No abstract

Despite existing in biological systems, developing synthetic polyampholyte (PA) hydrogels constructed by both ionic and metal–ligand bonds remains challenging. Herein, a simple secondary equilibrium approach is proposed to fabricate strong and tough PA hydrogels via the synergy of ionic and metal–ligand bonds. The original PA gels (constructed by ionic bonds) are first dialyzed in multivalent metal‐ion solutions to reach a swelling equilibrium and then moved to deionized water to dialyze excess free ions to achieve a new equilibrium. Through this approach, the original PA gel network can be optimized and eventually constructed by ionic and metal–ligand bonds, enabling a synergistic reinforcement. By selecting different original PA gel systems and diverse multivalent metal‐ions, the proposed approach is proved to be generalizable to fabricate strong and tough PA gels. Additionally, the hydrogels have stable ion‐conductivity even at the water‐equilibrium state, making them promising as strain sensors. The viscoelastic and elastic contributions to the mechanical properties of the hydrogels by a viscoelastic model are also discussed to further understand the strengthening and toughening mechanisms. The proposed strategy is simple but effective for achieving strong and tough PA‐based hydrogels. This study also provides new insights for PA hydrogels in electrolyte environments.

Supplementary data are available at Bioinformatics online.

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/tempcode.html.