Aliovalent Substitution Tunes Physical Properties in a Conductive Bis(dithiolene) Two-Dimensional Metal-Organic Framework.

Two-dimensional conductive metal-organic frameworks have emerged as promising electronic materials for applications in (opto)electronic, thermoelectric, magnetic, electrocatalytic, and energy storage devices. Many bottom-up or postsynthetic protocols have been developed to isolate these materials or further modulate their electronic properties. However, some methodologies commonly used in classic semiconductors, notably, aliovalent substitution, are conspicuously absent. Here, we demonstrate how aliovalent Fe(III) to Ni(II) substitution enables the isolation of a Ni bis(dithiolene) material from a previously reported Fe analogue. Detailed characterization supports the idea that aliovalent substitution of Fe(III) to Ni(II) results in an in situ oxidation of the organic dithiolene linker. This substitution-induced redox tuning modulates the electronic properties in the system, leading to higher electrical conductivity and Hall mobility but slightly lower carrier densities and weaker antiferromagnetic interactions. Moreover, this aliovalent substitution improves the material's electrochemical stability and thus enables pseudocapacitive behavior in the Ni material. These results demonstrate how classic aliovalent substitution strategies in semiconductors can also be leveraged in conductive MOFs and add further support to this class of compounds as emerging electronic materials.

Engineering Antifouling Conducting Polymers for Modern Biomedical Applications.

Conducting polymers are considered to be favorable electrode materials for implanted biosensors and bioelectronics, because their mechanical properties are similar to those of biological tissues such as nerve and brain tissues. However, one of the primary challenges for implanted devices is to prevent the unwanted protein adhesion or cell binding within biological fluids. The nonspecific adsorption generally causes the malfunction of implanted devices, which is problematic for long-term applications. When responding to the requirements of solving the problems caused by nonspecific adsorption, an increasing number of studies on antifouling conducting polymers has been recently published. In this review, synthetic strategies for preparing antifouling conducting polymers, including direct synthesis of functional monomers and post-functionalization, are introduced. The applications of antifouling conducting polymers in modern biomedical applications are particularly highlighted. This paper presents focuses on the features of antifouling conducting polymers and the challenges of modern biomedical applications.

Critical Study of the Recognition between C-Reactive Protein and Surface-Immobilized Phosphorylcholine by Quartz Crystal Microbalance with Dissipation.

C-reactive protein (CRP), a biomarker for cardiovascular disease, has been reported to have a strong affinity to zwitterionic phosphorylcholine (PC) groups in the presence of calcium ions. In addition, PC-immobilized surfaces have been used as a nonfouling coating to prevent nonspecific protein binding. By appropriately using the features of PC-immobilized surfaces, including specific recognition to CRP and nonfouling surface, it is reasonable to create an antibody-free biosensor for the specific capture of CRP. In this study, PC-functionalized 3,4-ethylenedioxythiophene (EDOT) monomers were used to prepare PC-immobilized surfaces. The density of PC groups on the surface can be fine-tuned by changing the composition of the monomer solutions for the electropolymerization. The density of PC group was confirmed by X-ray photoelectron spectroscopy (XPS). The specific interaction of CRP with PC groups was monitored by using a quartz crystal microbalance with dissipation (QCM-D). The amount of protein binding could be estimated by the reduction in frequency readout. Through the QCM-D measurement, we revealed the nonfouling property and the specific CRP capture from our PC-immobilized surfaces. Notably, the dissipation energy also dropped during the binding process between CRP and PC, indicating the release of water molecules from the PC groups during CRP adsorption. We anticipate that surface-bound water molecules are mainly released from areas near the immobilized PC groups. Based on Hofmeister series, we further examined the influence of ions by introducing four different anions including both kosmotrope (order maker) and chaotrope (disorder maker) into the buffer for the CRP binding test. The results showed that the concentration and the type of anions play an important role in CRP binding. The present fundamental study reveals deep insights into the recognition between CRP and surface-immobilized PC groups, which can facilitate the development of CRP sensing platforms.

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets.

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms. However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features. In order to solve these problems, we propose a new model based on Deep Belief Nets to predict the CTR of mobile advertising, which combines together the powerful data representation and feature extraction capability of Deep Belief Nets, with the advantage of simplicity of traditional Logistic Regression models. Based on the training dataset with the information of over 40 million mobile advertisements during a period of 10 days, our experiments show that our new model has better estimation accuracy than the classic Logistic Regression (LR) model by 5.57% and Support Vector Regression (SVR) model by 5.80%.