Doping dependence of heat transport in the iron-arsenide superconductor Ba(Fe1−x_{1-x}Cox_x)2_2As2_2: from isotropic to strongly kk-dependent gap structure

The temperature and magnetic field dependence of the in-plane thermal conductivity $\kappa$ of the iron-arsenide superconductor Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$ was measured down to $T \simeq 50$ mK and up to $H = 15$ T as a function of Co concentration $x$ in the range 0.048 $\leq x \leq$ 0.114. In zero magnetic field, a negligible residual linear term in $\kappa/T$ as $T \to 0$ at all $x$ shows that there are no zero-energy quasiparticles and hence the superconducting gap has no nodes in the $ab$-plane anywhere in the phase diagram. However, the field dependence of $\kappa$ reveals a systematic evolution of the superconducting gap with doping $x$, from large everywhere on the Fermi surface in the underdoped regime, as evidenced by a flat $\kappa (H)$ at $T \to 0$, to strongly $k$-dependent in the overdoped regime, where a small magnetic field can induce a large residual linear term, indicative of a deep minimum in the gap magnitude somewhere on the Fermi surface. This shows that the superconducting gap structure has a strongly $k$-dependent amplitude around the Fermi surface only outside the antiferromagnetic/orthorhombic phase.Comment: version accepted for publication in Physical Review Letters; new title, minor revision, revised fig.1, and updated reference

Modeling of proton-conducting solid oxide fuel cells fueled with syngas

Solid oxide fuel cells (SOFCs) with proton conducting electrolyte (H-SOFCs) are promising power sources for stationary applications. Compared with other types of fuel cells, one distinct feature of SOFC is their fuel flexibility. In this study, a 2D model is developed to investigate the transport and reaction in an H-SOFC fueled with syngas, which can be produced from conventional natural gas or renewable biomass. The model fully considers the fluid flow, mass transfer, heat transfer and reactions in the H-SOFC. Parametric studies are conducted to examine the physical and chemical processes in H-SOFC with a focus on how the operating parameters affect the H-SOFC performance. It is found that the presence of CO dilutes the concentration of H2, thus decreasing the H-SOFC performance. With typical syngas fuel, adding H2O cannot enhance the performance of the H-SOFC, although water gas shift reaction can facilitate H2 production

Nodes in the gap structure of the iron-arsenide superconductor Ba(Fe_{1-x}Co_x)_2As_2 from c-axis heat transport measurements

The thermal conductivity k of the iron-arsenide superconductor Ba(Fe_{1-x}Co_x)_2As_2 was measured down to 50 mK for a heat current parallel (k_c) and perpendicular (k_a) to the tetragonal c axis, for seven Co concentrations from underdoped to overdoped regions of the phase diagram (0.038 < x < 0.127). A residual linear term k_c0/T is observed in the T = 0 limit when the current is along the c axis, revealing the presence of nodes in the gap. Because the nodes appear as x moves away from the concentration of maximal T_c, they must be accidental, not imposed by symmetry, and are therefore compatible with an s_{+/-} state, for example. The fact that the in-plane residual linear term k_a0/T is negligible at all x implies that the nodes are located in regions of the Fermi surface that contribute strongly to c-axis conduction and very little to in-plane conduction. Application of a moderate magnetic field (e.g. H_c2/4) excites quasiparticles that conduct heat along the a axis just as well as the nodal quasiparticles conduct along the c axis. This shows that the gap must be very small (but non-zero) in regions of the Fermi surface which contribute significantly to in-plane conduction. These findings can be understood in terms of a strong k dependence of the gap Delta(k) which produces nodes on a Fermi surface sheet with pronounced c-axis dispersion and deep minima on the remaining, quasi-two-dimensional sheets.Comment: 12 pages, 13 figures

Quasiparticle Heat Transport in Ba1−x_{1-x}Kx_xFe2_2As2_2: Evidence for a k-dependent Superconducting Gap without Nodes

The thermal conductivity $\kappa$ of the iron-arsenide superconductor Ba$_{1-x}$K$_x$Fe$_2$As$_2$ ($T_c \simeq$ 30 K) was measured in single crystals at temperatures down to $T \simeq 50$ mK ($\simeq T_c$/600) and in magnetic fields up to $H = 15$ T ($\simeq H_{c2}$/4). A negligible residual linear term in $\kappa/T$ as $T \to 0$ shows that there are no zero-energy quasiparticles in the superconducting state. This rules out the existence of line and in-plane point nodes in the superconducting gap, imposing strong constraints on the symmetry of the order parameter. It excludes d-wave symmetry, drawing a clear distinction between these superconductors and the high-$T_c$ cuprates. However, the fact that a magnetic field much smaller than $H_{c2}$ can induce a residual linear term indicates that the gap must be very small on part of the Fermi surface, whether from strong anisotropy or band dependence, or both

Unravelling the spatial variation of nitrous oxide emissions from a step-feed plug-flow full scale wastewater treatment plant

