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The Moral Realism Debate Meets the Mathematical Realism Debate
No full textI raise objections to mathematical and moral realism according to which, respectively, some mathematical and moral statements are rendered true by mathematical and moral facts in the abstract world. I also raise objections to mathematical and moral fictionalism according to which, respectively, mathematical and moral statements are all false. I argue that just as mathematical inferentialism has advantages over mathematical realism and fictionalism, so moral functionalism has advantages over moral realism and fictionalism
Self-heating characteristics of electrically conductive cement composites with carbon black and carbon fiber
No full textThis study aimed to investigate the self-heating characteristics of electrically conductive cement composites (ECCCs) and propose an effective and affordable mix design for ECCC blocks that are applicable to the accelerated curing of concrete with carbon black and carbon fibers employed as conductive agents. Twelve mix proportions were prepared by varying the carbon black and carbon fiber contents. A voltage application protocol was designed and used to examine the self-heating capacities of the mixtures. The results show that the presence of carbon fibers was critical so that the electrical resistivities of ECCCs with 0.2 vol% carbon fibers were less than 0.16% of that without conductive agents. For a given content of carbon fiber, an increase in the carbon black content up to 0.8 vol% led to a drastic decrease in electrical resistivity, and achieved the highest average surface temperature of ECCC equal to approximately 77 ??C. However, the use of 1.2 vol% carbon black caused an increase in the electrical resistivity. Further, the trends were in accordance with the change in the dispersion degree of carbon black, as analyzed via fluorescence microscopy. Finally, two selected ECCC blocks (with 0.4 vol% carbon fiber and 0 vol% or 0.8 vol% carbon black) were tested for the accelerated curing of ordinary cement paste, charged at 25 V DC for 24 h. The cement paste cured using the blocks with 0.8 vol% carbon black attained more hydrated phases, and at least an 11% reduction in porosity at 24 h of curing
Effects of the surface ligands of quantum dots on the intaglio transfer printing process
No full textHigh-resolution semiconductor nanocrystal quantum dot (QD) patterns are required for applications in display devices. For this, the dry transfer printing of QDs is promising because it does not degrade the inherent properties of QDs. However, the effect of the surface ligands on this process remains poorly understood, despite its importance. Herein, we investigate the effect of the surface ligands on the intaglio transfer printing process. Colloidal QDs with organic (C8???C18) or inorganic (I???) ligands are prepared. In various pattern-printing tests, including patterns with a sub-10-??m width, the patterning yield is ???100% for QDs with long-chain ligands (C18). However, the patterning yield decreases with decrease in the chain length for the organic-ligand-passivated QDs and is the lowest for the QDs passivated with I???. Using surface energy characterization and finite element method simulations, we suggest two printing failure mechanisms: (i) Inorganic-ligand-passivated QDs are not effectively picked-up by the stamp because of poor adhesion, and, (ii) for the organic-ligand-passivated QDs, internal crack formation is easier for QDs having short-chain ligands because of the weak interparticle attraction between QDs. Our findings reveal previously unknown defects of the intaglio printing process and provide guidance for mitigating these problems for preparing high-resolution QD patterns
Maximum Voltage Gain Tracking Algorithm for High-Efficiency of Two-Stage Induction Heating System Using Resonant Impedance Estimation
No full textA boost power factor correction (PFC) circuit has replaced the diode rectifier to improve its poor power factor performance, low efficiency, and output power limitation for conventional induction heating (IH) applications. Accordingly, many studies have been conducted, but they considered only the efficiency of the boost PFC rather than the entire IH system, or their control and design were complicated. In this paper, an algorithm tracking the maximum voltage gain of the resonant network is proposed to improve the entire efficiency of the two-stage IH system based on an exact online resonant frequency estimation. It can make the resonant network operate at the maximum voltage gain point which can improve the efficiency of the series-resonant inverter (SRI) included in the IH system with low circulating current, the
minimum switching frequency, and zero voltage switching (ZVS) capability. The proposed algorithm also induces the minimum output voltage of the boost PFC, which can reduce its switching losses and total harmonic distortion (THD). The validity of the proposed algorithm is experimentally verified using a 2.4-kW prototype IH system, including the boost PFC and the IH-SRI controlled by a digital signal processor (DSP)
Highly Exposed -NH2 Edge on Fragmented g-C3N4 Framework with Integrated Molybdenum Atoms for Catalytic CO2 Cycloaddition: DFT and Techno-Economic Assessment
No full textThis study focuses on the applicability of single-atom Mo-doped graphitic carbon nitride (GCN) nanosheets which are specifically engineered with high surface area (exfoliated GCN), -NH2 rich edges, and maximum utilization of isolated atomic Mo for propylene carbonate (PC) production through CO2 cycloaddition of propylene oxide (PO). Various operational parameters are optimized, for example, temperature (130 degrees C), pressure (20 bar), catalyst (Mo(2)GCN), and catalyst mass (0.1 g). Under optimal conditions, 2% Mo-doped GCN (Mo(2)GCN) has the highest catalytic performance, especially the turnover frequency (TOF) obtained, 36.4 h(-1) is higher than most reported studies. DFT simulations prove the catalytic performance of Mo(2)GCN significantly decreases the activation energy barrier for PO ring-opening from 50-60 to 4.903 kcal mol(-1). Coexistence of Lewis acid/base group improves the CO2 cycloaddition performance by the formation of coordination bond between electron-deficient Mo atom with O atom of PO, while -NH2 surface group disrupts the stability of CO2 bond by donating electrons into its low-level empty orbital. Steady-state process simulation of the industrial-scale consumes 4.4 ton h(-1) of CO2 with PC production of 10.2 ton h(-1). Techno-economic assessment profit from Mo(2)GCN is estimated to be 60.39 million USD year(-1) at a catalyst loss rate of 0.01 wt% h(-1)
