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A synthesis method for nanosized forsterite (Mg 2 SiO 4 ) doped with varying concentrations of Ni and Co has been developed to support studies of carbonation-based extraction and separation of Ni and Co from mafic and ultramafic rocks.
Quinones are appealing targets as organic electrolytes for aqueous redox flow batteries (RFBs), but their utility continues to be constrained by limited stability under operating conditions. The present study evaluates the stability of a series water-soluble quinones, with redox potentials ranging from 605–885 mV vs. NHE, under acidic aqueous conditions (1 M H2SO4). Four of the quinones are examined as cathodic electrolytes in an aqueous RFB, paired with anthraquinone-2,7-disulfonate (AQDS) as the anodic electrolyte. The RFB data complement the solution stability measures and show that the most stable electrolyte is a tetrasubstituted quinone containing four sulfonated thioether substituents. The results highlight the importance of substituting all C–H positions of the quinone in order to maximize the quinone stability and set the stage for design of improve organic electrolytes for aqueous RFBs. The authors thank Joseph Fourie for assisting in the synthesis of compound 8. Financial support for this work was provided by the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences (SSS, TWR, JBG, CWA, YP), with supplemental contributions by the Wisconsin Alumni Research Foundation (WARF) through the WARF Accelerator Program (YQ and partial support for JBG, CWA, YP). NMR spectroscopy facilities were partially supported by the NSF (CHE-0342998 and CHE-1048642), a UW Madison Instructional Laboratory Modernization Award, and a gift from Paul J. and Margaret M. Bender.
The inability to process many covalent organic frameworks (COFs) as thin films plagues their widespread utilization. Herein, a vapor-phase pathway for the bottom-up synthesis of a class of porphyrin-based COFs is presented. This approach allows integrating electrocatalysts made of metal-ion-containing COFs into the electrodes’ architectures in a single-step synthesis and deposition. By precisely controlling the metal sites at the atomic level, remarkable electrocatalytic performance is achieved, resulting in unprecedentedly high mass activity values. How the choice of metal atoms, i.e., cobalt and copper, can determine the catalytic activities of POR-COFs is demonstrated. The theoretical data proves that the Cu site is highly active for nitrate conversion to ammonia on the synthesized COFs.
Developing novel lead-free ferroelectric materials is crucial for next-generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time-consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high-throughput combinatorial synthesis approach to fabricate lead-free ferroelectric superlattices and solid solutions of (Ba 0.7 Ca 0.3 )TiO 3 (BCT) and Ba(Zr 0.2 Ti 0.8 )O 3 (BZT) phases with continuous variation of composition and layer thickness. High-resolution x-ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well-controlled compositional gradients. Ferroelectric and dielectric property measurements identify the “optimal property point” achieved at the composition of 48BZT–52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT–BZT} N superlattice geometry. This high-throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth.
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We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization. Automation and machine learning are currently driving rapid innovation in high-throughput and autonomous materials design and discovery. We present two alloy design vignettes: one focusing on a multi-objective corrosion resistant alloy optimization and a study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. Lastly, we reflect on our early efforts in on-demand alloy deposition, highlighting some of the challenges. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.
The synthesis of graphene nanoribbons (GNRs) can be realized via CH4 chemical vapor deposition (CVD) on substrates such as Ge(001) that promote highly anisotropic growth kinetics. Small polycyclic aromatic hydrocarbons (PAHs) are first sublimed onto Ge near room temperature to form graphene-like seeds that subsequently initiate GNR growth with CH4 exposure at 1173 K. The behaviors of PAHs in the time in between their sublimation onto Ge and GNR growth are unclear. Here, we study an archetypical PAH, perylene-3,4,9,10-tetracarboxyl acid dianhydride (PTCDA), on Ge(001) using both scanning tunneling microscopy (STM) and density functional theory (DFT) to characterize PAH conformation, surface diffusivity, and clustering, versus temperature. PTCDA becomes mobile above 673 K (consistent with a DFT calculated diffusion barrier of 2.01 eV). The mobile PTCDA molecules meet, cluster, and fuse at Ge step edges. These clusters have an apparent height of 0.4 nm, similar to small graphene islands, and grow laterally with increasing temperature – reaching 1.2 – 2.1 nm in width at 1173 K, consistent with the seed size extrapolated from CVD experiments. These results provide a plausible picture for how PTCDA forms seeds for anisotropic GNR CVD and show that PAHs with reduced surface diffusivity and inter-molecular reactivity are needed to enable more monodisperse PAH-seeded GNR synthesis.
