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As the demand for silicone polymers continues to grow across various industries, the need for effective recycling methods has become increasingly important, because recycling silicone products reduces landfill waste, conserves resources, and uses less energy. Chemical recycling involves the depolymerization of silicone waste into oligomers, which can then be used to produce virgin-grade silicone. While this sector of the recycling industry is still in its infancy—we estimate that 35,000 to 45,000 metric tons of silicone waste will be chemically recycled worldwide in 2024—an increasing number of companies are beginning to explore the implementation of closed-loop systems to recycle silicones. This article examines the technical options and challenges for recycling silicone polymers, the major degradation chemistries available for depolymerizing silicones, and the current industrial reality of chemical recycling of silicones.

This study investigates the rheologic properties, elastohydrodynamic film, and friction coefficients of several siloxane-based lubricants to assess their shear stability and their potential for energy efficient lubrication. Several siloxane-based polymers with alkyl, aryl, and alkyl-aryl branches were synthesized in order to examine the relationship between their molecular structures and tribological performance. Nuclear magnetic resonance spectroscopy and gel permeation chromatography were used to characterize the molecular structures and masses, respectively. Density, viscosity, elastohydrodynamic film thickness, and friction measurements were measured from 303 to 398 K. Film thickness and friction measurements were made at loads and speeds that cover the boundary, mixed, and full film lubrication regimes. These results illustrate that the shear characteristics of siloxane lubricants vary significantly with polymer length as well as branch structure and content. The findings provide quantitative insight into the features of siloxane molecular structure conducive to optimum film formation with minimum wear and elastohydrodynamic friction to enhance energy efficiency.

Several new siloxane lubricants were synthesized with linear and ring-shaped branch structures of various lengths and branch contents, aiming at a search for better molecular design for lower boundary friction and more effective surface protection against wear of materials. Their molecular structure and mass were measured by means of nuclear magnetic resonance and gel permeation chromatography, respectively. The new lubricants were compared with commercially available polysiloxanes, poly-α-olefins, and perfluoropolyether in lubricating a steel ball-on-steel disk interface using a tribotester at a load of 1.76 GPa and an entrainment speed of 0.025 m/s. The results are used to explain the effects of alkyl branch length L , pendant type J , percent of branch functional monomers Q , and degree of polymerization DP on siloxane design for the most effective boundary lubrication.

The molecular structures, rheological properties, and friction coefficients of several new siloxane-based polymers were studied to explore their traction characteristics. The molecular structures including branch content were established by nuclear magnetic resonance spectroscopy, while the molecular mass distributions were determined by gel permeation chromatography. Density, viscosity, elastohydrodynamic film formation, and friction were investigated over a temperature range of 303–398 K. Film thickness and friction measurements were studied under the conditions that are representative of boundary, mixed, and full-film lubrication regimes, aiming at maximizing traction performance and temperature stability by simultaneous optimization of the size and content of ring-shaped branch structures. This study provides quantitative insight into the effect of siloxane molecular structure on the tribological performance for traction drive applications such as continuously variable transmissions.

The liquid phase epitaxy (LPE) of the metal-organic framework (MOF) HKUST-1 has been studied for three different COOH-terminated templating organic surfaces prepared by the adsorption of self-assembled monolayers (SAMs) on gold substrates. Three different SAMs were used, mercaptohexadecanoic acid (MHDA), 4’-carboxyterphenyl-4-methanethiol (TPMTA) and 9-carboxy-10-(mercaptomethyl)triptycene (CMMT). The XRD data demonstrate that highly oriented HKUST-1 SURMOFs with an orientation along the (100) direction was obtained on MHDA-SAMs. In the case of the TPMTA-SAM, the quality of the deposited SURMOF films was found to be substantially inferior. Surprisingly, for the CMMT-SAMs, a different growth direction was obtained; XRD data reveal the deposition of highly oriented HKUST-1 SURMOFs grown along the (111) direction.

Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system “Mittelfristige Klimaprognose” (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability and with respect to the temporal evolution at the 26° N latitude. Key Points Five initialization and ensemble generation methods are tested with respect to their impact on the skill of a decadal prediction system Results show that the tested methods provide an added value for the prediction skill as compared to the reference prediction system The study deals with dynamical consistency during initialization and ocean initial state uncertainty

The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research.