetostrictive effect allows the DAS based sensor to accurately translate strain into readable magnetic field data. Due to the high sensitivity seen in this sensor design, multiparameter sources, acoustic and magnetic fields, were tested and validated and a three dimensional magnetic-field vector sensor was proposed. Numerical analysis of the novel sensor design was first implemented using COMSOL Multiphysics, where inputs such as magnetostrictive element shape, size, distance, and number were first investigated. Upon optimizing system constraints, the sensor design was further modified such that single mode operation was consistent across multiple fiber draws while retaining high strain transfer from the ferromagnetic elements to the fiber optic core. Ferromagnetic material selection was evaluated as a function of the saturation magnetostriction constants and a total of 4 modules were used to fully characterize the complex physics involved in this sensor design. All fabrication and testing were performed in-house using a full scale 3-story fiber draw tower and custom environmental testing stations to imitate naturally occurring events such as magnetic or acoustic point sources. A unique stacking method was used to embed ferromagnetic nickel and Metglas® into a fiber optic preform which when combined with a custom fiber draw process resulted in consistent multi-material fibers drawn to lengths of 1-km. In-house testing facilities included different types of electromagnetic generators, in addition to a soil test bed, and an outdoor test bed which allowed 100 meters of fiber to be tested simultaneously. All tested sensors demonstrated high strain transfer capabilities on the order of 0.01-10 μϵ depending on the materials used, ferromagnetic rod number, and core to metal spacing. Due to the sensitivity of the system the difference between AC and DC was distinct, and directional magnetostriction was studied. Transverse and longitudinal magnetic wave propagation was controlled through a solenoid and rectangular Helmholtz coil, both built in-house. A three-dimensional magnetic field vector sensor was proposed due to the success of the magnetic field sensor, and a design was proposed and initially tested to validate direction as a function of field strength and distance. To summarize, this dissertation explores the first fully distributed magnetic field sensor using DAS based techniques and one of the first multi-material fiber draw processes which can produce consistent single mode fiber up to 1-km. Due to extensive FEA modeling, multiple iterations of the magnetic sensor were fully characterized and an equation describing the relationship between sensor design and strain transfer has been created and validated experimentally. Multi-parameter tests including acoustic and magnetic fields were implemented and an algorithm was developed to separate the mixed signals. Finally, a test was performed to demonstrate the feasibility of sensing magnetic fields directionally. Cumulative results demonstrate a high-quality sensor alternative to current designs which may surpass other magnetic sensors due to innate multi-parameter capabilities, in addition to the inexpensive production cost and extremely long operating lengths.

02 PETROLEUM↗

DOE OSTI2022

A Framework for Inverse Prediction Using Functional Response Data

Inverse prediction models have commonly been developed to handle scalar data from physical experiments. However, it is not uncommon for data to be collected in functional form. When data are collected in functional form, it must be aggregated to fit the form of traditional methods, which often results in a loss of information. For expensive experiments, this loss of information can be costly. In this study, we introduce the functional inverse prediction (FIP) framework, a general approach which uses the full information in functional response data to provide inverse predictions with probabilistic prediction uncertainties obtained with the bootstrap. The FIP framework is a general methodology that can be modified by practitioners to accommodate many different applications and types of data. We demonstrate the framework, highlighting points of flexibility, with a simulation example and applications to weather data and to nuclear forensics. Results show how functional models can improve the accuracy and precision of predictions.

42 ENGINEERING↗

DOE OSTI2021

Thermal stress reduces carbonate production of benthic foraminifera and changes the material properties of their shells

Abstract In shallow marine environments, benthic foraminifera are important foundation species and carbonate producers. Understanding their response to future climate is often drawn from their acclimation potential in short laboratory experiments, thereby limiting our understanding of migration, species replacement, and adaptive potential. To overcome this challenge, we examine two species of benthic foraminifera from a thermally polluted field site mimicking future warming. This site and a control station cover 13–36°C causing both warm and cold stress to the local species. Computer Tomography reveals that under heat stress, even with acclimation, Lachlanella significantly reduced its shell volume. In contrast, Pararotalia calcariformata did not reduce its shell volume but reduced the relative amount of calcite with respect to shell volume and changed its reproduction cycle from twice to once per year. Raman spectroscopy indicates that thermal conditions alter the chemical composition of the calcite shells of both species. Calcification during thermal stress creates alterations in the crystal structure that are unexpectedly more prominent under cold stress than warm stress indicating warming might positively affect the shell's protective function. Supported by previous laboratory experiments and observations from the geological record, our results provide new perspective to the effect of warming on benthic foraminifera.

