Characterization and surface modification of rubber from recycled tires

Les pneus en fin de cycle de vie soulèvent de graves problèmes environnementaux. Ils doivent être éliminés ou recyclés. En raison de leur structure réticulée, les pneus ne fondent pas ni ne se dissolvent. Ils sont généralement broyés en poudre (caoutchouc de pneu broyé, abrévié GTR). Ensuite, ces poudres sont mélangées avec une matrice (asphalte ou polymère thermoplastique) pour réutilisation. L'industrie du recyclage se heurte à deux problèmes principaux. En premier lieu, le contrôle de technique qualité est difficile à cause du manque de solubilité de la poudre et de moyens limités pour ces petites industries. Il est nécessaire de trouver des méthodes rapides et à faible coût pour améliorer la caractérisation des GTR. Dans le présent travail, nous avons mis l'accent sur l'utilisation de ces deux techniques et sur la spectrométrie de fluorescence-X (XRF), comme il y a des rapports dans la littérature démontrant que ceux-ci peuvent être utilisés, respectivement, pour déterminer la densité de réticulation, la composition en monomères et la composition élémentaire. Un deuxième problème est la faible adhérence entre la plupart des polymères et les GTR. Ceci résulte en un manque de résistance mécanique et une tendance à l'effritement des pièces fabriquées. Certaines études se concentrent sur l'ajout de monomère et d'initiateur au GTR, afin de faire une polymérisation in-situ de chaînes greffées sur la surface. Cependant, le poids moléculaire des greffons est inconnu et il est impossible de vérifier si celui-ci est supérieur au poids moléculaire d'enchevêtrement des chaînes. Des réactions photochimiques ont été utilisées pour greffer des chaînes polymériques terminées par un groupement thiol de poids moléculaire connu en utilisant les liaisons doubles présentes à la surface de GTR. La spectrométrie de photoélectrons induits par rayons X (XPS) et la spectroscopie infrarouge à transformée de Fourier (FTIR) ont été utilisées pour détecter les changements de surface et vérifier si le greffage a bien eu lieu. Finalement, la mesure des propriétés mécaniques a été utilisée pour évaluer l’effet du greffage avec les échantillons de GTR greffé mélangés à du polystyrène commercial.End of life tires raise severe environmental problems and must be disposed of or recycled. Due to their cross-linked structure, they do not melt or dissolve, and are usually ground into a powder (ground tire rubber or GTR) and mixed with a matrix (asphalt or a thermoplastic polymer) for reuse. The recycling industry encounters two main problems. First, quality control is difficult due to the lack of solubility of the powder and to the limited technical means of these small industries. Rapid and low-cost methods are needed to improve characterization of GTR. This work focused on the use of these two techniques and of X-ray fluorescence spectrometry (XRF), as there are reports in the literature showing than these may be used, respectively, to determine cross-link density, monomer and elemental composition. The second problem is the poor adhesion between most polymers and GTR, resulting in parts that lack mechanical strength and tend to crumble. Some studies focus on adding monomer and initiator to GTR and doing in-situ polymerization of graft chains onto the surface. However, molecular weight of grafts is unknown and it is not possible to verify if the molecular weight is above the chain entanglement molecular weight. Photochemical reactions were used to graft thiol-terminated polymer chains of known molecular weight by using free carbon double bonds that exist on the GTR surface. X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR) were used to detect the surface changes and graft degree. Mechanical properties measurement was used to monitor the treated samples blends with polystyrene matrix

Fitting magnetic field gradient with Heisenberg-scaling accuracy

We propose a quantum fitting scheme to estimate the magnetic field gradient with $N$-atom spins preparing in W state, which attains the Heisenberg-scaling accuracy. Our scheme combines the quantum multi-parameter estimation and the least square linear fitting method to achieve the quantum Cram\'{e}r-Rao bound (QCRB). We show that the estimated quantity achieves the Heisenberg-scaling accuracy. In single parameter estimation with assumption that the magnetic field is strictly linear, two optimal measurements can achieve the identical Heisenberg-scaling accuracy. Proper interpretation of the super-Heisenberg-scaling accuracy is presented. The scheme of quantum metrology combined with data fitting provides a new method in fast high precision measurements.Comment: 7 pages, 2 figure

Novel corona virus disease (COVID-19) in pregnancy: What clinical recommendations to follow?

Pregnancy is a state of partial immune suppression which makes pregnant women more vulnerable to viral infections, and the morbidity is higher even with seasonal influenza. Therefore, the COVID‐19 epidemic may have serious consequences for pregnant women. Although the vast majority of cases of COVID‐19 are currently in China, the risk of outward transmission appears to be significantly raising global concern. Human to human transmission of the virus is proven to occur,1, 2 perhaps even from asymptomatic patients,3, 4 and the mortality is substantial, especially among frail, elderly patients with comorbidities.5 Although there have been some criticisms surrounding suppression of early warnings, and slow initial response followed by heavy‐handed quarantine measures, as well as concerns expressed about the capacity to cope with the large number of patients, and shortage of protective equipment and in‐hospital infections leading to deaths among a substantial number of healthcare professionals,6, 7 China's effort to contain the disease and slow down its spread in China and world‐wide has been commendable. A large number of cases requiring hospitalization and intensive care is a serious burden even for affluent countries with well‐developed healthcare systems. However, the Chinese government, its health professionals, and the public, have set a new standard for handling the epidemic, and they have certainly contributed to reducing the potential risk of outbreak in neighboring countries with weaker healthcare systems. Furthermore, Chinese researchers and health professionals have generously shared their data, knowledge, experience and expertise that has helped to develop diagnostic tools, clinical management algorithms, set up clinical trials, and accelerate vaccine development. Clinical course and outcome of a substantial number of COVID‐19 patients have been reported, and recommendations regarding the care of such patients have been issued by several national health authorities across the world. However, the practices seem to vary considerably

Hierarchy in temporal quantum correlations

Einstein-Podolsky-Rosen (EPR) steering is an intermediate quantum correlation that lies in between entanglement and Bell non-locality. Its temporal analogue, temporal steering, has recently been shown to have applications in quantum information and open quantum systems. Here, we show that there exists a hierarchy among the three temporal quantum correlations: temporal inseparability, temporal steering, and macrorealism. Given that the temporal inseparability can be used to define a measure of quantum causality, similarly the quantification of temporal steering can be viewed as a weaker measure of direct cause and can be used to distinguish between direct cause and common cause in a quantum network.Comment: 10 pages, 3 figure

A Cross-Residual Learning for Image Recognition

ResNets and its variants play an important role in various fields of image recognition. This paper gives another variant of ResNets, a kind of cross-residual learning networks called C-ResNets, which has less computation and parameters than ResNets. C-ResNets increases the information interaction between modules by densifying jumpers and enriches the role of jumpers. In addition, some meticulous designs on jumpers and channels counts can further reduce the resource consumption of C-ResNets and increase its classification performance. In order to test the effectiveness of C-ResNets, we use the same hyperparameter settings as fine-tuned ResNets in the experiments. We test our C-ResNets on datasets MNIST, FashionMnist, CIFAR-10, CIFAR-100, CALTECH-101 and SVHN. Compared with fine-tuned ResNets, C-ResNets not only maintains the classification performance, but also enormously reduces the amount of calculations and parameters which greatly save the utilization rate of GPUs and GPU memory resources. Therefore, our C-ResNets is competitive and viable alternatives to ResNets in various scenarios. Code is available at https://github.com/liangjunhello/C-ResNetComment: After being added into fine training tricks and several key components from the current SOTA, the performance of C-ResNet may can be greatly improve