Spin transport and accumulation in the persistent photoconductor Al0.3_{0.3}Ga0.7_{0.7}As

Electrical spin transport and accumulation have been measured in highly Si doped Al0.3Ga0.7As utilizing a lateral spin transport device. Persistent photoconductivity allows for the tuning of the effective carrier density of the channel material in situ via photodoping. Hanle effect measurements are completed at various carrier densities and the measurements yield spin lifetimes on the order of nanoseconds, an order of magnitude smaller than in bulk GaAs. These measurements illustrate that this methodology can be used to obtain a detailed description of how spin lifetimes depend on carrier density in semiconductors across the metal-insulator transition

Competition adsorption of malachite green and rhodamine B on polyethylene and polyvinyl chloride microplastics in aqueous environment

Microplastics (MPs) will cause compound pollution by combining with organic pollutants in the aqueous environment. It is important for environmental protection to study the adsorption mechanism of different MPs for pollutants. In this study, the adsorption behaviors of malachite green (MG) and rhodamine B (RhB) on polyethylene (PE) and polyvinyl chloride (PVC) were studied in single systems and binary systems, separately. The results show that in single system, the adsorptions of between MPs for pollutants (MG and RhB) are more consistent with the pseudo-second-order kinetics and Freundlich isotherm model, the adsorption capacity of both MPs for MG is greater than that of RhB. The adsorption capacities of MG and RhB were 7.68 mg/g and 2.83 mg/g for PVC, 4.52 mg/g and 1.27 mg/g for PE. In the binary system, there exist competitive adsorption between MG and RhB on MPs. And the adsorption capacities of PVC for the two dyes are stronger than those of PE. This is attributed to the strong halogen-hydrogen bond between the two dyes and PVC, and the larger specific surface area of PVC. This study revealed the interaction and competitive adsorption mechanism between binary dyes and MPs, which is of great significance for understanding the interactions between dyes and MPs in the multi-component systems.publishedVersio

Comparing the adsorption of methyl orange and malachite green on similar yet distinct polyamide microplastics: Uncovering hydrogen bond interactions

Microplastics (MPs) and dye pollutants are widespread in aquatic environments. Here, the adsorption characteristics of anionic dye methyl orange (MO) and cationic dye malachite green (MG) on polyamide 6 (PA6) and polyamide 66 (PA66) MPs were investigated, including kinetics, isotherm equilibrium and thermodynamics. The co-adsorption of MO and MG under different pH was also evaluated. The results reveal that the adsorption process of MO and MG is suitably expounded by a pseudo-second-order kinetic model. The process can be characterized by two stages: internal diffusion and external diffusion. The isothermal adsorption equilibrium of MO and MG can be effectively described using the Langmuir model, signifying monolayer adsorption. Furthermore, the thermodynamic results indicated that the adsorption was spontaneous with exothermic and endothermic properties, respectively. The results of binary systems reveal that MO dominates the adsorption at low pH (2–5), while MG dominates at high pH (8–10). Strong competitive adsorption was observed between MO and MG in neutral conditions (pH 6–8). The desorption experiments confirm that PA6 and PA66 could serve as potential carriers of MO and MG. The interaction between dyes and polyamide MPs is primarily mediated through hydrogen bonds and electrostatic attraction. The results reveal that PA6 formed more hydrogen bonds with the dyes, resulting in higher adsorption capacity than that of PA66. This difference can be attributed to the disparities in the synthesis process and polymerization method. Our study uncovered the adsorption mechanism of dye pollutants on PA6 and PA66, and provided a more comprehensive theoretical basis for the risk assessment concerning different types of polyamide MPs in aquatic environments.publishedVersio

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R2) as the evaluation indicator (R2: 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R2: 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment

An acoustic tracking model based on deep learning using two hydrophones and its reverberation transfer hypothesis, applied to whale tracking