Titelboim, Danna↗

DOE OSTI2024

Hydrogen sensing in different hydrogen-carrying gases using composites of PdSnO 2 and halloysite nanotubes

Here, in this work, we developed composites of palladium-decorated tin dioxide (PdSnO 2 ) and halloysite nanotubes (HNTs) by adding different amounts of HNTs as additives into PdSnO 2 , and investigated how the different amounts of HNTs in the composites and the different hydrogen (H 2 ) carrying gases (air and helium (He)) could affect the H 2 sensing performance of such composites (PdSnO 2 -HNT). Through the sensing-performance characterization using H 2 carried by air, it was found that PdSnO 2 with an appropriate small amount of HNTs (i.e., 2 % of PdSnO 2 mass) can improve the sensing performance with respect to limit of detection (LOD) and response/recovery time. Further, the optimal PdSnO2-HNT composite as a sensor was tested to detect H 2 in He. The testing results indicated that the composite can detect H 2 in He, but its performance parameters (i.e., the profile of calibration curve, LOD, and response time) are different from those of such a sensor in detecting H 2 in air. Moreover, the composite still presented better sensing performance than PdSnO 2 without HNTs in detecting H 2 in He. Possible reasons for the effects of HNT and H 2 -carrying gas on the sensing performance of PdSnO 2 -HNT-based sensors were discussed. We believe that this study provides valuable insights into the functionality and the adaptability of PdSnO 2 -HNT-based H 2 sensors in diverse operational conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Quantum Monte Carlo and Density Functional Theory Study of Strain and Magnetism in 2D 1T-VSe 2 with Charge Density Wave States

Two-dimensional (2D) 1T-VSe 2 has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states, and the interplay between the two. We employed a combined Diffusion Monte Carlo (DMC) and density functional theory (DFT) approach to accurately investigate the magnetic properties, CDW states, and their responses to strain in monolayer 1T-VSe 2 . Our calculations show the delicate competition between various phases, revealing critical insights into the relationship between their energetic and structural properties. Here, we performed classical Monte Carlo simulations informed by our DMC and DFT results and found the magnetic transition temperature (T c ) of the undistorted (non-CDW) FM phase to be 228 K and the distorted (CDW) phase to be 68 K. Additionally, we studied the response of biaxial strain on the energetic stability and magnetic properties of various phases of 2D 1T-VSe 2 and found that small amounts of strain can increase the T c , suggesting a promising route for engineering and enhancing magnetic behavior. Finally, we synthesized 1T-VSe 2 and performed Raman spectroscopy measurements, which were in close agreement with our calculated results, validating our computational approach. Our work emphasizes the role of highly accurate DMC methods in advancing the understanding of monolayer 1T-VSe 2 and provides a robust framework for future studies of 2D magnetic materials.

2D magnets↗

DOE OSTI2022

Experimental Procedure and Results for Contact Thermal Conductance Measurements performed during FY 2021

Weapons Test Engineering groups at LANL are responsible for testing and simulating weapon assemblies and subassemblies in support of qualification and certification of nuclear weapon systems. The complex nature of the assemblies themselves coupled with the loading environments, gives rise to low confidence and high uncertainties in the interpretation of both experimental and simulated results. In an effort to reduce these uncertainties, a project was started at LANL to perform focused and systematic experiments combined with simulations to increase confidence in both arenas. A key goal of the work performed under the Delivery Environments (DE) Testbeds to Reduce Uncertainties in Simulations and Tests TRUST program, focuses on identifying and minimizing sources of uncertainty associated with experimental and computational techniques. Specifically, with the above as motivation, a measurement procedure was developed by MST-8 to perform thermal conductivity measurements on cylinders of both similar and dissimilar metallic systems as a function of loading conditions and varying material interface morphologies between these components.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Freestanding BaTiO 3 ‐Au Vertically Aligned Nanocomposite toward Flexible Multi‐Sensing Platform

