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Development of hydroxyapatite-enhanced membrane for nanoplastics removal: Multiple scenarios and mechanism exploration

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Journal of hazardous materials 2025年 495卷 138781页

作者： Mengna Li Guohe Huang Shuguang Wang Jing Huang Zeyuan Xu Environmental Systems Engineering Program University of Regina Regina Saskatchewan S4S 0A2 Canada China-Canada Center of Energy Environment and Sustainability Research UR-SDU School of Environmental Science and Engineering Shandong University Qingdao 266237 China. China-Canada Center of Energy Environment and Sustainability Research UR-SDU School of Environmental Science and Engineering Shandong University Qingdao 266237 China. Electronic address: huangg@uregina.ca. Shandong Key Laboratory of Water Pollution Control and Resource Reuse School of Environmental Science and Engineering Shandong University Qingdao 266237 China. Environmental Systems Engineering Program University of Regina Regina Saskatchewan S4S 0A2 Canada. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia 27100 Italy.

Nanoplastics (NPs) are widespread in wastewater systems and more toxic than microplastics (MPs), necessitating effective removal techniques. Although membranes can effectively remove MPs, their efficiency and mechanisms for NPs removal require further study. A novel hydroxyapatite functionalized PVDF (HAPF) membrane was developed for NPs removal. The optimized HAPF membrane was prepared via a one-step method with 800 mg/L HAP at pH 7.3 for 8 hours. It achieved a water flux of 4376.44 LMH, 3.4 times higher than that of the pristine PVDF membrane, while maintaining a polystyrene (PS) NPs rejection above 99.5 %. The HAPF membrane maintained 95 % of its pure water flux when treating wastewater with only PS NPs, but exhibited severe flux decline under varying conditions, such as surface-functionalized PS NPs, pH changes, or coexisting contaminants. Among these, the presence of MgSO alongside PS NPs caused the most severe water flux reduction, with the HAPF membrane undergoing complete blocking, followed by the formation of a cake layer. XDLVO results revealed that there is an attractive interaction between the HAPF membrane and PS NPs, primarily driven by acid-base interaction. The HAPF membrane exhibited significantly lower R and R values than the PVDF membrane and achieved a flux recovery rate of 92.94 % over multiple fouling-cleaning cycles. Its water flux was 18.9 times that of the PVDF membrane during long-term filtration. The developed HAPF membrane holds significant potential for advanced water treatment and fouling resistance .

关键词： Coexisting pollutants Hermia theory Hydroxyapatite functionalized PVDF (HAPF) membrane Nanoplastic removal XDLVO theory

A statistical learning approach to reactive power control in distribution systems

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arXiv 2019年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Recursive network estimation from binary-valued observation data

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arXiv 2019年

作者： Xing, Yu He, Xingkang Fang, Haitao Johansson, Karl Henrik Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden

This paper studies the problem of recursively estimating the weighted adjacency matrix of a network out of a temporal sequence of binary-valued observations. The observation sequence is generated from nonlinear networked dynamics in which agents exchange and display binary outputs. Sufficient conditions are given to ensure stability of the observation sequence and identifiability of the system parameters. It is shown that stability and identifiability can be guaranteed under the assumption of independent standard Gaussian disturbances. Via a maximum likelihood approach, the estimation problem is transformed into an optimization problem, and it is verified that its solution is the true parameter vector under the independent standard Gaussian assumption. A recursive algorithm for the estimation problem is then proposed based on stochastic approximation techniques. Its strong consistency is established and convergence rate analyzed. Finally, numerical simulations are conducted to illustrate the results and to show that the proposed algorithm is insensitive to small unmodeled factors. Copyright © 2019, The Authors. All rights reserved.

关键词： Approximation theory

Two-timescale voltage control in distribution grids using deep reinforcement learning

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arXiv 2019年

作者： Yang, Qiuling Wang, Gang Sadeghi, Alireza Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for distribution grids by judiciously coupling data-driven with physicsbased optimization. On a faster timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it;whereas, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the longterm discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47-bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

Local Exchangeability

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arXiv 2019年

作者： Campbell, Trevor Syed, Saifuddin Yang, Chiao-Yu Jordan, Michael I. Broderick, Tamara Department of Statistics University of British Columbia Vancouver Canada Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge United States

Exchangeability—in which the distribution of an infinite sequence is invariant to reorderings of its elements—implies the existence of a simple conditional independence structure that may be leveraged in the design of statistical models and inference procedures. In this work, we study a relaxation of exchangeability in which this invariance need not hold precisely. We introduce the notion of local exchangeability—where swapping data associated with nearby covariates causes a bounded change in the distribution. We prove that locally exchangeable processes correspond to independent observations from an underlying measure-valued stochastic process. Using this main probabilistic result, we show that the local empirical measure of a finite collection of observations provides an approximation of the underlying measure-valued process and Bayesian posterior predictive distributions. The paper concludes with applications of the main theoretical results to a model from Bayesian nonparametrics and covariate-dependent permutation tests. Copyright © 2019, The Authors. All rights reserved.

