Search

Quantitative structure-activity relationship modeling techniques are widely employed in diverse areas of chemistry, including environmental risk assessment of compounds and virtual screening of drugs. The modeling process entails optimizing the hyper parameters of the model and selecting appropriate molecular descriptors. It is a typical optimization problem with high-dimensional, nonlinear, continuous and discrete variables coexisting. To address this challenge, this paper proposes an enhanced snake optimizer with multiple strategies. First, new food emergence and temperature change mechanisms are introduced to balance the exploration and exploitation. Second, a death and regeneration mechanism is employed to enhance the algorithm's ability to escape from local optima. Third, new formulas for food searching, fighting, mating and eating are designed to simplify the complexity of the algorithm and improve its search efficiency and accuracy. Fourth, the performance of the proposed algorithm is evaluated using the CEC2017 benchmark functions, eight engineering cases with continuous variables, and six feature selection cases with discrete variables. Experimental results demonstrate that the algorithm significantly outperforms comparison algorithms. Finally, the algorithm was applied to quantitative structure-activity relationship modeling for environmental toxicity prediction. Compared with other algorithms, the algorithm can better configure hyper-parameters and select fewer and more effective features for environmental toxicity prediction model. It will be an effective tool to improve the performance of quantitative structure-activity relationship models. • New food emergence and temperature change mechanisms are presented to better balance exploration and exploitation. • Death and regeneration mechanism is adopted to avoid local optima. • The performance of the proposed algorithm is validated in CEC2017, engineering design and feature selection problems. • A joint optimization approach is proposed for quantitative structure-activity relationship modeling. • An environmental toxicity prediction model is obtained by jointly optimizing hyper-parameters and molecular descriptors. [ABSTRACT FROM AUTHOR]

How defects between layers of porous membranes affect their gas separation performance is still a problem. Here, we design three bilayer porous graphene membranes with different and representative defect torsion angles to regulate gas separation. We find from first-principles density functional theory calculations that the selectivity of the H 2 /CH 4 mixture can be varied by several orders of magnitude, of about 107:1 (E barrier/CH4 : 0.24, 0.18, 0.24 eV), 1019:1 (E barrier/CH4 : 0.49, 0.37, 0.47 eV) and 1012:1 (E barrier/CH4 : 0.42, 0.15, 0.45 eV), when going through the models of bi-H pore10 -20 x , bi-H pore10 -20 y and bi-H pore10 -47 y. Every barrier curve of gas penetration features three peaks. Additionally, molecular dynamics simulations show that the bilayer porous graphene membrane with a defect torsion angle of 20° along the x / y -axis possesses high H 2 /CH 4 separation ratio (up to 56–58:1) at room temperature after 5000 ps. The defect torsion angle of bilayer porous graphene improves the selectivity exponentially by regulating the three high penetration energy barriers of CH 4 , while the three energy barriers of H 2 are all very low. This work provides a new strategy to achieve highly selective separation of mixture by porous 2D-materials via regulating defect torsion angle between layers. [Display omitted] • The defect torsion angle is a powerful knob for regulating gas separation. • The potential energy curve for defect torsion angle model features three peaks. • Bi-H pore10 -20 y shows a high separation selectivity of 1019:1 for H 2 over CH 4. [ABSTRACT FROM AUTHOR]

Spiro-OMeTAD is a commonly used hole transport layer (HTL) in perovskite photodetectors (PDs) due to its high glass transition temperature, good solubility in organic solvents, amorphous nature, and better energy level alignment with perovskite layers, etc. However, the unoxidized Spiro-OMeTAD has low electrical conductivity and hole mobility, while air oxidation raises device performance and reproducibility issues. In this study, we successfully achieved early and rapid oxidation of Spiro-OMeTAD by introducing a quantitative amount of manganese dioxide (MnO 2), circumventing the challenges of air oxidation and greatly reducing device preparation time and cost. Additionally, MnO 2 -doped HTLs exhibited significantly improved PD performance, with specific detectivity (D*) increased from 4.36 × 1012 Jones to 4.14 × 1013 Jones. Unpackaged MnO 2 -doped PDs maintained about 81.4 % of the initial EQE value in the glove box after one month, showing remarkable stability. This work demonstrates the feasibility of using MnO 2 as an oxidizing agent for Spiro-OMeTAD and allows us to appreciate the potential of transition metal-based oxides in the oxidation of HTMs. [ABSTRACT FROM AUTHOR]