Reliability-based design optimization (RBDO) involves evaluation of probabilistic constraints, which can be done in two different ways, the reliability index approach (RIA) and the performance measure approach (PMA). It has been reported in the literature that RIA yields instability for some problems but PMA is robust and efficient in identifying a probabilistic failure mode in the optimization process. However, several examples of numerical tests of PMA have also shown instability and inefficiency in the RBDO process if the advanced mean value (AMV) method, which is a numerical tool for probabilistic constraint evaluation in PMA, is used, since it behaves poorly for a concave performance function, even though it is effective for a convex performance function. To overcome difficulties of the AMV method, the conjugate mean value (CMV) method is proposed in this paper for the concave performance function in PMA. However, since the CMV method exhibits the slow rate of convergence for the convex function, it is selectively used for concave-type constraints. That is, once the type of the performance function is identified, either the AMV method or the CMV method can be adaptively used for PMA during the RBDO iteration to evaluate probabilistic constraints effectively. This is referred to as the hybrid mean value (HMV) method. The enhanced PMA with the HMV method is compared to RIA for effective evaluation of probabilistic constraints in the RBDO process. It is shown that PMA with a spherical equality constraint is easier to solve than RIA with a complicated equality constraint in estimating the probabilistic constraint in the RBDO process.

Surface modification is one of the most important techniques in modern science and engineering. The facile introduction of a wide variety of desired properties onto virtually any material surface is an ultimate goal in surface chemistry. To achieve this goal, the incorporation of structurally diverse molecules onto any material surface is an essential capability for ideal surface modification. Here, a general strategy for surface modification is presented in which many diverse surfaces can be functionalized by immobilizing a wide variety of molecules. This strategy functionalizes surfaces by a one‐step immersion of substrates in a one‐pot mixture of a molecule and a catecholamine surface modification agent. This one‐step procedure for surface modification represents a standard protocol to control interfacial properties.

Polydopamine coating is emerging as a useful method of surface functionalization due to the ability of this compound to form a nanometer-scale organic thin film on virtually any material surface to which proteins, peptides, oligonucleotides, metal ions or synthetic polymers are able to be attached. The unique properties of polydopamine make this technique suitable for nanomedicine. To facilitate the use of polydopamine, evaluation of toxicity is of great importance. In this article, we investigated the in vivo toxicity of polydopamine. We found that the polydopamine functions as a biocompatible layer, attenuating adverse biological responses caused by intrinsic properties of the coated material. One-step polydopamine coating greatly reduced the inflammatory response to poly-L-lactic acid surfaces and the immunological responses of blood on quantum dots were also reduced. Our results indicate that polydopamine provides a versatile platform that can reduce the in vivo toxicity of biomaterials that contact tissue or blood.

Dopamine-melanin, polydopamine molecular assembly by cation-π bonds.