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/Plug-flow activated sludge reactors (ASR) that are step-feed with wastewater are widely adopted in wastewater treatment plants (WWTPs) due to their ability to maximise the use of the organic carbon in wastewater for denitrification. Nitrous oxide (N2O) emissions are expected to vary along these reactors due to pronounced spatial variations in both biomass and substrate concentrations. However, to date, no detailed studies have characterised the impact of the step-feed configuration on emission variability. Here we report on the results from a comprehensive online N2O monitoring campaign, which used multiple gas collection hoods to simultaneously measure emission along the length of a full-scale, stepfed, plug-flow ASR in Australia. The measured N2O fluxes exhibited strong spatial-temporal variation along the reactor path. The step-feed configuration had a substantial influence on the N2O emissions, where the N2O emission factors in sections following the first and second step feed were 0.68% ± 0.09% and 3.5% ± 0.49% of the nitrogen load applied to each section. The relatively high biomass-specific nitrogen loading rate in the second section of the reactor was most likely cause of the high emissions from this section

Nodes in the gap structure of the iron arsenide superconductor Ba(Fe(1-x)Cox)(2)As-2 from c-axis heat transport measurements

The thermal conductivity κ of the iron-arsenide superconductor Ba(Fe1−xCox)2As2 was measured down to 50 mK for a heat current parallel (κc) and perpendicular (κa) to the tetragonal c axis for seven Co concentrations from underdoped to overdoped regions of the phase diagram (0.038≤x≤0.127). A residual linear term κc0/T is observed in the T→0 limit when the current is along the c axis, revealing the presence of nodes in the gap. Because the nodes appear as x moves away from the concentration of maximal Tc, they must be accidental, not imposed by symmetry, and are therefore compatible with an s± state, for example. The fact that the in-plane residual linear term κa0/T is negligible at all x implies that the nodes are located in regions of the Fermi surface that contribute strongly to c-axis conduction and very little to in-plane conduction. Application of a moderate magnetic field (e.g., Hc2/4) excites quasiparticles that conduct heat along the a axis just as well as the nodal quasiparticles conduct along the c axis. This shows that the gap must be very small (but nonzero) in regions of the Fermi surface which contribute significantly to in-plane conduction. These findings can be understood in terms of a strong k dependence of the gap Δ(k) which produces nodes on a Fermi-surface sheet with pronounced c-axis dispersion and deep minima on the remaining, quasi-two-dimensional sheets

Unravelling the spatial variation of nitrous oxide emissions from a step-feed plug-flow full scale wastewater treatment plant.

Plug-flow activated sludge reactors (ASR) that are step-feed with wastewater are widely adopted in wastewater treatment plants (WWTPs) due to their ability to maximise the use of the organic carbon in wastewater for denitrification. Nitrous oxide (N2O) emissions are expected to vary along these reactors due to pronounced spatial variations in both biomass and substrate concentrations. However, to date, no detailed studies have characterised the impact of the step-feed configuration on emission variability. Here we report on the results from a comprehensive online N2O monitoring campaign, which used multiple gas collection hoods to simultaneously measure emission along the length of a full-scale, step-fed, plug-flow ASR in Australia. The measured N2O fluxes exhibited strong spatial-temporal variation along the reactor path. The step-feed configuration had a substantial influence on the N2O emissions, where the N2O emission factors in sections following the first and second step feed were 0.68% ± 0.09% and 3.5% ± 0.49% of the nitrogen load applied to each section. The relatively high biomass-specific nitrogen loading rate in the second section of the reactor was most likely cause of the high emissions from this section

The Effect of Tantalum Incorporation on the Physical and Chemical Properties of Ternary Silicon–calcium–phosphorous Mesoporous Bioactive Glasses

Synthesis and characterization of the first mesoporous bioactive glasses (MBGs) containing tantalum are reported here, along with their potential application as hemostats. Silica MBGs were synthesized using with the molar composition of (80-x)% Si, 15% Ca, 5% P, and x% Ta. It was found that incorporation of \u3e1 mol % Ta into the MBGs changes their physical and chemical properties. Increasing Ta content from 0 to 10 mol % causes a decrease in the surface area and pore volume of ~20 and ~35%, respectively. This is due to the increase in nonbridging oxygens and mismatch of thermal expansion coefficient which created discontinuities in the ordered channel structure. However, the effect is not significant on the amount of ions (Si, Ca, P, and Ta) released, from the sample into deionized water, for short durations (\u3c60 \u3emin). In a mouse tail-cut model, a significant decrease in bleeding time (≥50% of average bleeding time) was found for Ta-MBGs compared to having no treatment, Arista, and MBG without Ta. Further studies are proposed to determine the mechanism of Ta involvement with the hemostatic process. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 2229–2237, 2019

Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies

Log data can reveal valuable information about how users interact with web search services, what they want, and how satisfied they are. However, analyzing user intents in log data is not easy, especially for new forms of web search such as AI-driven chat. To understand user intents from log data, we need a way to label them with meaningful categories that capture their diversity and dynamics. Existing methods rely on manual or ML-based labeling, which are either expensive or inflexible for large and changing datasets. We propose a novel solution using large language models (LLMs), which can generate rich and relevant concepts, descriptions, and examples for user intents. However, using LLMs to generate a user intent taxonomy and apply it to do log analysis can be problematic for two main reasons: such a taxonomy is not externally validated, and there may be an undesirable feedback loop. To overcome these issues, we propose a new methodology with human experts and assessors to verify the quality of the LLM-generated taxonomy. We also present an end-to-end pipeline that uses an LLM with human-in-the-loop to produce, refine, and use labels for user intent analysis in log data. Our method offers a scalable and adaptable way to analyze user intents in web-scale log data with minimal human effort. We demonstrate its effectiveness by uncovering new insights into user intents from search and chat logs from Bing