Superconductivity near Itinerant Ferromagnetic Quantum Criticality

Superconductivity mediated by spin fluctuations in weak and nearly ferromagnetic metals is studied close to the zero-temperature magnetic transition. We solve analytically the Eliashberg equations for p-wave pairing and obtain the normal state quasiparticle self-energy and the superconducting transition temperature $T_c$ as a function of the distance to the quantum critical point. We show that the reduction of quasiparticle coherence and life-time due to scattering by quasistatic spin fluctuations is the dominant pair-breaking process, which leads to a rapid suppression of $T_c$ to a nonzero value near the quantum critical point. We point out the differences and the similarities of the problem to that of the theory of superconductivity in the presence of paramagnetic impurities.Comment: 4 pages, 1 figure, revised version to appear in Phys. Rev. Let

Environmental impact assessment of wastewater discharge with multi-pollutants from iron and steel industry

The iron and steel industry discharges large quantities of wastewater. The environmental impact of the wastewater is traditionally assessed from the quantitative aspect. However, the water quality of discharged wastewater plays a more significant role in damaging the natural environment. Moreover, comprehensive assessment of multi-pollutants in wastewater from both quality and quantity is still a gap. In this work, a total environmental impact score (TEIS) is defined to assess the environmental impact of wastewater discharge, by considering the volume of wastewater and the quality of main processes. To implement the comprehensively qualitative and quantitative assessment, a field monitoring and measurement of wastewater discharge volume and the quality is conducted to acquire pH, suspend solids (SS), chemical oxygen demand (COD), total nitrogen (TN), total iron (TFe), and hexavalent chromium (Cr(VI)). The sequence of TEIS values is obtained as steelmaking > ironmaking > sintering > hot rolling > coking > cold rolling and TN > COD > SS > pH > Cr(VI) > TFe. The TEIS of the investigated steel plant is 26.27. The leading process lies in steelmaking with a TEIS of 19.98. The dominant pollutant is TN with a TEIS of 15.00. Finally, a sensitivity analysis is performed to validate the feasibility and generalisability of the TEIS

Development of Food-Luring Baited Traps for Solenopsis invicta (Hymenoptera: Formicidae) Monitoring in the Field in Southern China

Solenopsis invicta Buren (Hymenoptera: Formicidae), a red imported fire ant that originated from South America, is a worldwide invasive pest. This study investigated the efficacy of the newly designed baited trap to detect red imported fire ants, Solenopsis invicta Buren, under field conditions in China. Among the five food lures tested for red imported fire ants, the ants preferred ham sausage and fish powder, followed by mixed powder (50% fish powder + 50% black soldier fly powder) and black soldier fly powder. These lures were compared to sugar water (control) to determine their efficacy in trapping red imported fire ants. Field data revealed that the ham sausage powder trap was more efficient than the fish powder trap based on its ability to trap more red imported fire ants under field conditions and ease of use. Thus, it was concluded that the baited traps are efficient for longterm red imported fire ants monitoring

Mental health status and its associated factors among female nurses in the normalization of COVID-19 epidemic prevention and control in China

ObjectiveTo investigate mental health status and its associated factors among female nurses in the normalization of COVID-19 epidemic prevention and control in China.MethodsRandom cluster sampling was applied to recruit 740 female nurses in China. The respondents completed the survey with mobile devices. Demographic questionnaire, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Insomnia Severity Index, and The Impact of Event Scale-Revised were used to assess demographic Information, anxiety, depression, insomnia and PTSD symptoms, respectively. The associated factors of mental health status were identified by binary logistic regression analysis.ResultsThe prevalence of anxiety and depression was 7.9 and 17.8%, respectively. Insomnia was an associated factor of anxiety (OR = 6.237, 95%CI = 6.055–23.761, P < 0.001) and depression (OR = 9.651, 95%CI = 5.699–22.370, P < 0.001), while PTSD was an associated factor of anxiety (OR = 11.995, 95%CI = 2.946–13.205, P < 0.001) and depression (OR = 11.291, 95%CI = 6.056–15.380, P < 0.001), Being married was a protective factor of depression (OR = 0.811, 95%CI = 1.309–6.039, P < 0.01).ConclusionFemale nurses showed problems in mental health. Insomnia, PTSD and marital status were associated with mental health. The hospital management should pay more attention to the unmarried groups, and strive to improve the sleep quality of female nurses and reduce their stress caused by traumatic events

van Hove Singularity-Driven Emergence of Multiple Flat Bands in Kagome Superconductors

