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Polyaniline (PANI) composites have gained momentum as supercapacitive materials due to their high energy density and power density. However, some drawbacks in their performance remain, such as the low stability after hundreds of charge-discharge cycles and limitations in the synthesis scalability. Herein, we report for the first time PANI-Graphitic oxidized carbon nitride composites as potential supercapacitor material. The biomimetic polymerization of aniline assisted by hematin, supported by phosphorous and oxygen-modified carbon nitrides (g-POCN and g-OCN, respectively), achieved up to 89% yield. The obtained PAI/g-POCN and PANI/g-OCN show enhanced electrochemical properties, such as conductivity of up to 0.0375 S/cm, specific capacitances (Cs) of up to 294 F/g (at high current densities, 5 A/g) and a stable operation after 500 charge-discharge cycles (at 3 A/g). In contrast, the biomimetic synthesis of Free PANI, assisted by stabilized hematin in cosolvents, exhibited lower performance properties (65%). Due to their structural differences, the electrochemical properties of Free PANI (conductivity of 0.0045 S/cm and Cs of up to 82 F/g at 5 A/g) were lower than those of nanostructured PANI/g-POCN and g-OCN supports, which provide stability and improve the properties of biomimetically synthesized PANI. This work reveals the biomimetic synthesis of PANI, assisted by hematin supported by modified carbon nitrides, as a promising strategy to produce nanostructured supercapacitors with high performance.
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The overuse of fossil-based resources to produce thermoplastic materials and rubbers is dramatically affecting the environment, reflected in its clearest way as global warming. As a way of reducing this, multiple efforts are being undertaken including the use of more sustainable alternatives, for instance, those of natural origin as the main feedstock alternative, therefore having a lower carbon footprint. Contributing to this goal, the synthesis of bio-based rubbers based on ß-myrcene and trans-ß-farnesene was addressed in this work. Polymyrcene (PM) and polyfarnesene (PF) were synthesized via coordination polymerization using a neodymium-based catalytic system, and their properties were compared to the conventional polybutadiene (PB) and polyisoprene (PI) also obtained via coordination polymerization. Moreover, different average molecular weights were also tested to elucidate the influence over the materials' properties. The crosslinking of the rubbers was carried out via conventional and efficient vulcanization routes, comparing the final properties of the crosslinking network of bio-based PM and PF with the conventional fossil-based PB and PI. Though the mechanical properties of the crosslinked rubbers improved as a function of molecular weight, the chemical structure of PM and PF (with 2 and 3 unsaturated double bonds, respectively) produced a crosslinking network with lower mechanical properties than those obtained by PB and PI (with 1 unsaturated double bond). The current work contributes to the understanding of improvements (in terms of crosslinking parameters) that are required to produce competitive rubber with good sustainability/performance balance.
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Deep learning, one of the fastest-growing branches of artificial intelligence, has become one of the most relevant research and development areas of the last years, especially since 2012, when a neural network surpassed the most advanced image classification techniques of the time. This spectacular development has not been alien to the world of the arts, as recent advances in generative networks have made possible the artificial creation of high-quality content such as images, movies or music. We believe that these novel generative models propose a great challenge to our current understanding of computational creativity. If a robot can now create music that an expert cannot distinguish from music composed by a human, or create novel musical entities that were not known at training time, or exhibit conceptual leaps, does it mean that the machine is then creative? We believe that the emergence of these generative models clearly signals that much more research needs to be done in this area. We would like to contribute to this debate with two case studies of our own: TimbreNet, a variational auto-encoder network trained to generate audio-based musical chords, and StyleGAN Pianorolls, a generative adversarial network capable of creating short musical excerpts, despite the fact that it was trained with images and not musical data. We discuss and assess these generative models in terms of their creativity and we show that they are in practice capable of learning musical concepts that are not obvious based on the training data, and we hypothesize that these deep models, based on our current understanding of creativity in robots and machines, can be considered, in fact, creative.
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STUDY DESIGN: A retrospective cohort study with IRB approval. INTRODUCTION: Significant blood loss remains an important concern in terms of the performance of the posterior spinal fusion in adolescent idiopathic scoliosis. Several strategies have been reported to minimize blood loss during surgery. In order to address the need to minimize blood loss without sacrificing the quality of the fusion, in our hospital, we adopted a two-step surgical approach. This surgical approach consist of the exposure and instrumentation of the lumbar region prior to and followed by an extension of the surgical incision to the thoracic region for its subsequent instrumentation. The main purpose of this study was to compare a two-step surgical approach with the one-step (standard) approach. METHODS: This study was a review of all the data on consecutive posterior spinal fusion surgeries performed by a specific two-surgeon team during 2004-2013. Demographics, surgical variables, radiographic findings, and outcomes regarding blood loss, morbidity, and the duration of the procedure were evaluated. RESULTS: Eighty-five patients underwent the standard surgical exposure, and 41 patients underwent the two-step surgical technique. With the exception of BMI, neither group showed any statistically preoperative variable significant differences. None of the postoperative outcome variables were statistically significant between both surgical approaches. CONCLUSIONS: No differences were detected in terms of using a two-step surgical approach versus the one-step standard surgical approach regarding perioperative blood loss, surgical time, or complications. LEVEL OF EVIDENCE: Level of evidence III.