Increasing global lead consumption has been mainly supported by the acid battery manufacturing industry. As the lead demand will continue to grow, to provide the necessary lead will require an efficient approach to recycling lead acid batteries. In this paper was performed a mathematical modeling of the process parameters for lead recovery from spent lead-acid batteries. The results of the mathematical modeling compare well with the experimental data. The experimental method applied consists in the solubilisation of the sulfate/oxide paste with sodium hydroxide solutions followed by electrolytic processing for lead recovery. The parameters taken into considerations were NaOH molarity (4M, 6M and 8M), solid/liquid ratio - S/L (1/10, 1/30 and 1/50) and temperature (40°C, 60°C and 80°C). The optimal conditions resulted by mathematical modeling of the electrolytic process of lead deposition from alkaline solutions have been established by using a second-order orthogonal program, in order to obtain a maximum efficiency of current without exceeding an imposed energy specific consumption. The optimum value for the leaching recovery efficiency, obtained through mathematical modeling, was 89.647%, with an error of δ=3.623 which leads to a maximum recovery efficiency of 86.024%. The optimum values for each variable that ensure the lead extraction efficiency equal to 89.647% are the following: 3M - NaOH, 1/35 - S/L, 70°C - temperature.
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http://dx.doi.org/10.1016/j.wasman.2016.08.005 | DOI Listing |
ASN Neuro
January 2025
Department of Anatomy and Neurobiology, Virginia Commonwealth University, Richmond, Virginia, USA.
People living with HIV (PLWH) experience HIV-associated neurocognitive disorders (HAND), even though combination antiretroviral therapy (cART) suppresses HIV replication. HIV-1 transactivator of transcription (HIV-1 Tat) contributes to the development of HAND through neuroinflammatory and neurotoxic mechanisms. C-C chemokine 5 receptor (CCR5) is important in immune cell targeting and is a co-receptor for HIV viral entry into CD4+ cells.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Laboratory for Protein Crystallography, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.
[FeFe]-hydrogenases catalyze the reversible two-electron reduction of two protons to molecular hydrogen. Although these enzymes are among the most efficient H-converting biocatalysts in nature, their catalytic cofactor (termed H-cluster) is irreversibly destroyed upon contact with dioxygen. The [FeFe]-hydrogenase CbA5H from has a unique mechanism to protect the H-cluster from oxygen-induced degradation.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Molecular & Cellular Biosciences, University of Cincinnati, Cincinnati, OH 45267.
TGFβ family ligands are synthesized as precursors consisting of an N-terminal prodomain and C-terminal growth factor (GF) signaling domain. After proteolytic processing, the prodomain typically remains noncovalently associated with the GF, sometimes forming a high-affinity latent procomplex that requires activation. For the TGFβ family ligand anti-Müllerian hormone (AMH), the prodomain maintains a high-affinity interaction with its GF that does not render it latent.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Physics and Astronomy, University of Manitoba, Winnipeg R3T 2N2, Canada.
Theory and simulations are used to demonstrate implementation of a variational Bayes algorithm called "active inference" in interacting arrays of nanomagnetic elements. The algorithm requires stochastic elements, and a simplified model based on a magnetic artificial spin ice geometry is used to illustrate how nanomagnets can generate the required random dynamics. Examples of tracking and PID control are demonstrated and shown to be consistent with the original stochastic differential equation formulation of active inference.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Electrical and Computer Engineering Department, Concordia University, Montreal, Canada.
Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways.
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