Indian monsoon variability in the Mahanadi Basin over the last two glacial cycles and its implications on the Indonesian throughflow

The orbital-scale variability of the Indian summer monsoon(ISM)has been influenced by multiple factors,such as atmospheric CO_(2)concentration,global ice volume,and insolation.Proxies for weathering activity and paleo-productivity provide potential insights into the driving forces of its variability.We documented multi-proxy data at IODP Site U1445,located in the Mahanadi Basin of the northwestern Bay of Bengal,to find out ISM variability over the last 200 ka.The proxy records,such as Nd/Sr isotopes of detrital particles,clay mineral compositions of the fine-grained sediments,biogenic opal and CaCO_(3),organic carbon contents,and carbon isotopes of organic matter,represent sediment sources,weathering patterns,and paleo-productivity related to the ISM variability.Detrital Nd/Sr isotope data and clay mineral compositions suggest that the sediments at Site U1445 originated mainly from the Ganges,Brahmaputra,and Meghna rivers without dramatic provenance change between the glacial and interglacial periods.The weathering activity inferred from clay mineral compositions and the paleo-productivity shift reconstructed by biogenic opal and CaCO_(3)contents suggest that the land-sea interactions were closely linked to the ISM precipitation between the glacial and interglacial periods.High precipitation by the strong ISM resulted in intense chemical weathering and dominant biogenic opal deposition during the interglacial periods.In contrast,low precipitation by the weak ISM led to reduced chemical weathering and predominant CaCO_(3)deposition during the glacial periods.Further,the ISM variability driving the land-sea interactions in the Mahanadi Basin was modulated by the Indonesian throughflow(ITF).Our study emphasizes the role of low-latitude forcing of climatic changes in the strong relationship between the ISM and ITF over orbital periods,providing a base for future investigations.

Deep-learning-based inverse design model for intelligent discovery of organic molecules

The discovery of high-performance functional materials is crucial for overcoming technical issues in modern industries.Extensive efforts have been devoted toward accelerating and facilitating this process,not only experimentally but also from the viewpoint of materials design.Recently,machine learning has attracted considerable attention,as it can provide rational guidelines for efficient material exploration without time-consuming iterations or prior human knowledge.In this regard,here we develop an inverse design model based on a deep encoder-decoder architecture for targeted molecular design.Inspired by neural machine language translation,the deep neural network encoder extracts hidden features between molecular structures and their material properties,while the recurrent neural network decoder reconstructs the extracted features into new molecular structures having the target properties.In material design tasks,the proposed fully data-driven methodology successfully learned design rules from the given databases and generated promising light-absorbing molecules and host materials for a phosphorescent organic light-emitting diode by creating new ligands and combinatorial rules.

Morphology and electric potential-induced mechanical behavior of metallic porous nanostructures

Understanding mechanical behaviors influenced by electric potential and tribological contacts is important for verifying the robustness and reliability of applications based on metallic porous nanostructures in electrical stimulations.In this work,nickel-based metallic porous nanostructures were studied to characterize their mechanical properties and morphologically dependent contact areas during application of an electric potential using a nanoindenter.W e observed that the indentation moduli of nickel-based metallic porous nanostructures were altered by pore size and application of electric potential.In addition,the structural aspects of the surface morphology of nickel-based porous nanostructures had a critical effect on the determination of contact area.W e suggest that the relation between electric potential and the mechanical behaviors of metallic porous nanostructures can be crucial for building mechanically robust functional devices,which are influenced by electric potential.The morphological shape characteristics of metallic porous nanostructures can be alternative decisive factors for manipulation of tribological performance through regulation of contact area.

Mechanical properties of graphene oxide?silk fibroin bionanofilms via nanoindentation experiments and finite element analysis

Understanding the mechanical properties of bionanofilms is important in terms of identifying their durability.The primary focus of this study is to examine the effect of water vapor annealed silk fibroin on the indentation modulus and hardness of graphene oxide-silk fibroin(GO-SF)bionanofilms through nanoindentation experiments and finite element analysis(FEA).The GO-SF bionanofilms were fabricated using the layer-by-layer technique.The water vapor annealing process was employed to enhance the interfacial properties between the GO and SF layers,and the mechanical properties of the GO-SF bionanofilms were found to be affected by this process.By employing water vapor annealing,the indentation modulus and hardness of the GO-SF bionanofilms can be improved.Furthermore,the FEA models of the GO-SF bionanofilms were developed to simulate the details of the mechanical behaviors of the GO-SF bionanofilms.The difference in the stress and strain distribution inside the GO-SF bionanofilms before and after annealing was analyzed.In addition,the load-displacement curves that were obtained by the developed FEA model conformed well with the results from the nanoindentation tests.In summary,this study presents the mechanism of improving the indentation modulus and hardness of the GO-SF bionanofilms through the water vapor annealing process,which is established with the FEA simulation models.