Acoustic tracking of whales’ underwater cruises is essential for protecting marine ecosystems. For cetacean conservationists, fewer hydrophones will provide more convenience in capturing high-mobility whale positions. Currently, it has been possible to use two hydrophones individually to accomplish direction finding or ranging. However, traditional methods only aim at estimating one of the spatial parameters and are susceptible to the detrimental effects of reverberation superimposition. To achieve complete whale tracking under reverberant interference, in this study, an intelligent acoustic tracking model (CIAT) is proposed, which allows both horizontal direction discrimination and distance/depth perception by mining unpredictable features of position information directly from the received signals of two hydrophones. Specifically, the horizontal direction is discriminated by an enhanced cross-spectral analysis to make full use of the exact frequency of received signals and eliminate the interference of non-source signals, and the distance/depth direction combines convolutional neural network (CNN) with transfer learning to address the adverse effects caused by unavoidable acoustic reflections and reverberation superposition. Experiments with real recordings show that 0.13 km/MAE is achieved within 8 km. Our work not only provides satisfactory prediction performance, but also effectively avoids the reverberation effect of long-distance signal propagation, opening up a new avenue for underwater target tracking

Design of a flexure-based mechanism possessing low stiffness and constant force

This paper presents a novel design of a flexure-based constant force mechanism with a long travel stroke. Unlike the conventional force control method using a force sensor and feedback controller to obtain constant force output, the proposed compliant mechanism provides a constant force utilizing the unique mechanical property of the mechanical structure. The constant force is generated by using the combination of a negative-stiffness and a positive-stiffness mechanism. In order to achieve a low driving force, the negative stiffness is realized by a special bistable beam, which is a step beam with structural holes. Meanwhile, the positive stiffness is generated by the leaf flexure hinges with structural holes. The regular structural holes can reduce the mass and stiffness of the whole mechanism. Furthermore, the elliptic integral method and the pseudo-rigid-body approach are utilized to establish the model of the constant force mechanism. Based on the established model, the performance of the constant force mechanism is evaluated computationally. Additionally, the parametric model of the proposed mechanism is investigated using the nonlinear finite element analysis. Finally, a prototype is fabricated using 3D printing technique. The open-loop and the closed-loop experimental tests are implemented to investigate the performance of the developed constant force mechanism. It is noted that the constant force mechanism can be robustly controlled by a proportional-integral-derivative control method. Experimental results demonstrate that the developed constant force mechanism has a constant force with slight fluctuation with a range of 500 μm

Design of a novel 3D tip-based nanofabrication system with high precision depth control capability

The design, analysis, and experimental investigation of a novel 3D tip-based nanofabrication system with high precision depth control capability is presented in this paper. Based on this system, a new depth control method, namely tip displacement-based closed-loop (DC) depth control methodology is proposed to improve the depth control capability. As the force-depth prediction with the commonly-used depth control method, i.e. the normal force-based closed-loop (FC) method, may depend on the machining speed, the machining direction, and the material properties, etc. Compared with the FC method, the DC method decreases the complexity and the high uncertainty. The tip feed system utilizes a non-contact force, i.e. the electromagnetic force, to adjust the tip displacement. Therefore, the tip support mechanism can be used to accomplish the tip-sample contact detection. Additionally, an active compensation method is proposed to eliminate the tilt angle between the sample surface and the horizontal plane. Otherwise the machining depth will change gradually, i.e. getting deeper or lower. Furthermore, a series of patterns have been fabricated on silicon sample surface with the proposed system and method. The maximum machining depth of a single scan reaches 300 nm, which is much larger than that of an atomic force microscope (AFM)-based nanofabrication system. The experimental results demonstrate that the system has advantages of distinguished depth control capability, high machining accuracy, and excellent repeatability, which diminishes the influence of above-mentioned factors on the machining depth. Also, the method has the potential of machining arbitrary 2D/3D patterns with well-controlled depth and high accuracy