RESUMEN
Sulfide-based all-solid-state battery (ASSB) with a lithium metal anode (LMA) is a promising candidate to surpass conventional Li-ion batteries owing to their inherent safety against fire hazards and potential to achieve a higher energy density. However, the narrow electrochemical stability window and chemical reactivity of the sulfide solid electrolyte towards the LMA results in interfacial degradation and poor electrochemical performance. In this direction, we introduce an organic additive approach, that is the mixing of prelithiated trithiocyanuric acid, Li3TCA, with Li6PS5Cl, to establish a stable interface while preserving high ionic conductivity. Including 2.5 wt% Li3TCA alleviates the decomposition of the electrolyte on the lithium metal interface, decreasing the Li2S content in the solid-electrolyte interface (SEI) thus forming a more stable interface. In Li|Li symmetric cells this enables a rise in the critical current density from 1.0 to 1.9 mA cm-2 and stable cycling for over 750 hours at a high current density of 1.0 mA cm-2. This approach also enables Li|NbO-NCM811 full cell to operate more than 500 cycles at 0.3C.
RESUMEN
The presence of energetically low-lying triplet states is a hallmark of organic semiconductors. Even though they present a wealth of interesting photophysical properties, these optically dark states significantly limit optoelectronic device performance. Recent advances in emissive charge-transfer molecules have pioneered routes to reduce the energy gap between triplets and "bright" singlets, allowing thermal population exchange between them and eliminating a significant loss channel in devices. In conjugated polymers, this gap has proved resistant to modification. Here, we introduce a general approach to reduce the singlet-triplet energy gap in fully conjugated polymers, using a donor-orthogonal acceptor motif to spatially separate electron and hole wave functions. This new generation of conjugated polymers allows for a greatly reduced exchange energy, enhancing triplet formation and enabling thermally activated delayed fluorescence. We find that the mechanisms of both processes are driven by excited-state mixing between π-π*and charge-transfer states, affording new insight into reverse intersystem crossing.
RESUMEN
Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.
RESUMEN
A reinforcement learning agent that autonomously explores its environment can utilize a curiosity drive to enable continual learning of skills, in the absence of any external rewards. We formulate curiosity-driven exploration, and eventual skill acquisition, as a selective sampling problem. Each environment setting provides the agent with a stream of instances. An instance is a sensory observation that, when queried, causes an outcome that the agent is trying to predict. After an instance is observed, a query condition, derived herein, tells whether its outcome is statistically known or unknown to the agent, based on the confidence interval of an online linear classifier. Upon encountering the first unknown instance, the agent "queries" the environment to observe the outcome, which is expected to improve its confidence in the corresponding predictor. If the environment is in a setting where all instances are known, the agent generates a plan of actions to reach a new setting, where an unknown instance is likely to be encountered. The desired setting is a self-generated goal, and the plan of action, essentially a program to solve a problem, is a skill. The success of the plan depends on the quality of the agent's predictors, which are improved as mentioned above. For validation, this method is applied to both a simulated and real Katana robot arm in its "blocks-world" environment. Results show that the proposed method generates sample-efficient curious exploration behavior, which exhibits developmental stages, continual learning, and skill acquisition, in an intrinsically-motivated playful agent.
RESUMEN
We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of motor skills in absence of an explicit teacher signal. In this approach, the acquisition of skills is driven by the information content of the sensory input signals relative to a learner that aims at representing sensory inputs using fewer and fewer computational resources. We show that, from initially random exploration of its environment, the robotic system autonomously develops a small set of basic motor skills that lead to different kinds of tactile input. Next, the system learns how to exploit the learned motor skills to solve supervised texture classification tasks. Our approach demonstrates the feasibility of autonomous acquisition of tactile skills on physical robotic platforms through curiosity-driven reinforcement learning, overcomes typical difficulties of engineered solutions for active tactile exploration and underactuated control, and provides a basis for studying developmental learning through intrinsic motivation in robots.
RESUMEN
BACKGROUND: Morphine and its derivatives are key drugs in pain control. Despite its well-known analgesic properties morphine at high concentrations may be proalgesic. Particularly, short-lasting painful sensations have been reported upon dermal application of morphine. To study a possible involvement of TRP receptors in the pro-nociceptive effects of morphine (0.3 - 10 mM), two models of nociception were employed using C57BL/6 mice and genetically related TRPV1 and TRPA1 knockout animals, which were crossed and generated double knockouts. Hindpaw skin flaps were used to investigate the release of calcitonin gene-related peptide indicative of nociceptive activation. RESULTS: Morphine induced release of calcitonin gene-related peptide and sensitized the release evoked by heat or the TRPA1 agonist acrolein. Morphine activated HEK293t cells transfected with TRPV1 or TRPA1. Activation of C57BL/6 mouse dorsal root ganglion neurons in culture was investigated with calcium imaging. Morphine induced a dose-dependent rise in intracellular calcium in neurons from wild-type animals. In neurons from TRPV1 and TRPA1 knockout animals activation by morphine was markedly reduced, in the TRPV1/A1 double knockout animals this morphine effect was abrogated. Naloxone induced an increase in calcium levels similar to morphine. The responses to both morphine and naloxone were sensitized by bradykinin. CONCLUSION: Nociceptor activation and sensitization by morphine is conveyed by TRPV1 and TRPA1.
