Uranyl Affinity between Uranyl Cation and Different Kinds of Monovalent Anions: Density Functional Theory and Quantitative Structure–Property Relationship Model

In order to design effective extractants for uranium extraction from seawater, it is imperative to acquire a more comprehensive understanding of the bonding properties between the uranyl cation (UO22+) and various ligands. Therefore, we employed density functional theory to investigate the complexation reactions of UO22+ with 29 different monovalent anions (L–1), exploring both mono- and bidentate coordination. We proposed a novel concept called “uranyl affinity” (Eua) to facilitate the establishment of a standardized scale for assessing the ease or difficulty of coordination bond formation between UO22+ and diverse ligands. Furthermore, we conducted an in-depth investigation into the underlying mechanisms involved. During the process of uranyl complex [(UO2L)+] formation, lone pair electrons from the coordinating atom in L– are transferred to either the lowest unoccupied molecular degenerate orbitals 1ϕu or 1δu of the uranyl ion, which originate from the uranium atom’s 5f unoccupied orbitals. In light of discussion concerning the mechanisms of coordination bond formation, quantitative structure–property relationship analyses were conducted to investigate the correlation between Eua and various structural descriptors associated with the 29 ligands under investigation. This analysis revealed distinct patterns in Eua values while identifying key influencing factors among the different ligands

Histological observation of a gelatin sponge transplant loaded with bone marrow-derived mesenchymal stem cells combined with platelet-rich plasma in repairing an annulus defect - Fig 2

<p>The exposure of annulus fibrosus after decompression of the lamina, (A) sham group, arrow showing annulus fibrosus. (B)injury group,arrow showing a 1 × 1 cm defect of annulus fibrosus. (C)therapeutic group,arrow showing complexes including BMSCs, PRP,and Gelatin sponges.</p

Quantitative analysis of type II collagen staining for each group (x ± s, n = 90).

<p>Quantitative analysis of type II collagen staining for each group (x ± s, n = 90).</p

HE staining score for each group (x ± s, n = 90).

<p>HE staining score for each group (x ± s, n = 90).</p

AB-PAS staining in each group after 3,6,12 weeks.

<p>(A-C) AB-PAS staining in the sharm group after 3,6,12 weeks (×100). (D-F) AB-PAS staining in the injury group after 3,6,12 weeks (×100). (G-I) AB-PAS staining in the therapeutic groupafter 3,6,12 weeks (×100). Black arrows:cartilage cells.</p

Masson staining in each group after 3,6,12 weeks.

<p>(A-C) Masson staining in the sharm group after 3,6,12 weeks (×100). (D-F) Masson staining in the injury group after 3,6,12 weeks (×100). (G-I) Masson staining in the therapeutic group after 3,6,12 weeks (×100). Blue arrows:mature bone trabecular.Black arrows:muscle fiber connective tissue.</p

Results of HE staining in each group after 3,6,12 weeks.

<p>(A-C) HE staining in the sharm group after 3,6,12 weeks (×100). (D-F) HE staining in the injury group after 3,6,12 weeks (×100). (G-I) HE staining in the therapeutic groupafter 3,6,12 weeks (×100).Blue arrows:collagen and matrix.Black arrows:cartilage cells.</p

Masson staining score for each group (x ± s, n = 90).

<p>Masson staining score for each group (x ± s, n = 90).</p

The flowchart of the heuristic algorithm.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.</div

The mean and variance travel time for each link.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.</div