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Li3VO4 (LVO) is a highly promising anode material for lithium-ion batteries, owing to its high capacity and stable discharge plateau. However, LVO faces a significant challenge due to its poor rate capability, which is mainly attributed to its low electronic conductivity. To enhance the kinetics of lithium ion insertion and extraction in LVO anode materials, a conductive polymer called poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is applied to coat the surface of LVO. This uniform coating of PEDOT:PSS improves the electronic conductivity of LVO, thereby enhancing the corresponding electrochemical properties of the resulting PEDOT:PSS-decorated LVO (P-LVO) half-cell. The charge/discharge curves between 0.2 and 3.0 V (vs. Li+/Li) indicate that the P-LVO electrode displays a capacity of 191.9 mAh/g at 8 C, while the LVO only delivers a capacity of 111.3 mAh/g at the same current density. To evaluate the practical application of P-LVO, lithium-ion capacitors (LICs) are constructed with P-LVO composite as the negative electrode and active carbon (AC) as the positive electrode. The P-LVO//AC LIC demonstrates an energy density of 107.0 Wh/kg at a power density of 125 W/kg, along with superior cycling stability and 97.4% retention after 2000 cycles. These results highlight the great potential of P-LVO for energy storage applications.

COSMOSAC is a model that allows apriori predictions of activity coefficients for characterizing solute-solvent interactions. The method requires the input of sigma profile, the charge distribution on the surface of the molecules, which can be obtained through quantum mechanics calculation. Since Sigma profile is a unique function of molecular structure, it is desirable that they can be obtained using a surrogate model of the quantum computation with a molecular description as input. Previously, a model, the Universal Digital Chemical Space (UDCS), that was developed that allowed us to calculate the Sigma profiles used Simplified Molecular-Input-Line-Entry system (SMILES) as input. In this work, an improved version of this approach was developed using a Transformer model to encode the SMILES text string. Successive input elements in the text string, known as K-mers was also encoded and errors of predicted moments of Sigma profiles and prediction of activity coefficient of reference solvents were also considered as in the loss function. Results showed that while the prediction accuracy of Sigma profile (coefficient of determination R2) were not significantly improved, prediction accuracy of the first and second moment, especially the poorer ranked results; as well as the activity coefficients can be significantly improved with the inclusion of higher K-mers. Further improvement can be achieved with the inclusion of activity loss which substantially improved the accuracy of the 5th and 25th percentile of the moment loss and the activity coefficient of the species in n-hexane.

This paper developed a new variable selection method for soft sensor applications using the nonnegative garrote (NNG) and artificial neural network (ANN). The proposed method employs the ANN to generate a well-trained network, and then uses the NNG to conduct the accurate shrinkage of input weights of the ANN. This paper took Bayesian information criterion as the model evaluation criterion, and the optimal garrote parameter s was determined by v-fold cross-validation. The performance of the proposed algorithm was compared to existing state-of-art variable selection methods. Two artificial dataset examples and a real industrial application for air separation process were applied to demonstrate the performance of the methods. The experimental results showed that the proposed method presented better model accuracy with fewer variables selected, compared to other state-of-art methods. [ABSTRACT FROM AUTHOR]