Hydrogels are biocompatible soft materials that resemble biological tissues more than any other material. However, the use of these systems in soft robotics has been limited to aqueous environments. In the work published to date, hydrogels have relied on external water to swell or shrink in response to stimuli and, therefore, to actuate macroscopically. In the work reported here, this limitation is overcome by synthesizing a novel type of electroactive hydrogels capable of actuating when a low electric field is applied, even outside water. The bending actuation of these materials is caused by the movement of solvated ions within the hydrogel, which generates a concentration gradient, making it possible to use them directly in ambient-air conditions. A mathematical model for this behavior is proposed. Issues like resistive heating and material drying are addressed by preparing graphene hybrid hydrogels and by using hygroscopic salts. Two applications are presented as a demonstration of the capabilities of these hydrogels: a soft gripper with two continuum actuators and a soft fingertip capable of changing its volume and stiffness. In addition, the possibility of fabrication by 3D printing technologies enhances the applicability of these promising materials, thus paving the way for innovative developments.
Mimicking nature's self-healing ability has always been desired in science, especially when devices accumulate damage over time with performance, including the loss of function due to deterioration. SHAP (Self-Healing AETA([2-(acryloyloxy)ethyl]trimethylammonium chloride)based Polymer), a hydrogel with autonomous self-healing ability that can be applied for the development of a pneumatic artificial muscle, is presented here. Unlike other self-healing hydrogels, SHAP does not require any external stimulus to self-heal and it presents outstanding anti-drying properties. Few-layer graphene is also incorporated into the polymer network of the hydrogel in order to study the possible influence that the nanomaterial has on the properties of the scaffolds. The mechanical behavior and the self-healing abilities of the resulting hydrogels are analyzed. Moreover, the mechanism of self-healing is discussed in terms of experimental results and theoretical calculations. The data suggest a mechanism based on strong hydrogen-bonding interactions between the water molecules that remain inside SHAP, which keeps the material wet and soft under ambient conditions. Finally, the development of a SHAP-based artificial muscle is presented. The results show good performance of the healed artificial muscles after damage, even with healing periods as short as 10 minutes.
In recent times, there has been an increased use of software and computational models in Medicinal Chemistry, both for the prediction of effects such as drug-target interactions, as well as for the development of (Quantitative) Structure-Activity Relationships ((Q)SAR). Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the creation of new computational tools. The adoption of computational tools by medicinal chemists is sadly, and all too often accompanied, by a lack of understanding of the legal aspects related to software and model use, that is, the copyright protection of new medicinal chemistry software and software-mediated discovered products. This article aims to provide a reference to the various legal avenues that are available for the protection of software, and the acceptance and legal treatment of scientific results and techniques derived from such software. An overview of relevant international tax issues is also presented. We have considered cases of patents protecting software, models, and/or new compounds discovered using methods such as molecular modeling or QSAR. This paper has been written and compiled by the authors as a review of current topics and trends on the legal issues in certain fields of Medicinal Chemistry and as such is not intended to be exhaustive.
The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W(k)(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation).
In this paper we present a hydrogel with self-healing capabilities and its application for the development of a Pneumatic Artificial Muscle (PAM). Unlike other hydrogels, our material can be used outside of aqueous environments and does not need any external stimulus to self-heal, which makes it an interesting alternative for the manufacturing of soft robots. First, the mechanical properties of the hydrogel and its self-healing ability are analyzed. Second, we present the development of a pneumatic muscle based on the classic McKibben design but including our material. Finally, we analyze the capabilities of our self-repairing muscles before and after being punctured. The results show a good performance of our actuators even after low healing periods (10 minutes).
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