Spatially-variable carbonation reactions in polycrystalline olivine
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Semiempirical energy relations provide a means of estimating thermodynamic properties. Specifically, linear scaling relationships (LSRs) and Brønsted–Evans–Polanyi (BEP) relationships correlate adsorption energies between adsorbates across surfaces and reaction energies with activation barriers, respectively. Although vibrational scaling relations (VSRs) exist between adsorbates at identical sites, scaling between vibrational frequencies of adsorbed local minima and transition states is lacking. Here, we present density functional theory calculations for AH X (A = C, N, O) diffusions on transition metal surfaces and reveal linear scaling between frequencies of local minima and the transition state between those minima. Using d-band theory and linear muffin tin orbital theory (LMTO), we derive the slopes of these transition state vibrational scaling relations (TSVSRs) and, in so doing, provide a rigorous theory extending the original BEP relations developed for solution chemistry to surface chemistry. Furthermore, with a single reference DFT calculation, we predict the slopes and quantify uncertainty in the predictions.
The cuprate superconductor La 2−x Ba x CuO 4 (LBCO) near x = 0.125 is a striking example of intertwined electronic orders, where 3D superconductivity is anomalously suppressed, allowing spin and charge stripe order to develop. Understanding this interplay remains a key challenge in cuprates, highlighting the necessity of external tuning for deeper insight. While in-plane uniaxial stress enhances superconductivity and suppresses stripe order, the effects of c -axis compression remains largely unexplored. Here, we use muon spin rotation ( μ SR) and AC susceptibility with an in situ piezoelectric stress device to investigate the spin-stripe order and superconductivity in LBCO-0.115 under c-axis compression. The measurements reveal a gradual suppression of the superconducting transition temperature ( T c ) with increasing c -axis stress, in stark contrast to the strong enhancement observed under in-plane stress. We further show that while in-plane stress rapidly reduces both the magnetic volume fraction ( V m ) and the spin-stripe ordering temperature ( T so ), c-axis compression has no effect, with V m and T so exhibiting an almost unchanged behavior up to the highest applied stress of 0.21 GPa. These findings demonstrate a strong anisotropy in stress response.
Polymer nanocomposite films are of great interest due to their enhanced properties over base polymers. By incorporating 2D titanium carbide a representative of a new family of 2D materials, MXenes, as nanofillers into a water soluble polyacrylamide (PAM) matrix, the resulting films benefit from the flexibility, robustness, and processability of PAM, as well as the conductivity and mechanical properties of MXene fillers. We report on manufacturing and characterization of MXene-PAM nanocomposite films. Dimethylsulfoxide (DMSO) intercalation in-between the Ti 3 C 2 -based MXene layers led to full delamination of the MXene layers and hence a uniform dispersion of hydrophilic MXene nanosheets in aqueous PAM solutions was achieved. Moreover, the polymer composite solutions of up to 75 wt.% MXene loading were sonicated and cast onto large Teflon trays and dried at room temperature to produce shiny black films. The observation of reduced 0002 peaks of Ti 3 C 2 T x phase in X-ray diffraction patterns and TEM images indicate the presence of well dispersed nanoflakes. The as-prepared composite films are flexible and the conductivity was increased significantly to 3 x 10 -3 S cm -1 for 6 wt. % MXene-PAM films. With high MXene loading, some non-uniformity between the top and bottom surfaces was observed. This could be due to the segregation of MXene layers in composite films during drying. Finally, the power law dependence of conductivity above the percolation threshold is presented through detailed conductivity measurements.
The pseudogap phenomena have been a long-standing mystery of the cuprate high-temperature superconductors. The pseudogap in the electron-doped cuprates has been attributed to band folding due to antiferromagnetic (AFM) long-range order or short-range correlation. We performed an angle-resolved photoemission spectroscopy study of the electron-doped cuprates Pr 1.3−x La 0.7 Ce x CuO 4 showing spin-glass, disordered AFM behaviors, and superconductivity at low temperatures and, by measurements with fine momentum cuts, found that the gap opens on the unfolded Fermi surface rather than the AFM Brillouin zone boundary. The gap did not show a node, following the full symmetry of the Brillouin zone, and its magnitude decreased from the zone-diagonal to (π,0) directions, opposite to the hole-doped case. These observations were reproduced by cluster dynamical-mean-field-theory calculation, which took into account electron correlation precisely within a (CuO 2 ) 4 cluster. In conclusion, the present experimental and theoretical results are consistent with the mechanism that electron or hole doping into a Mott insulator creates an in-gap band that is separated from the upper or lower Hubbard band by the pseudogap.