Abstract Flexible and wearable sensors show enormous potential for personalized healthcare devices by real‐time monitoring of an individual's health. Typically, a single functional material is selected for one sensor to sense a particular physical signal while multiple materials will be selected for multi‐mode sensing. Vertically aligned nanocomposites (VANs) have recently demonstrated various material combinations and novel coupled multifunctionalities that are hard to achieve in any single‐phase material alone, including multiphase multiferroics, magneto‐optic coupling, and strong magnetic and optical anisotropy. Integrating these novel VANs into wearable sensors shows enormous potential in multi‐mode sensing owing to their multifunctional nature. In this work, the transfer of VANs onto polydimethylsiloxane as a novel flexible chemical and pressure sensor is demonstrated. For this demonstration, the classical BaTiO 3 ‐Au VAN with combined plasmonic and piezoelectric properties is used to demonstrate a multi‐sensing mechanism. A thin water‐soluble buffer of Sr 3 Al 2 O 6 serves as a buffer layer for the epitaxial growth and transfer process. The electrical output based on the piezoelectric responses and identifying 4‐mercaptobenzoic acid by surface‐enhanced Raman spectroscopy reveal great potential for free‐standing VANs in a wearable multifunctional sensing platform.

Tsai, Benson Kunhung [School of Materials Engineer↗

DOE OSTI2022

Crystal plasticity modeling of strain-induced martensitic transformations to predict strain rate and temperature sensitive behavior of 304 L steels: Applications to tension, compression, torsion, and impact

This paper advances crystallographically-based Olson-Cohen (direct γ → α’) and deformation mechanism (indirect γ→ε→α’) phase transformation models for predicting strain-induced austenite to martensite transformation. Here, the advanced transformation models enable predictions of not only strain-path sensitive, but also of strain-rate and temperature sensitive deformation of polycrystalline stainless steels (SSs). The deformation of constituent grains in SSs is modeled as a combination of anisotropic elasticity, crystallographic slip, and phase transformation, while the hardening is based on the evolution of dislocation density and explicit shifts in phase fractions. Such grain-scale deformation is implemented within the meso-scale elasto-plastic self-consistent (EPSC) homogenization model, which is coupled with the implicit finite element (FE) method to provide a constitutive response at each FE integration point for solving boundary value problems at the macro-scale. Parameters pertaining to the hardening and transformation models within FEEPSC are calibrated and validated on a suite of data including flow curves and phase fractions for monotonic compression, tension, and torsion as a function of strain-rate and temperature for wrought and additively manufactured (AM) SS304L. To illustrate the potential and accuracy of the integrated multi-level FE-EPSC simulation framework, geometry, mechanical response, phase fractions, and texture evolution are simulated during gas-gun impact deformation of a cylinder and quasi-static tension of a notched specimen made of AM SS304L. Details of the simulation framework, comparison between experimental and simulation results, and insights from the results are presented and discussed.

304L steels↗

DOE OSTI2023

Deciphering the multiple deformation mechanisms responsible for sustained work hardening in a FeCrCoNi medium entropy alloy

Two important and desirable properties of materials for most structural applications are high tensile strength and ductility, which typically require high work hardening to delay necking. Here, in this work, we designed and tensile tested a face-centered cubic (fcc) Fe-Cr-Co-Ni medium-entropy alloy in which multiple deformation mechanisms are triggered during tensile loading at different temperatures to induce sustained work hardening. Our strategy involved control of the relative stabilities of the fcc, hcp (hexagonal close-packed), and bcc (body-centered cubic) phases in this quaternary system via high-throughput thermodynamic calculations. This alloy not only exhibits extensive deformation-induced nanotwinning at room temperature, but also displays a two-step sequential phase transformation [γ (fcc) → ε (hcp) martensite → α’ (bcc) martensite] at 77 K, which contrasts with the single-step phase transformation [γ → ε martensite] observed in many other fcc high/medium entropy alloys with a low stacking fault energy. The sequence of phase transformation at 77K was supported by first-principles density functional theory calculations. This work provides new templates for the design of alloys capable of multiple deformation mechanisms for sustained work hardening.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Thin-Film Stabilization and Magnetism of η -Carbide-Type Iron Nitrides

