Mechanical gradients are important as tough joints, for strain field engineering in printable electronics, for actuators, and for biological studies, yet they are difficult to prepare and quantitatively characterize. We demonstrate the additive fabrication of gradient bioinspired nanocomposites based on stiff, renewable cellulose nanofibrils that are bottom-up toughened via a tailor-made copolymer. Direct filament writing of different nanocomposite hydrogels in patterns, and subsequent healing of the filaments into continuous films while drying leads to a variety of linear, parabolic and striped bulk gradients. In situ digital image correlation under tensile deformation reveals important differences in the strain fields regarding asymmetry and step heights of the patterns. We envisage that merging top-down and bottom-up structuring of nanocellulose hybrids opens avenues for aperiodic and multiscale, bioinspired nanocomposites with optimized combinations of stiffness and toughness.
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http://dx.doi.org/10.1002/anie.201511512 | DOI Listing |
Sci China Life Sci
December 2024
State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
Salivary proteins serve multifaceted roles in maintaining oral health and hold significant potential for diagnosing and monitoring diseases due to the non-invasive nature of saliva sampling. However, the clinical utility of current saliva biomarker studies is limited by the lack of reference intervals (RIs) to correctly interpret the testing result. Here, we developed a rapid and robust saliva proteome profiling workflow, obtaining coverage of >1,200 proteins from a 50-µL unstimulated salivary flow with 30 min gradients.
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December 2024
Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.
Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO), Ozone (O), Sulphur Dioxide (SO), Fine Particles Matter (PM), Coarse Particles Matter (PM), and Ammonia (NH).
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December 2024
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration.
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December 2024
Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, Delhi, 110089, India.
This study explores the thermal conductivity and viscosity of water-based nanofluids containing silicon dioxide, graphene oxide, titanium dioxide, and their hybrids across various concentrations (0 to 1 vol%) and temperatures (30 to 60 °C). The nanofluids, characterized using multiple methods, exhibited increased viscosity and thermal conductivity compared to water, with hybrid nanofluids showing superior performance. Graphene oxide nanofluids displayed the highest thermal conductivity and viscosity ratios, with increases of 52% and 177% at 60 °C and 30 °C, respectively, for a concentration of 1 vol% compared to base fluid.
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November 2024
KYAMOS Ltd., 37 Polyneikis Street, Strovolos, Nicosia 2047, Cyprus.
: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. : In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. The network's performance was evaluated under both ideal and non-ideal conditions, incorporating noise and background temperature variations.
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