Structural materials properties are highly dependent on their microstructure. Their microstructure is in turn affected by multiple fabrication and thermo-mechanical treatment parameters, all of which conform a highly-dimensional parametric space with often hidden correlations that are difficult to extract by experimentation alone. This is particularly true for alloys of the dual-phase Ti-6Al-4V family, with their greatly complex and rich microstructures, which combine several intrinsic length scales associated with multiple grain and subgrain structures, grains with different crystal lattices (α and β phases), and complex chemistry. In this paper we use a comprehensive set of machine learning techniques to develop predictive tools relating the yield strength and hardening rate of these alloys to a set of input parameters covering extensive ranges. The data generator is a finite-element crystal plasticity model for polycrystal deformation that takes into account slip anisotropy and employs standard dislocation evolution models for the α and β phases of Ti-based alloys. Our dataset includes over two thousand independent simulations and is used to train the machine learning models, which are then used to establish correlations between microstructural parameters and the alloys’ mechanical response. Our results point to the most influential parameters affecting yield strength and hardening rate, information that can then be used to guide experimental synthesis and characterization efforts to save time and resources.
Nanoparticle capping agents are critical for controlling the growth, oxidation state, and final particle size during aqueous synthesis. However, despite the known phytotoxicity of cetyltrimethylammonium bromide (CTAB) to plants, it is used to synthesize metal oxide nanoparticles of uniform size and with mesoporous structure. Among the few studies that have investigated how CTAB influences nanoparticle toxicity, CTAB has never been identified as the primary cause of nanoparticle toxicity in environmental systems; rather nanoparticle surface charge or morphology was identified as the driver of toxicity in environmentally relevant systems. In the current study, CTAB release from CTAB surface modified Cu 2 O nanoparticles (SM-Cu 2 O NPs) inhibited duckweed (Landoltia punctata) growth, even when administered at subtoxic Cu concentrations. Organic ligands, such as humic acid (HA) and ethylenediaminetetraacetic acid (EDTA), lessened growth inhibition associated with exposure to SM-Cu 2 O NPs, likely through electrostatic and hydrophobic interactions with CTAB. Finally, such results highlight the need for a more holistic approach to nanoparticle surface modification and improved communication between toxicologists and synthetic chemists to develop green alternatives for nanoparticle synthesis.
Methanogenic archaea have been shown to reduce iron from ferric [Fe(III)] to ferrous [Fe(II)] state, but minerals that form during iron reduction by different methanogens remain to be characterized. Here, we show that zerovalent iron (ZVI) minerals, ferrite [α-Fe(0)] and austenite [γ-Fe(0)], appear in the X-ray diffraction spectra minutes after the addition of ferrihydrite to the cultures of a methanogenic archaeon, Methanosarcina barkeri (M. barkeri). M. barkeri cells and redox-active, nonenzymatic soluble organic compounds in organic-rich spent culture supernatants can promote the formation of ZVI; the latter compounds also likely stabilize ZVI. Methanogenic microbes that inhabit organic- and Fe(III)-rich anaerobic environments may similarly reduce Fe(III) to Fe(II) and ZVI, with implications for the preservation of paleomagnetic signals during sediment diagenesis and potential applications in the protection of iron metals against corrosion and in the green synthesis of ZVI.
Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.
