Process models used for activated sludge, anaerobic digestion and in general wastewater treatment plant process design and optimization have traditionally focused on important biokinetic conversions. There is a growing realization that abiotic processes occurring in the wastewater (i.e. 'solvent') have a fundamental effect on plant performance. These processes include weak acid-base reactions (ionization), spontaneous or chemical dose-induced precipitate formation and chemical redox conversions, which influence pH, gas transfer, and directly or indirectly the biokinetic processes themselves. There is a large amount of fundamental information available (from chemical and other disciplines), which, due to its complexity and its diverse sources (originating from many different water and process environments), cannot be readily used in wastewater process design as yet. This position paper outlines the need, the methods, available knowledge and the fundamental approaches that would help to focus the effort of research groups to develop a physicochemical framework specifically in support of whole-plant process modeling. The findings are that, in general, existing models such as produced by the International Water Association for biological processes are limited by omission of key corrections such as non-ideal acid-base behavior, as well as major processes (e.g., ion precipitation). While the underlying chemistry is well understood, its applicability to wastewater applications is less well known. This justifies important further research, with both experimental and model development activities to clarify an approach to modeling of physicochemical processes.
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http://dx.doi.org/10.2166/wst.2012.300 | DOI Listing |
Chemistry
January 2025
Indian Institute of Science Education and Research (IISER), Chemistry, Dr. Homi Bhabha Road, Pashan, 411008, Pune, INDIA.
Metal-organic frameworks (MOFs) are a fascinating class of structured materials with diverse functionality originating from the distinctive physicochemical properties. This review focuses on the specific chemical design of geometrically frustrated MOFs along with the origin of the intriguing magnetic properties. We have discussed the arrangement of spin centres (metal and ligand) which are responsible for the unusual magnetic phenomena in MOFs.
View Article and Find Full Text PDFInorg Chem
January 2025
School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Zwitterionic energetic materials offer a unique combination of high performance and stability, yet their synthesis and stability enhancement remain key challenges. In this study, we report the synthesis of a highly stable (dinitromethyl-functionalized zwitterionic compound, 1-(amino(iminio)methyl)-4,5-dihydro-1H-pyrazol-5-yl)dinitromethanide (), with a thermal decomposition temperature of 215 °C, surpassing that of most previously reported energetic monocyclic zwitterions ( < 150 °C). This compound was synthesized via intramolecular cyclization of a trinitromethyl-functionalized hydrazone precursor.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
Department of Environmental Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea. Electronic address:
This study describes the preparation of novel hybrid aerogels derived from gelatin (Gel), incorporating Br-functionalized zirconium-based metal-organic framework (UiO-66-Br; MOF) as modifying agent to effectively eliminate phosphate and fluoride ions from aqueous environments. The adsorption performance of MOF decorated Gel (Gel-xMOF) hybrid aerogels was investigated under different conditions, including agitation time, adsorbent dosage, solution pH, initial phosphate and fluoride concentrations, coexisting ions, and temperature. The functional groups of the gelatin network, coupled with UiO-66-Br, enhanced the adsorption performance of phosphate and fluoride ions from aqueous solutions.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
BIFOLD─Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany.
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data.
View Article and Find Full Text PDFPLoS One
January 2025
ESQlabs Gmbh, Saterland, Germany.
Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance.
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