Transition-metal nitrides in ..eta..-carbide-type structures exhibit unusual bonding motifs and proximity to magnetic instabilities. Yet they remain unexplored in thin-film form due to the difficulty of stabilizing nitrogen-poor ternaries among competing phases. Here, we report the thin-film synthesis and phase-stability mapping of the ..eta..-nitride systems Fe-W-N and Fe-Mo-N. Amorphous Fe-M-N (M = W, Mo) combinatorial libraries deposited by reactive cosputtering crystallize upon rapid thermal annealing, enabling systematic identification of synthesis windows as a function of composition and annealing temperature. Using laboratory powder X-ray diffraction and synchrotron grazing incidence wide-angle X-ray scattering, we establish that Fe 3 Mo 3 N-based ..eta..-carbide phases form over a substantially broader compositional and thermal range than W-based compositions, where ..eta.. structures are stabilized only when the films are Fe-rich. These trends are rationalized using mixed chemical-potential vs composition phase diagrams that capture the narrow nitrogen chemical-potential stability of ..eta..-nitrides. Magnetic measurements reveal that ferromagnetism is induced in Fe-rich Fe 3.54 Mo 2.46 N with a small exchange-bias-like response that is absent in Fe 3 W 3 N-based compositions, highlighting the sensitivity of magnetic behavior to modest deviations from stoichiometry. This work establishes practical thin-film synthesis routes for ..eta..-nitride materials and demonstrates how composition can be tuned to access emergent magnetic phenomena in these complex nitrides.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Composition and Structure of Fluorescent Graphene Quantum Dots Generated by Enzymatic Degradation of Graphene Oxide

The broad applications of carbon nanomaterials (CNMs) in both materials and life sciences necessitate investigation of their metabolites due to the inevitable contact of CNMs and biological systems. Graphene oxide (GO), along with other types of CNMs, can be enzymatically degraded by myeloperoxidase (MPO), an enzyme released during the innate immune response. However, the enzymatic degradation products are neither well-defined nor well-understood. Some products generated during MPO-catalyzed degradation of GO could emit blue photoluminescence (PL) and were simply dubbed graphene quantum dots (GQDs) without further elucidating their structures. In this work, we use liquid chromatography-mass spectrometry (LC-MS) to isolate and elucidate chemical structures of the MPO-catalyzed degradation products. A general chemical formula screening workflow was developed for the GQDs, which are in the form of polyaromatic hydrocarbons (PAHs), obtained in the degradation products. Structures of the PAHs responsible for the blue PL were further proposed using density functional theory (DFT) calculations. Our results indicated that structures with several conjugated benzene rings are likely to generate the observed PL. This work provides insights into the mechanism of enzymatic degradation and open opportunities for fluorescence imaging of GO in biological systems.

composition and structure screening↗

DOE OSTI2023

Real-space Green's function approach for intrinsic losses in x-ray spectra

Intrinsic inelastic losses in x-ray spectra originate from excitations in an interacting electron system due to a suddenly created core-hole. These losses characterize the features observed in x-ray photoemission spectra (XPS), as well as many-body effects such as satellites and edge-singularities in x-ray absorption spectra (XAS). However, they are usually neglected in practical calculations. As shown by Langreth these losses can be treated within linear response in terms of a cumulant Green's function in momentum space. Here we present a complementary ab initio real-space Green's function generalization of the Langreth cumulant in terms of the dynamically screened core-hole interaction W c (ω) and the independent particle response function. Here we find that the cumulant kernel β⁡(ω) is analogous to XAS, but with the transition operator replaced by the core-hole potential with monopole selection rules. The behavior reflects the analytic structure of the loss function, with peaks near the zeros of the dielectric function, consistent with delocalized quasiboson excitations. The approach simplifies when W c (ω) is localized and spherically symmetric. In conclusion, illustrative results and comparisons are presented for the electron gas, sodium, and some early transition metal compounds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Self-sorting in supramolecular assemblies