Abstract Pennycress (Thlaspi arvense L.) accumulates oil up to 35% of the total seed biomass, and its overall fatty acid composition is suitable for aviation fuel. However, for this plant to become economically viable, its oil production needs to be improved. In vivo culture conditions that resemble the development of pennycress embryos in planta were developed based on the composition of the liquid endosperm. Then, substrate uptake rates and biomass accumulation were measured from cultured pennycress embryos, revealing a biosynthetic efficiency of 93%, which is one of the highest in comparison with other oilseeds to date. Additionally, the ratio of carbon in oil to CO2 indicated that non-conventional pathways are likely to be responsible for such a high carbon conversion efficiency. To identify the reactions enabling this phenomenon, parallel labeling experiments with 13C-labeled substrates were conducted in pennycress embryos. The main findings of these labeling experiments include: (i) the occurrence of the oxidative reactions of the pentose phosphate pathway in the cytosol; (ii) the reversibility of isocitrate dehydrogenase; (iii) the operation of the plastidic NADP-dependent malic enzyme; and (iv) the refixation of CO2 by Rubisco. These reactions are key providers of carbon and reductant for fatty acid synthesis and elongation.
Critical components, such as detonators, in Sandia's stockpile contain heterogeneous materials whose performance and reliability depend on accurate, predictive models of coupled, complex phenomena to predict their synthesis, processing, and operation. Ongoing research in energetic materials has shown that microstructural properties, such as density, pore-size, morphology, and specific surface area are correlated to their initiation threshold and detonation behavior. However, experiments to study these specific characteristics of energetic materials are challenging and time consuming. Therefore, in this work, we turn to mesoscale modeling methods that may be capable of reproducing some observed phenomena to refine and predict outcomes beforehand. Even so, we have no physics-based modeling capability to predict how the microstructure of an energetic material will evolve over near- and long-term time scales. Thus, the goal of this work is to (i) identify any knowledge gaps in how the underlying microstructure forms and evolves during the synthesis process, and (ii) develop and test a mesoscale phase-field model for vapor deposition to capture critical mechanisms of microstructure formation, evolution, and variability in vapor-deposited energetic materials, such as processing conditions, material properties, and substrate interactions. Based on this work, the phase-field method is shown to be a valuable tool for developing the necessary models containing coupled, complex phenomena to investigate and understand the synthesis and processing of energetic materials.
Upregulation of triacylglycerols (TAGs) in vegetative plant tissues such as leaves has the potential to drastically increase the energy density and biomass yield of bioenergy crops. In this context, constraint-based analysis has the promise to improve metabolic engineering strategies. Here we present a core metabolism model for the C 4 biomass crop Sorghum bicolor ( iTJC1414 ) along with a minimal model for photosynthetic CO 2 assimilation, sucrose and TAG biosynthesis in C 3 plants. Extending iTJC1414 to a four-cell diel model we simulate C 4 photosynthesis in mature leaves with the principal photo-assimilatory product being replaced by TAG produced at different levels. Independent of specific pathways and per unit carbon assimilated, energy content and biosynthetic demands in reducing equivalents are about 1.3 to 1.4 times higher for TAG than for sucrose. For plant generic pathways, ATP- and NADPH-demands per CO 2 assimilated are higher by 1.3- and 1.5-fold, respectively. If the photosynthetic supply in ATP and NADPH in iTJC1414 is adjusted to be balanced for sucrose as the sole photo-assimilatory product, overproduction of TAG is predicted to cause a substantial surplus in photosynthetic ATP. This means that if TAG synthesis was the sole photo-assimilatory process, there could be an energy imbalance that might impede the process. Adjusting iTJC1414 to a photo-assimilatory rate that approximates field conditions, we predict possible daily rates of TAG accumulation, dependent on varying ratios of carbon partitioning between exported assimilates and accumulated oil droplets (TAG, oleosin) and in dependence of activation of futile cycles of TAG synthesis and degradation. We find that, based on the capacity of leaves for photosynthetic synthesis of exported assimilates, mature leaves should be able to reach a 20% level of TAG per dry weight within one month if only 5% of the photosynthetic net assimilation can be allocated into oil droplets. From this we conclude that high TAG levels should be achievable if TAG synthesis is induced only during a final phase of the plant life cycle.