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Rare earth elements (REE) are critical elements found in monazite, xenotime, and hydrated REE phosphates which typically form in hydrothermal mineral deposits. Accurate predictions of the solubility of these REE phosphates and the speciation of REE in aqueous fluids are both key to understanding the controls on the transport, fractionation, and deposition of REE in natural systems. Previous monazite and xenotime solubility experiments indicate the presence of large discrepancies between experimentally derived solubility constants versus calculated solubilities by combining different data sources for the thermodynamic properties of minerals and aqueous species at hydrothermal conditions. In this study, these discrepancies were resolved by using the program GEMSFITS to optimize the standard partial molal Gibbs energy of formation (ΔfG°298) of REE aqueous species (REE3+ and REE hydroxyl complexes) at 298.15 K and 1 bar while keeping the thermodynamic properties fixed for the REE phosphates. A comprehensive experimental database was compiled using solubility data available between 25 and 300 °C. The latter permits conducting thermodynamic parameter optimization of ΔfG°298 for REE aqueous species. Optimal matching of the rhabdophane solubility data between 25 and 100 °C requires modifying the ΔfG°298 values of REE3+ by 1–6 kJ/mol, whereas matching of the monazite solubility data between 100 and 300 °C requires modifying the ΔfG°298 values of both REE3+ and REEOH2+ by ∼ 2–10 kJ/mol and ∼ 15–31 kJ/mol, respectively. For xenotime, adjustments of ΔfG°298 values by 1–26 kJ/mol are only necessary for the REE3+ species. The optimizations indicate that the solubility of monazite in acidic solutions is controlled by the light (L)REE3+ species at <150 °C and the LREEOH2+ species at >150 °C, whereas the solubility of xenotime is controlled by the heavy (H)REE3+ species between 25 and 300 °C. Based on the optimization results, we conclude that the revised Helgeson-Kirkham-Flowers equation of state does not reliably predict the thermodynamic properties of REE3+, REEOH2+, and likely other REE hydroxyl species at hydrothermal conditions. We therefore provide an experimental database (ThermoExp_REE) as a basic framework for future updates, extensions with other ligands, and optimizations as new experimental REE data become available. The optimized thermodynamic properties of aqueous species and minerals are available open access to accurately predict the solubility of REE phosphates in fluid-rock systems. 

With the rapid growth of large language models, big data, and malicious online attacks, it has become increasingly important to have tools for anomaly detection that can distinguish machine from human, fair from unfair, and dangerous from safe. Prior work has shown that two-distribution (specified complexity) hypothesis tests are useful tools for such tasks, aiding in detecting bias in datasets and providing artificial agents with the ability to recognize artifacts that are likely to have been designed by humans and pose a threat. However, existing work on two-distribution hypothesis tests requires exact values for the specification function, which can often be costly or impossible to compute. In this work, we prove novel finite-sample bounds that allow for two-distribution hypothesis tests with only estimates of required quantities, such as specification function values. Significantly, the resulting bounds do not require knowledge of the true distribution, distinguishing them from traditional p-values. We apply our bounds to detect student cheating on multiple-choice tests, as an example where the exact specification function is unknown. We additionally apply our results to detect representational bias in machine-learning datasets and provide artificial agents with intention perception, showing that our results are consistent with prior work despite only requiring a finite sample of the space. Finally, we discuss additional applications and provide guidance for those applying these bounds to their own work. 

We show that the base polytopeP_Mof any paving matroidMcan be systematically obtained from a hypersimplex by slicing off certain subpolytopes, namely base polytopes of lattice path matroids corresponding to panhandle-shaped Ferrers diagrams. We calculate the Ehrhart polynomials of these matroids and consequently write down the Ehrhart polynomial ofP_M, starting with Katzman’s formula for the Ehrhart polynomial of a hypersimplex. The method builds on and generalizes Ferroni’s work on sparse paving matroids. Combinatorially, our construction corresponds to constructing a uniform matroid from a paving matroid by iterating the operation ofstressed-hyperplane relaxationintroduced by Ferroni, Nasr and Vecchi, which generalizes the standard matroid-theoretic notion of circuit-hyperplane relaxation. We present evidence that panhandle matroids are Ehrhart positive and describe a conjectured combinatorial formula involving chain forests and Eulerian numbers from which Ehrhart positivity of panhandle matroids will follow. As an application of the main result, we calculate the Ehrhart polynomials of matroids associated with Steiner systems and finite projective planes, and show that they depend only on their design-theoretic parameters: for example, while projective planes of the same order need not have isomorphic matroids, their base polytopes must be Ehrhart equivalent.

