Critical Crossover Between Yosida-Kondo Dominant Regime and Magnetic Frustration Dominant Regime in the System of a Magnetic Trimer on a Metal Surface

Quantum Monte Carlo simulations were carried out for the system of a magnetic trimer on a metal surface. The magnetic trimer is arranged in two geometric configurations, viz., isosceles and equilateral triangles. The calculated spectral density and magnetic susceptibility show the existence of two phases: Yosida-Kondo dominant phase and magnetic frustration dominant phase. Furthermore, a critical transition between these two phases can be induced by changing the configuration of the magnetic trimers from isosceles to equilateral triangle.Comment: 8 pages, 4 figures; accepted for publication in J. Phys. Soc. Jp

Miniaturized multisensor system with a thermal gradient: Performance beyond the calibration range

Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensing material (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as a nano-electronic nose to distinguish five different gases and estimate their concentrations. This innovative approach uses identical sensors working at different operating temperatures thanks to the thermal gradient created by an integrated microheater. A system with in-house developed hardware and software was used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitative discrimination of all gases after calibration. The system worked perfectly within the calibrated range (100% correct classification, 6.9% average error on concentration value). This work focuses on minimizing the number of points needed for calibration while maintaining good sensor performance, both for classification and error in estimating concentration. Therefore, the calibration range (in terms of gas concentration) was gradually reduced and further tests were performed with concentrations outside these new reduced limits. Although with only a few training points, down to just two per gas, the system performed well with 96% correct classifications and 31.7% average error for the gases at concentrations up to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower than the calibration range, the system worked less well, with 93% correct classifications and 38.6% average error, probably due to proximity to the limit of detection of the sensors

Electronic noses based on metal oxide nanowires: A review

Metal oxides are ideal for the fabrication of gas sensors: they are sensitive to many gases while allowing the device to be simple, tiny, and inexpensive. Nonetheless, their lack of selectivity remains a limitation. In order to achieve good selectivity in applications with many possible interfering gases, the sensors are inserted into an electronic nose that combines the signals from nonselective sensors and analyzes them with multivariate statistical algorithms in order to obtain selectivity. This review analyzes the scientific articles published in the last decade regarding electronic noses based on metal oxide nanowires. After a general introduction, Section 2 discusses the issues related to poor intrinsic selectivity. Section 3 briefly reviews the main algorithms that have hitherto been used and the results they can provide. Section 4 classifies the recent literature into fundamental research, agrifood, health, security. In Section 5, the literature is analyzed regarding the metal oxides, the surface decoration nanoparticles, the features that differentiate the sensors in a given array, the application for which the device was developed, the algorithm used, and the type of information obtained. Section 6 concludes by discussing the present state and points out the requirements for their use in real-world applications

BIOLOGICAL AMMONIA REMOVAL BY SUBMERGED AERATED FILTER FROM HANOI GROUND WATER

Joint Research on Environmental Science and Technology for the Eart

Flocculation of Reactive Blue 19 (RB19) using Alum and the Effects of Catalysts Addition

There are a variety of primary coagulants which can be used in a water treatment plant. One of the earliest, and still the most extensively used, is aluminum sulfate, also known as alum. Aluminum Sulfate (Alum) is one of the most commonly used flocculent in waste water treatment processes. Effectiveness of Alum in flocculation process is determined by many factors such as the effluents pH, flocculent dose as well as the use of catalyst to improve efficiency rate of flocculation. Hence special attention to these factors especially the use of catalyst has been brought about by this study. Experiments were carried out using Reactive Blue 19 Dye as the contaminant of waste water and two catalysts namely Calcium Hydroxide (CaOH2) and Poly Aluminum Chloride (PACl) were evaluated. The results obtained proved that indeed after addition of catalysts, removal efficiency rates of Alum can be increased up to 25% using Calcium Hydroxide and up to 35% using Poly Aluminum Chloride compared to Alum alone. The optimum conditions for this study were at pH 5.5 ~7.5, 300 mg/L of Alum 30seconds of rapid mixing time with 300 rpm , 30rpm of mixing rate for 5 minutes and 30 minutes of settling time. Moreover, Alum showed the highest performance under these conditions and using 50 mg/L PACl as catalyst with 98.52% of COD reduction and 90.60% of color reduction. In conclusion, Alum with the support of PACl as catalyst is an effective coagulant, which can reduce the level of COD and Dye Color in Reactive Blue 19 contaminated wastewater

Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification

Classification of different gases is important, and it is possible to use different gas sensors for this purpose. Electronic noses, for example, combine separated gas sensors into an array for detecting different gases. However, the use of separated sensors in an array suffers from being bulky, high-energy consumption and complex fabrication processes. Generally, gas sensing properties, including gas selectivity, of semiconductor gas sensors are strongly dependent on their working temperature. It is therefore feasible to use a single device composed of identical sensors arranged in a temperature gradient for classification of multiple gases. Herein, we introduce a design for simple fabrication of gas sensor array based on bilayer Pt/SnO2 for real-time monitoring and classification of multiple gases. The study includes design simulation of the sensor array to find an effective gradient temperature, fabrication of the sensors and test of their performance. The array, composed of five sensors, was fabricated on a glass substrate without the need of backside etching to reduce heat loss. A SnO2 thin film sensitized with Pt on top deposited by sputtering was used as sensing material. The sensor array was tested against different gases including ethanol, methanol, isopropanol, acetone, ammonia, and hydrogen. Radar plots and principal component analysis were used to visualize the distinction of the tested gases and to enable effective classification

Isogeometric analysis of linear isotropic and kinematic hardening elastoplasticity

Material nonlinearity is of great importance in many engineering problems. In this paper, we exploit NURBS-based isogeometric approach in solving materially nonlinear problems, i.e. elastoplastic problems. The von Mises model with linear isotropic hardening and kinematic hardening is presented, and furthermore the method can also be applied to other elastoplastic models without any loss of generality. The NURBS basis functions allow us to describe exactly the curved geometry of underlying problems and control efficiently the accuracy of approximation solution. Once the discretized system of non-linear equilibrium equation is obtained, the Newton-Raphson iterative scheme is used. Several numerical examples are tested. The accuracy and reliability of the proposed method are verified by comparing with results from ANSYS Workbench software

The role of Tay indigenous knowledge in climate change adaptation in the Northern Mountainous Region of Vietnam

Through generations of observation and experimentation, the Tay people of Bac Kan Province in the Northern Mountainous Region of Vietnam have developed complex farming systems, cultural practices and an indigenous knowledge base well-suited to their environments. Drawing on data collected through surveys, interviews and focus group discussions, this article first documents some of this knowledge and its role in supporting agricultural production. However, this research also uncovered that contemporary climate change is occurring at rates faster than that knowledge base can meaningfully adjust and adapt. Agricultural productivity was found to be greatly reduced, with men seeking off-farm employment to supplement the loss in income. Agrochemical use has soared and resulted in declines in the health of the local population. Village gender dynamics have also shifted and women have taken on the extra burden of farming. This paper posits that if indigenous knowledge was better integrated into adaptation planning and policies, its conservation and application would enhance resiliency to climate change in indigenous communities and beyond. Simultaneously, it also adds that as the nature, speed and severity of climate change in many marginal areas occur at rates faster than indigenous knowledge can adapt, blended forms of knowledge may offer practical solutions

The role of Tay indigenous knowledge in climate change adaptation in the Northern Mountainous Region of Vietnam

459-472Through generations of observation and experimentation, the Tay people of Bac Kan Province in the Northern Mountainous Region of Vietnam have developed complex farming systems, cultural practices and an indigenous knowledge base well-suited to their environments. Drawing on data collected through surveys, interviews and focus group discussions, this article first documents some of this knowledge and its role in supporting agricultural production. However, this research also uncovered that contemporary climate change is occurring at rates faster than that knowledge base can meaningfully adjust and adapt. Agricultural productivity was found to be greatly reduced, with men seeking off-farm employment to supplement the loss in income. Agrochemical use has soared and resulted in declines in the health of the local population. Village gender dynamics have also shifted and women have taken on the extra burden of farming. This paper posits that if indigenous knowledge was better integrated into adaptation planning and policies, its conservation and application would enhance resiliency to climate change in indigenous communities and beyond. Simultaneously, it also adds that as the nature, speed and severity of climate change in many marginal areas occur at rates faster than indigenous knowledge can adapt, blended forms of knowledge may offer practical solutions