Supramolecular self-assembly enables living organisms to form highly functional hierarchical structures with individual components self-organized across multiple length scales. This has inspired work on multicomponent supramolecular materials to understand factors behind co-assembly versus self-sorting of molecules. We report here on a supramolecular system comprised of negatively charged peptide amphiphile (PA) molecules, in which only a tiny fraction of the molecules (0.7 mol%) were covalently conjugated to one of two different fluorophores, half to fluorescein isothiocyanate (FTIC) and the other half to tetramethylrhodamine (TAMRA). Confocal microscopy of the system revealed self-sorting of the two different fluorescent PA molecules, where TAMRA PA is concentrated in micron-scale domains while FITC PA remains dispersed throughout the sample. From Förster resonance energy transfer and fluorescence recovery experiments, we conclude that conjugation of the negatively charged FITC to PA significantly disrupts its co-assembly with the 99.3 mol% of unlabeled molecules, which are responsible for formation of micron-scale domains. Conversely, conjugation of the zwitterionic TAMRA causes no such disruption. Interestingly, this dissimilar behavior between FITC and TAMRA PA causes them to self-sort at large length scales in the supramolecular system, mediated not by specific interactions among the individual fluorophores but instead by their different propensities to co-assemble with the majority component. We also found that greater ionic strength in the aqueous environment of the system promotes mixing by lowering the electrostatic barriers involved in self-sorting. Furthermore, our results demonstrate great thermodynamic subtlety in the driving forces that mediate self-sorting versus co-assembly in supramolecular peptide assemblies.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Artificial intelligence for materials research at extremes

Abstract Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Exceptional Electrical Detection of Trace NO 2 via Mixed Metal MOF-on-MOF Film-Based Sensors

The tunability of metal–organic frameworks (MOFs) makes them exceptional materials for the development of highly selective, low-power sensors for toxic gas detection. Herein, we demonstrate enhanced detection of NO 2 gas by a MOF-based electrical impedance sensor made using a unique mixed metal MOF-on-MOF synthesis. For this work, a combined experimental and computational study was performed using the exemplar Ni x Mg 1–x -MOF-74 to understand the fundamental structure–property relationships behind metal mixing and MOF film synthesis methods on sensor performance. Density functional theory results indicated that the presence of Ni in Mg-MOF-74 increased framework stability and increased the electron density of states at lower energies near the HOMO, as well as enhanced the NO 2 –Mg adsorption interaction. Impedance data of the Ni x Mg 1–x -MOF-74 films with larger Ni contents showed greater impedance change after exposure to 1 ppm of NO 2 gas. Furthermore, when synthesized through either a drop-cast or direct solvothermal film growth approach, the monometallic Ni-based sensors had the best performance. However, the mixed metal Ni x Mg 1–x -MOF-74 sensors synthesized through a MOF-on-MOF approach resulted in the highest impedance change, outperforming all monometallic Ni-based sensors. In particular, the mixed metal Ni-on-Mg-MOF-74 film was the best-performing sensor with an impedance change of 309 upon trace NO 2 exposure. Change in impedance response after NO 2 exposure was improved by 52% compared to the best monometallic Ni-on-Ni-MOF-74 sensor. Structural analysis of the Ni-on-Mg film showed that the first Mg-MOF-74 layer acts as a structural template controlling the structural features of the final film after metal exchange with Ni. This led to improved film quality, evidenced by the greater crystallinity and larger MOF grain sizes, and resulted in enhanced sensor performance which was not achievable through other metal mixing methods. Altogether, this study identifies structure–property relationships and synthetic templating methods that inform MOF-based sensor design, allowing for improved detection of toxic compounds.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Engineering of Ferroic Orders in Thin Films by Anionic Substitution

Multiferroics are a unique class of materials where magnetic and ferroelectric orders coexist. The research on multiferroics contributes significantly to the fundamental understanding of the strong correlations between different material degrees of freedom and provides an energy-efficient route toward the electrical control of magnetism. While multiple ABO 3 oxide perovskites are identified as being multiferroic, their magnetoelectric coupling strength is often weak, necessitating the material search in different compounds. Here, the observation of room-temperature multiferroic orders in multi-anion SrNbO 3-x N x thin films is reported. In these samples, the multi-anion state enables the room-temperature ferromagnetic ordering of the Nb d-electrons. Simultaneously, ferroelectric responses that originate from the structural symmetry breaking associated are found with both the off-center displacements of Nb and the geometric displacements of Sr, depending on the relative O-N arrangements within the Nb-centered octahedra. So findings not only diversify the available multiferroic material pool but also demonstrate a new multiferroism design strategy via multi-anion engineering.

thin-films↗