 

Abstract Organic electronics can be biocompatible and conformable, enhancing the ability to interface with tissue. However, the limitations of speed and integration have, thus far, necessitated reliance on silicon-based technologies for advanced processing, data transmission and device powering. Here we create a stand-alone, conformable, fully organic bioelectronic device capable of realizing these functions. This device, vertical internal ion-gated organic electrochemical transistor (vIGT), is based on a transistor architecture that incorporates a vertical channel and a miniaturized hydration access conduit to enable megahertz-signal-range operation within densely packed integrated arrays in the absence of crosstalk. These transistors demonstrated long-term stability in physiologic media, and were used to generate high-performance integrated circuits. We leveraged the high-speed and low-voltage operation of vertical internal ion-gated organic electrochemical transistors to develop alternating-current-powered conformable circuitry to acquire and wirelessly communicate signals. The resultant stand-alone device was implanted in freely moving rodents to acquire, process and transmit neurophysiologic brain signals. Such fully organic devices have the potential to expand the utility and accessibility of bioelectronics to a wide range of clinical and societal applications. 

Observed scatter in the Lyαopacity of quasar sightlines atz< 6 has motivated measurements of the correlation between Lyαopacity and galaxy density, as models that predict this scatter make strong and sometimes opposite predictions for how they should be related. Our previous work associated two highly opaque Lyαtroughs atz∼ 5.7 with a deficit of Lyαemitting galaxies (LAEs). In this work, we survey two of the most highly transmissive lines of sight at this redshift toward thez= 6.02 quasar SDSS J1306+0356 and thez= 6.17 quasar PSO J359-06. We find that both fields are underdense in LAEs within 10h⁻¹Mpc of the quasar sightline, somewhat less extensive than underdensities associated with Lyαtroughs. We combine our observations with three additional fields from the literature and find that while fields with extreme opacities are generally underdense, moderate opacities span a wider density range. The results at high opacities are consistent with models that invoke UV background fluctuations and/or late reionization to explain the observed scatter in intergalactic medium (IGM) Lyαopacities. There is tension at low opacities, however, as the models tend to associate lower IGM Lyαopacities with higher densities. Although the number of fields surveyed is still small, the low-opacity results may support a scenario in which the ionizing background in low-density regions increases more rapidly than some models suggest after becoming ionized. Elevated gas temperatures from recent reionization may also be making these regions more transparent.

 

Abstract The first stars were born from chemically pristine gas. They were likely massive, and thus they rapidly exploded as supernovae, enriching the surrounding gas with the first heavy elements. In the Local Group, the chemical signatures of the first stellar population were identified among low-mass, long-lived, very metal-poor ([Fe/H] < −2) stars, characterized by high abundances of carbon over iron ([C/Fe] > +0.7): the so-called carbon-enhanced metal-poor stars. Conversely, a similar carbon excess caused by first-star pollution was not found in dense neutral gas traced by absorption systems at different cosmic time. Here we present the detection of 14 very metal-poor, optically thick absorbers at redshift z ∼ 3–4. Among these, 3 are carbon-enhanced and reveal an overabundance with respect to Fe of all the analyzed chemical elements (O, Mg, Al, and Si). Their relative abundances show a distribution with respect to [Fe/H] that is in very good agreement with those observed in nearby very metal-poor stars. All the tests we performed support the idea that these C-rich absorbers preserve the chemical yields of the first stars. Our new findings suggest that the first-star signatures can survive in optically thick but relatively diffuse absorbers, which are not sufficiently dense to sustain star formation and hence are not dominated by the chemical products of normal stars. 

Previous work has shown that artificial agents with the ability to discern function from structure (intention perception) in simple combinatorial machines possess a survival advantage over those that cannot. We seek to examine the strength of the relationship between structure and function in these cases. To do so, we use genetic algorithms to generate simple combinatorial machines (in this case, traps for artificial gophers). Specifically, we generate traps both with and without structure and function, and examine the correlation between trap coherence and lethality, the capacity of genetic algorithms to generate lethal and coherent traps, and the information resources necessary for genetic algorithms to create traps with specified traits. We then use the traps generated by the genetic algorithms to see if artificial agents with intention perception still possess a survival advantage over those that do not. Our findings are two-fold. First, we find that coherence (structure) is much harder to achieve than lethality (function) and that optimizing for one does not beget the other. Second, we find that agents with intention perception do not possess strong survival advantages when faced with traps generated by a genetic algorithm. 

Previous work has shown that artificial agents with the ability to discern function from structure (intention perception) in simple combinatorial machines possess a survival advantage over those that cannot. We seek to examine the strength of the relationship between structure and function in these cases. To do so, we use genetic algorithms to generate simple combinatorial machines (in this case, traps for artificial gophers). Specifically, we generate traps both with and without structure and function, and examine the correlation between trap coherence and lethality, the capacity of genetic algorithms to generate lethal and coherent traps, and the information resources necessary for genetic algorithms to create traps with specified traits. We then use the traps generated by the genetic algorithms to see if artificial agents with intention perception still possess a survival advantage over those that do not. Our findings are two-fold. First, we find that coherence (structure) is much harder to achieve than lethality (function) and that optimizing for one does not beget the other. Second, we find that agents with intention perception do not possess strong survival advantages when faced with traps generated by a genetic algorithm.