This article extends our previous works (Amatore, C.; Oleinick, A.; Svir, I. Anal. Chem. 2008, 80, 7947-7956; 7957-7963.) about the effects of resistive and capacitive distortions in voltammetry at disk microelectrodes. The particular case of voltammetry of a self-assembled monolayer carrying one redox site per molecule is investigated here. In addition, the effect of an uneven distribution of the effective electrochemical potential on the possibility of electron hopping (EH) contributions is examined. An original model of EH has been developed considering both diffusion-type (i.e., related to concentration gradients) and migration-type (i.e., imposed by an uneven distribution of the electrical potential due to an ohmic drop and capacitance charging) contributions. This predicts that as soon as the system performs out of thermodynamic equilibrium and provided that the EH rate constants are not too small the system tends to re-establish its out-of-equilibrium state through EH. Hence, EH somewhat tries to compensate the voltammetric distortions that would be enforced by the uneven distribution of the electrochemical driving force incurred by the system due to an ohmic drop and capacitive charging. However, this rigorous analysis established that, though EH may be effective under specific circumstances particularly near the electrode edge, its overall influence on voltammetric waves remains negligible for any realistic experimental situation.
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Alzheimers Dement
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School of Electronic Information Engineering, Lang Fang Normal University, Langfang, 065000, Hebei, China.
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College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control, Guilin University of Technology, Guilin, 541006, China. Electronic address:
The uneven distribution of lead (Pb) in rice and soil across the primary rice-growing regions of southern China has led to challenges in assessing rice quality and associated health risks. Therefore, it is crucial to develop a fast and precise method for forecasting the accumulation of Pb in soils and rice to evaluate the environmental risks of heavy metals. We utilized eight machine learning models to fit the training data and find the optimal model based on 1,396 pairs of soil-rice samples collected during field surveys in Guizhou Province.
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