Skeletal Nanostructures Promoting Electrocatalytic Reactions with Three-Dimensional Frameworks
No full textHollow skeletal nanomaterials, such as nanoframes and nanocages, represent a class of advanced electrocatalysts and exhibit excellent performance in various electrochemical energy conversion reactions. Their three-dimensional (3D) framework, which allows a high surface-area-to-volume ratio, efficient molecular accessibility, and nanoscale confinement effect, leads to higher catalytic activity compared to solid nanoparticle (NP)-based catalysts without requiring the use of a significant amount of precious metal. In this Perspective, we present notable exemplars of skeletal nanostructures that have demonstrated superior activity over solid NP-based catalysts. In particular, we highlight that the 3D framework in skeletal nanostructures consists of inherently reactive catalytic surfaces and discuss a multitude of factors affecting the excellent performance of skeletal nanocatalysts. We next introduce the design strategies that promote the catalytic activity and durability of skeletal nanostructures, including the strengthening of framework structures and the reorganization of the atomic array in a skeletal nanostructure. Finally, we provide future research directions in this emerging class of catalysts
Lifetime Prediction of Silicone and Direct Ink Writing-based Soft Sensors Under Cyclic Strain
No full textThe softness and stretchability of soft sensors has generated much interest with respect to applying soft sensors for human activity monitoring and proprioception of soft robots. However, most of the research in this area has focused on electrical stability, despite the importance of mechanical failure, thus limiting practical application. In this study, the lifetime of silicone-based soft sensors was examined under accelerated cyclic strain conditions, to construct lifetime prediction models of crack nucleation and growth considering the failure properties of the sensor's silicone elastomer. To establish the models, an accelerated life test was conducted, in which the lifetime was estimated according to a Weibull distribution under accelerated cyclic strain conditions. Specifically, a lifetime prediction model using the crack growth approach (CGA) was constructed by experimentally measuring the energy release rate (tearing energy) of the silicone elastomer due to crack propagation. Compared to the inverse power law-based model, the CGA-based model showed about 90% improvement in lifetime prediction accuracy in the strain ranges from 150 to 270% with root mean square error 456 and 4592 cycles, respectively, thus indicating that tearing energy is an important parameter for sensor lifetime prediction. The proposed model is expected to be useful for predicting the lifetime of soft sensors under various strain operating conditions
SANTA: A safety analysis code for neutron absorbers in spent nuclear fuel pools
No full textRecent experimental reports on the premature corrosion of an Al-B4C neutron absorber in a spent nuclear fuel pool, which was possibly assisted by 10B(n, ??)7Li reaction-induced porous microstructures, illustrated the need for quantitative evaluation of the neutron-induced energetic particle emission reactions inside neutron absorbers. For this purpose, we developed a Safety Analysis code for NeuTron Absorbers in spent nuclear fuel pools (SANTA) by integrating several existing codes (TRITON, ORIGEN, CSAS6, and modified SDTrimSP) with newly developed modules to provide the essential parameters for the experimental emulation of irradiation-assisted corrosion of the absorbers. The most important outputs of this code are radiation damage in displacement per atom (dpa) units and He concentration in atomic ppm. These outputs are required for the design of heavy-ion irradiation experiments on the absorbers to emulate the radiation damage induced by energetic 7Li ions and ??-particles. The SANTA code is also user-friendly: its whole sequence can be executed using simplified text-based input without user intervention. This code may improve the understanding of the gas bubble formation and irradiation-assisted corrosion mechanisms of the neutron absorbers, and it could provide a foundation for the future development of more robust safety codes for spent nuclear fuel pools
Analytic ranks of elliptic curves over number fields
No full textLet E be an elliptic curve over Q. Then, we show that the average analytic rank of E over cyclic extensions of degree l over Q with l a prime not equal to 2, is at most 2+rQ(E), where rQ(E) is the analytic rank of the elliptic curve E over Q. This bound is independent of the degree l Also, we also obtain some average analytic rank results over Sd-fields
Optimal Network Protocol Selection for Competing Flows via Online Learning
No full textToday's Internet must support applications with increasingly dynamic and heterogeneous connectivity requirements, such as video streaming and the Internet of Things. Yet current network management practices generally rely on pre-specified network configurations, which may not be able to cope with dynamic application needs. Moreover, even the best-specified policies will find it difficult to cover all possible scenarios, given applications' increasing heterogeneity and dynamic network conditions, e.g., on volatile wireless links. In this work, we instead propose a model-free learning approach to find the optimal network policies for current network flow requirements. This approach is attractive as comprehensive models do not exist for how different policy choices affect flow performance under changing network conditions. However, it can raise new challenges for online learning algorithms: policy configurations can affect the performance of multiple flows sharing the same network resources, and this performance coupling limits the scalability and optimality of existing online learning algorithms. In this work, we extend multi-armed bandit frameworks to propose new online learning algorithms for protocol selection with provably sublinear regret under certain conditions. We validate the optimality and scalability of our algorithms through data-driven simulations and testbed experiments