Amino acid salt (AAs)
aqueous solutions have recently exhibited
a great potential in CO
2
absorption from various gas mixtures.
In this work, four hybrid machine learning methods were developed
to evaluate 626 CO
2
and AAs equilibrium data for different
aqueous solutions of AAs (potassium sarcosinate, potassium
l
-asparaginate, potassium
l
-glutaminate, sodium
l
-phenylalanine, sodium glycinate, and potassium lysinate) gathered
from reliable references. The models are the hybrids of the least
squares support vector machine and coupled simulated annealing optimization
algorithm, radial basis function neural network (RBF-NN), particle
swarm optimization–adaptive neuro-fuzzy inference system, and
hybrid adaptive neuro-fuzzy inference system. The inputs of the models
are the CO
2
partial pressure, temperature, mass concentration
in the aqueous solution, molecular weight of AAs, hydrogen bond donor
count, hydrogen bond acceptor count, rotatable bond count, heavy atom
count, and complexity, and the CO
2
loading capacity of
AAs aqueous solution is considered as the output of the models. The
accuracies of the models’ results were verified through graphical
and statistical analyses. RBF-NN performance is promising and surpassed
that of other models in estimating the CO
2
loading capacities
of AAs aqueous solutions.
Lateral microstructure heterogeneity in anodes is known to induce nonuniform current density, state of charge, and lithium plating. This means that such electrode heterogeneity can limit fast charging of lithium-ion batteries. In this work, a combination of experiments and simulation is employed to understand the effect of mm scale lateral heterogeneity on cell aging. A previously developed model was extended to efficiently simulate SEI formation and Li plating for independent regions of an electrode. The model consists of three parallel regions each described under a P2D framework and with a distinct ionic resistance and possibly active material loading. The results suggest that during fast charge when the active material is uniformly distributed across the three regions, the region with the highest resistance reaches the end of life sooner than the other regions. There is also positive feedback from Li metal filling the pores near the separator interface that further accelerates lithium plating. Finally, when there is a non-uniform active material distribution associated with the ionic resistance heterogeneity, tight competition between regions can occur, leading to less overall lithium plating and plating that is more uniform between regions.
To improve power and cycling performance of lithium-ion batteries, dual-layer or porosity-gradient electrodes have been proposed. By using a higher porosity close to the separator, the intention is to improve ion transport where it is most needed. Here, MacMullin numbers of two dual-layer anode samples are tested using an impedance measurement technique developed previously. To characterize the microstructure of each layer independently, we developed an improved transmission-line model that accounts for each layer's properties. Virtual experiments in which impedance measurements were simulated using COMSOL Multiphysics were used to examine and improve the accuracy of experimental inversion process. The results for the two dual-layer anodes studied show that MacMullin numbers follow expected trends, though the anodes are quite different from each other.
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