Abstract Renewable fuel generation is essential for a low carbon footprint economy. Thus, over the last five decades, a significant effort has been dedicated towards increasing the performance of solar fuels generating devices. Specifically, the solar to hydrogen efficiency of photoelectrochemical cells has progressed steadily towards its fundamental limit, and the faradaic efficiency towards valuable products in CO 2 reduction systems has increased dramatically. However, there are still numerous scientific and engineering challenges that must be overcame in order to turn solar fuels into a viable technology. At the electrode and device level, the conversion efficiency, stability and products selectivity must be increased significantly. Meanwhile, these performance metrics must be maintained when scaling up devices and systems while maintaining an acceptable cost and carbon footprint. This roadmap surveys different aspects of this endeavor: system benchmarking, device scaling, various approaches for photoelectrodes design, materials discovery, and catalysis. Each of the sections in the roadmap focuses on a single topic, discussing the state of the art, the key challenges and advancements required to meet them. The roadmap can be used as a guide for researchers and funding agencies highlighting the most pressing needs of the field.
Transient phenomena and their control are of high relevance in magnetic confinement fusion plasmas to guarantee a stable and safe plasma operation. Interpretative simulations can maximize the insights gained from experiments on present machines and predictive simulations can help in the preparation of design, mitigation techniques and operational scenarios for future devices. In this article, we provide an overview of recent advances and novel scientific results obtained with the 3D non-linear hybrid fluid-kinetic code JOREK, covering physics of plasma transients from the core to the scrape-off layer (SOL) both for tokamak and stellarator devices. Substantial progress was made in the physics understanding, model validation with experiments and experiment interpretation, thus, giving confidence for predictions to devices like DTT, ITER and DEMO. The topics addressed comprise a wide range: the edge physics of new operation scenarios and edge localized mode suppression; major disruptions with a focus on runaway electrons and vertical displacement events as well as disruption mitigation by shattered pellet injection; the physics mechanisms and operational limits of the flux pumping regime for sawtooth control; MHD limits of stellarators and work towards incorporating advanced edge/SOL/exhaust dynamics; continuing improvements of the code for more efficient hybrid simulations on conventional and accelerated high performance computing architectures.
Electronic structure of high-temperature superconducting cuprates is studied by analyzing experimental data independently obtained from two complementary spectroscopies: one, quasiparticle interference (QPI) measured by scanning-tunneling microscopy, and the other, angle-resolved photoemission spectroscopy (ARPES). We combine these two sets of data in a unified theoretical analysis. Through explicit calculations of experimentally measurable quantities, we show that a simple two-component fermion model (TCFM) representing electron fractionalization succeeds in reproducing various detailed features of these experimental data: ARPES and QPI data are concomitantly reproduced by the TCFM in full energy and momentum spaces. The measured QPI pattern reveals a signature characteristic of the TCFM, distinct from the conventional single-component prediction, supporting the validity of the electron fractionalization in the cuprates. The integrated analysis also solves the puzzles of ARPES and QPI data that are seemingly inconsistent with each other. The overall success of the TCFM offers a comprehensive understanding of the electronic structure of the cuprates, in particular, the unoccupied side of the spectra, of which momentum-resolved structure has long been unexplored experimentally. We further predict that a characteristic QPI pattern should appear in the unoccupied high-energy part if the fractionalization is at work. We propose that integrated-spectroscopy analyses offer a promising way to explore challenging issues of strongly correlated electron systems.
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The k-ε turbulence model has been described as perhaps “the most widely used complete turbulence model.” This family of heuristic Reynolds Averaged Navier-Stokes (RANS) turbulence closures is supported by a suite of model parameters that have been estimated by demanding the satisfaction of well-established canonical flows such as homogeneous shear flow, log-law behavior, etc. While this procedure does yield a set of so-called nominal parameters, it is abundantly clear that they do not provide a universally satisfactory turbulence model that is capable of simulating complex flows. Recent work on the Bayesian calibration of the k-ε model using jet-in-crossflow wind tunnel data has yielded parameter estimates that are far more predictive than nominal parameter values. In this paper, we develop a self-similar asymptotic solution for axisymmetric jet-in-crossflow interactions and derive analytical estimates of the parameters that were inferred using Bayesian calibration. The self-similar method utilizes a near field approach to estimate the turbulence model parameters while retaining the classical far-field scaling to model flow field quantities. Our parameter values are seen to be far more predictive than the nominal values, as checked using RANS simulations and experimental measurements. They are also closer to the Bayesian estimates than the nominal parameters. A traditional simplified jet trajectory model is explicitly related to the turbulence model parameters and is shown to yield good agreement with measurement when utilizing the analytical derived turbulence model coefficients. Finally, the close agreement between the turbulence model coefficients obtained via Bayesian calibration and the analytically estimated coefficients derived in this paper is consistent with the contention that the Bayesian calibration approach is firmly rooted in the underlying physical description.