The newly discovered Kagome superconductors AV$_3$Sb$_5$ (A=K, Rb and Cs) continue to bring surprises in generating unusual phenomena and physical properties, including anomalous Hall effect, unconventional charge density wave, electronic nematicity and time-reversal symmetry breaking. Here we report an unexpected emergence of multiple flat bands in the AV$_3$Sb$_5$ superconductors. By performing high-resolution angle-resolved photoemission (ARPES) measurements, we observed four branches of flat bands that span over the entire momentum space. The appearance of the flat bands is not anticipated from the band structure calculations and cannot be accounted for by the known mechanisms of flat band generation. It is intimately related to the evolution of van Hove singularities. It is for the first time to observe such emergence of multiple flat bands in solid materials. Our findings provide new insights in revealing the underlying mechanism that governs the unusual behaviors in the Kagome superconductors. They also provide a new pathway in producing flat bands and set a platform to study the flat bands related physics.Comment: 20 pages, 4 figure

Benchmark study of run-to-run controllers for the lithographic control of the critical dimension

The article of record as published may be found at http://dx.doi.org/10.1117/1.2743657We present a systematic robustness analysis for several feedback controllers used in photolithographic critical dimension CD control in semiconductor manufacturing. Our study includes several controllers based on either the exponentially weighted moving average EWMA estimation or Kalman filters. The robustness is characterized by two features, namely the controller’s stability margin in the presence of model mismatch and the controller’s sensitivity to unknown noise. Simulations on the closed-loop control system are shown for the performance comparison. Both the analysis and the simulations prove that the multiple-dimensional feedback controller developed in this paper using the average of previous inputs and outputs outperforms the other controllers in the group

A three tier cooperative control architecture for multi-step semiconductor manufacturing proces

The article of record as published may be found at http://dx.doi.org/10.1016/j.jprocont.2008.04.003In this paper, cooperative control is investigated and applied to chained processes with multiple steps and multiple tools in semiconductor manufacturing. A cooperative control architecture is proposed to optimize product quality, to improve yield, to achieve best tool performance, and to minimize throughput time. The architecture consists of three tiers: the top tier for target optimization and overall product performance, the middle tier for tool selection based on tool performance, throughput time and tool availability, and the bottom tier for tool level run-to-run control. Large data sets are collected from four individual process steps in a fabrication facility of a leading semiconductor manufacturer and the data sets are processed and lined up for the study of cooperative control. Monte Carlo simulations are carried out based on the real data to demonstrate a significant improvement for the end-of-line product qualit

Pore-structure-enhanced electrochemical reduction of CO2 to formate on Sn-based double-layer catalysts

Sn-based materials can be used as electrocatalysts for the CO2 electroreduction reaction (CO2RR), preferentially producing formate. Although some Sn-based catalysts with a high faradaic efficiency for formate have been reported, the sensitivity of CO2RR activity to the catalyst structure remains elusive. Herein, a correlation between CO2RR activity and the geometric configuration of Sn-based catalysts was discovered using both double-shell SnOx nanospheres with apertures of different sizes, and CO2RR simulations using a three-step mechanism model. The kinetics analysis and simulation results suggest that a high loading of intermediate CO2,ads is the key to achieving high CO2RR performance with production of formate in the potential range −0.89 V ~ −1.26 V (vs. RHE). This understanding led to the design of double-shell SnOx nanospheres with enclosed apertures to increase the mesoporosity of the structure and hence its CO2 adsorption capability. Such a mechanism-guided approach to the design of catalysts not only enables a deep understanding of the CO2RR kinetics, but also sets a clear direction for the design of catalysts for scaled CO2RR applications

Kernel Density Derivative Estimation of Euler Solutions

Conventional Euler deconvolution is widely used for interpreting profile, grid, and ungridded potential field data. The Tensor Euler deconvolution applies additional constraints to the Euler solution using all gravity vectors and the full gravity gradient tensor. These algorithms use a series of different-sized moving windows to yield many solutions that can be employed to estimate the source location from the entire survey area. However, traditional discrimination techniques ignore the interrelation among the Euler solutions, so they cannot be employed to separate adjacent targets. To overcome this difficulty, we introduced multivariate Kernel Density Derivative Estimation (KDDE) as an extension of Kernel Density Estimation, which is a mathematical process to estimate the probability density function of a random variable. The multivariate KDDE was tested on a single cube model, a single cylinder model, and three composite models consisting of two cubes with various separations using gridded data. The probability value calculated by the multivariate KDDE was used to discriminate spurious solutions from the Euler solution dataset and isolate adjacent geological sources. The method was then applied to airborne gravity data from British Columbia, Canada. Then, the results of synthetic models and field data show that the proposed method can successfully locate meaningful geological targets