关键词： Random processes

Exponential convergence for distributed smooth optimization under the restricted secant inequality condition

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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Johansson, Karl H. Chai, Tianyou Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the distributed smooth optimization problem in which the objective is to minimize a global cost function formed by a sum of local smooth cost functions, by using local information exchange. The standard assumption for proving exponential/linear convergence of first-order methods is the strong convexity of the cost functions, which does not hold for many practical applications. In this paper, we first show that the continuous-time distributed primal-dual gradient algorithm converges to one global minimizer exponentially under the assumption that the global cost function satisfies the restricted secant inequality condition. This condition is weaker than the strong convexity condition since it does not require convexity and the global minimizers are not necessary to be unique. We then show that the discrete-time distributed primal-dual algorithm constructed by using the Euler's approximation method converges to one global minimizer linearly under the same condition. The theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Continuous time systems

Truly aliasing-free digital RF-PWM power coding scheme for switched-mode power amplifiers

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Truly aliasing-free digital RF-PWM power coding scheme for s...

2018 IEEE Radio and Wireless Symposium, RWS 2018

作者： Tanovic, Omer Ma, Rui Mitsubishi Electric Research Laboratories CambridgeMA United States Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781538607091

This paper presents a novel method for multilevel power coding and upconversion to RF frequency, by the means of a digital radio-frequency pulse width modulation (RF-PWM). Delta-sigma modulation (ΔΣM) and memoryless predistortion are used to mitigate spectral aliasing effects, inherent to digital RF-PWM operation, and achieve high linearity of the transmitter. In this method, time-domain characterization of a multilevel digital RF-PWM was fully exploited in order to provide optimal parameter selection for the ΔΣM, which in turn enables aliasing-free digital RF-PWM that runs at reasonable clock speeds. Experiments with an arbitrary waveform generator are used to demonstrate effectiveness of the proposed novel power encoding scheme. The novel algorithm offers in-band SNR of - 45dB, while maintaining coding efficiency of about 55%, for a 20MHz-bandwidth 64QAM signal, while mere RF-PWM, running at the same clock speed, can achieve SNR of only - 10dB. © 2018 IEEE.

关键词： Power amplifiers

Linear convergence of first- And zeroth-order primal-dual algorithms for distributed nonconvex optimization

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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Digital Futures Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information *** first propose a distributed first-order primal-dual algorithm. We show that it converges sublinearly to a stationary point if each local cost function is smooth and linearly to a global optimum under an additional condition that the global cost function satisfies the Polyak-Lojasiewicz condition. This condition is weaker than strong convexity, which is a standard condition for proving linear convergence of distributed optimization algorithms, and the global minimizer is not necessarily unique. Motivated by the situations where the gradients are unavailable, we then propose a distributed zeroth-order algorithm, derived from the proposed distributed first-order algorithm by using a deterministic gradient estimator, and show that it has the same convergence properties as the proposed first-order algorithm under the same conditions. The theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost functions

Unraveling Ultrafast Photoionization in Hexagonal Boron Nitride

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arXiv 2021年

作者： Xue, Lianjie Liu, Song Hang, Yang Summers, Adam M. Wilson, Derrek J. Wang, Xinya Chen, Pingping Folland, Thomas G. Hachtel, Jordan A. Shi, Hongyu Hosseini-Zavareh, Sajed Das, Suprem R. Lei, Shuting Zhang, Zhuhua Sorensen, Christopher M. Guo, Wanlin Caldwell, Joshua D. Edgar, James H. Blaga, Cosmin I. Trallero-Herrero, Carlos A. Department of Physics Kansas State University ManhattanKS66506 United States Tim Taylor Department of Chemical Engineering Kansas State University ManhattanKS66506 United States J. R. Macdonald Laboratory Department of Physics Kansas State University ManhattanKS66506 United States State Key Laboratory of Mechanics and Control of Mechanical Structures Institute of Nano Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China Advanced Laser Light Source and few-cycle Inc. Institut Nationale de la Recherche Scientifique 1650 Boul. Lionel-Boulet VarennesQCJ3X 1P7 Canada Department of Industrial and Manufacturing Systems Engineering Kansas State University ManhattanKS66506 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Science Oak Ridge National Laboratory Oak Ridge37830 United States Department of Electrical and Computer Engineering Kansas State University ManhattanKS66506 United States Department of Physics University of Connecticut StorrsCT06269 United States

The non-linear response of dielectrics to intense, ultrashort electric fields has been a sustained topic of interest for decades with one of its most important applications being femtosecond laser micro/nano-machining. More recently, renewed interests in strong field physics of solids were raised with the advent of mid-infrared femtosecond laser pulses, such as high-order harmonic generation, optical-field-induced currents, etc. All these processes are underpinned by photoionization (PI), namely the electron transfer from the valence to the conduction bands, on a time scale too short for phononic motion to be of relevance. Here, in hexagonal boron nitride, we reveal that the bandgap can be finely manipulated by femtosecond laser pulses as a function of field polarization direction with respect to the lattice, in addition to the field’s intensity. It is the modification of bandgap that enables the ultrafast PI processes to take place in dielectrics. We further demonstrate the validity of the Keldysh theory in describing PI in dielectrics in the few TW/cm2 regime. © 2021, CC BY.

关键词： Photoionization