The feasibility of hybrid systems for simultaneous removal of nitrate (NO3-) and ammonium ions (NH4+) from livestock wastewater was examined in batch experiments. As a part of efforts to remove nitrate and ammonium simultaneously, Fe0 and adsorbents including coconut-based granular activated carbon (GAC), sepiolite and filtralite were used. Various parameters such as adsorbent dosages and temperature were studied. Removal of NO3- increased with increase in temperature. Maximum NO3- removal (85.3%) was observed for the Fe0-filtralite hybrid system at 45 degrees C for a 24 h reaction time. Increase in GAC and sepiolite dosages had significant (P < 0.01) effect on the NH4+ removal efficiency, which was primarily due to the net negative surface charge of the adsorbents. The efficiency of hybrid systems for the removal of NO3- was in the order of filtralite > sepiolite > GAC, and the order of the removal of NH4+ was GAC > sepiolite > filtralite. The results of the present study suggest that the use of hybrid systems could be a promising innovative technology for achieving simultaneous removal of NO3- and NH4 from livestock wastewater.
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http://dx.doi.org/10.1080/09593330.2011.565079 | DOI Listing |
Chaos
January 2025
Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Crawley, Western Australia 6009, Australia.
We propose a universal method based on deep reinforcement learning (specifically, soft actor-critic) to control the chimera state in the coupled oscillators. The policy for control is learned by maximizing the expectation of the cumulative reward in the reinforcement learning framework. With the aid of the local order parameter, we design a class of reward functions for controlling the chimera state, specifically confining the spatial position of coherent and incoherent domains to any desired lateral position of oscillators.
View Article and Find Full Text PDFNanoscale
January 2025
School of Chemistry & Chemical Engineering, School of Materials Science and Engineering and Anhui Province Key Laboratory of Chemistry for Inorganic/Organic Hybrid Functionalized Materials, Department of Chemistry and Center for Atomic Engineering of Materials, Key Laboratory of Structure and Functional Regulation of Hybrid Materials of the Ministry of Education, Anhui University, Hefei, 230601, China.
Atomically precise nanoclusters (NCs) can serve as an excellent platform for a comprehensive understanding of structure-property relationships. Herein, three structurally similar Cu NCs (Cu-1, Cu-2 and Cu-3) have been prepared for the photocatalytic phenylacetylene self-coupling reaction. It was found that Cu-1 NC achieved the highest turnover number (TON) of 524.
View Article and Find Full Text PDFJ Chem Phys
January 2025
School of Chemistry, Beihang University, Beijing 100191, China.
Dynamic density functional theory (DDFT) is a fruitful approach for modeling polymer dynamics, benefiting from its multiscale and hybrid nature. However, the Onsager coefficient, the only free parameter in DDFT, is primarily derived empirically, limiting the accuracy and broad application of DDFT. Herein, we propose a machine learning-based, bottom-up workflow to directly extract the Onsager coefficient from molecular simulations, circumventing partly heuristic assumptions in traditional approaches.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Laboratory of Theoretical Chemistry, Institute of Chemistry, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/A, Budapest H-1117, Hungary.
Single-Molecule Junctions (SMJs) are key platforms for the exploration of electron transport at the molecular scale. In this study, we present a method that employs different exchange-correlation density functionals for the molecule and the lead domains in an SMJ, enabling the selection of the optimal one for each part. This is accomplished using a formally exact projection-based density-functional theory (DFT-in-DFT) embedding technique combined with the non-equilibrium Green's function method to predict zero-bias conductance.
View Article and Find Full Text PDFiScience
January 2025
School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, China.
This study introduces a hybrid network model for phase classification, integrating quantum networks and complex-valued neural networks. This architecture uses elemental composition as its only input, eliminating complex feature engineering. Parameterized quantum networks handle sparse elemental data and convert data from real to complex domains, increasing information dimensionality.
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