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A key aspect of the search for Earth-like exoplanets with direct imaging is determining if the exoplanet is in the habitable zone. Future direct imaging mission concepts such as HabEx and LUVOIR require an efficient cadence of observations. Previous work shows that a minimum of three epochs, spanning more than half a period, can determine orbital parameters to 10\% for a single, circular orbit. Multi-planet systems may require a different number and cadence of observations. We begin to address the multi-planet minimum observation approach by considering only the astrometric data of exoplanet candidate objects in high contrast images. Existing multi-planet trajectory matching libraries such as “Orbits For The Impatient” (OFTI) currently require users to specify which point sources belong to which planet and assumes that the user has already matched true-positive detections to planets. Additionally, planet matching needs to be considered when assessing the impact of observation scheduling on trajectory estimation accuracy. To address this need for fitting orbits to multiple objects with limited knowledge, we present an approach using a Monte Carlo study of different observation schedules and planetary systems in which we have developed an algorithm to automatically match observations to planets, and then check the accuracy of the matches. With a large number of cases, we can constrain the number of observations and the spacing necessary to “deconfuse” the detections. We present preliminary planet matching success rates for different observing schedules and planetary system parameters. We use these results to assess the scope of the confusion problem and discuss potential mitigation strategies.
The plasma membrane proton pump is the primary energy transducing, electrogenic ion pump of the plasma membrane in plants and fungi. Compared to its fungal counterpart, the plant plasma membrane proton pump's regulatory C‐terminal domain (CTD) contains an additional regulatory segment that links multiple sensory pathways regulating plant cell length through phosphorylation and recruitment of regulatory 14‐3‐3 proteins. However, a complete structural model of a plant proton pump is lacking. Here, we performed covalent labeling with mass spectrometric analysis (CL‐MS) on the Arabidopsis pump AHA2 to identify potential interactions between the CTD and the catalytic domains. Our results suggest that autoinhibition in the plant enzyme is much more structurally complex than in the fungal enzyme.
The design of polymeric semiconductors exhibiting high electrical conductivity (σ) and thermoelectric power factor (PF) will be vital for flexible large-area electronics. In this work, four polymers based on diketopyrrolopyrrole (DPP), 2,3-dihydrothieno[3,4-b][1,4]dioxine (EDOT), thieno[3,2-b]thiophene (TT), and 3, 3'-bis (2-(2-(2-methoxyethoxy) ethoxy) ethoxy)-2, 2'-bithiophene (MEET) are investigated as side-chains, with the MEET polymers newly synthesized for this study. These polymers are systematically doped with tetrafluorotetracyanoquinodimethane ( F 4 TCNQ), CF3SO3H, and the synthesized dopant Cp(CN) 3 -(COOMe) 3 , differing in geometry and electron affinity. The DPP-EDOT-based polymer containing MEET as side-chains exhibits the highest conductivity (σ) ≈700 S cm–1 in this series with the acidic dopant (CF 3 SO 3 H). This polymer also shows the lowest oxidation potential by cyclic voltammetry (CV), the strongest intermolecular interactions evidenced by differential scanning calorimetry (DSC), and has the most oxygen-based functionality for possible hydrogen bonding and ionic screening. Other polymers exhibit high σ ≈300–500 S cm–1 and power factor up to 300 µW m –1 K –2 . The mechanism of conductivity is predominantly electronic, as validated by time-dependent conductance studies and transient thermo voltage monitoring over time, including for those doped with the acid. Furthermore, these materials maintain significant thermal stability and air stability over ≈6 weeks. Density functional theory calculations reveal molecular geometries and inform about frontier energy levels. Raman spectroscopy, in conjunction with scanning electron microscopy (SEM-EDS) and x-ray diffraction, provides insight into the solid-state microstructure and degree of phase separation of the doped polymer films. Infrared spectroscopy enables this study to further quantify the degree of charge transfer from polymer to dopant.
Abstract The chemical composition of growing media is a key factor for plant growth, impacting agricultural yield and sustainability. However, there is a lack of affordable chemical sensors for ubiquitous nutrient ion monitoring in agricultural applications. This work investigates using fully printed ion‐sensor arrays to measure the concentrations of nitrate, ammonium, and potassium in mixed‐electrolyte media. Ion sensor arrays composed of nitrate, ammonium, and potassium ion‐selective electrodes and a printed silver‐silver chloride (Ag/AgCl) reference electrode are fabricated and characterized in aqueous solutions in a range of concentrations that encompass what is typical for agricultural growing media (0.01 m m –1 m ). The sensors are also tested in mixed‐electrolyte solutions of NaNO 3 , NH 4 Cl, and KCl of varying concentrations, and the recorded potentials are input into Nernstian and artificial neural network models to compare the prediction accuracy of the models against ground truth. The artificial neural network models demonstrated higher accuracy over the Nernstian model, and the model using only ion‐sensor inputs is 7.5% more accurate than the Nernstian model under the same conditions. By enabling more precise and efficient fertilizer application, these sensor arrays coupled to computational models can help increase crop yields, optimize resource use, and reduce environmental impact.