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您的检索式：作者名="Mouhamad Diallo"
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| 1 | Renewable Energy Powered Membrane Process for Removal of Excess Fluoride Ions and Safe Drinking Water Supply in Rural Areas in Senegal显示文摘The analysis results made on drinking water from the rural areas of Kaolack,Diourbel and Fatick central region of Senegal showed the excess of fluoride and chloride ions.Depending on the area,the fluoride concentration in the water can reach 3.5 mg/L.The values obtained are completely above of WHO(World Health Organization)limits.The objective of this study is to assess the performances of membrane filtration units powered by renewable energies for fluoride and salinity excess removal in remote village in Senegal.Many membrane filtration units have been installed in rural areas of regions such as Kaolack,Fatick and Diourbel.These membrane filtration units are equipped by the LPRO(Low Pressure Reverse Osmosis)membranes with 9 m^(2)of surface.A rejection rate of fluoride and chloride ions obtained is 99.33%and 95.25%respectively.The conversion rates are ranged from 45%to 65%.These results clearly show that membrane processes can be used in Africa,especially in isolated rural areas,with the combination of renewable energies.Currently,more than twenty membrane filtration units are installed in Senegal and provide drinking water of very good quality for populations living in rural areas.The prospect is to expand it on a larger scale,which is already underway,with the construction of a desalination plant in Dakar. | Mohamad Moustapha Dieme Saidou Nourou Diop Mouhamadou Abdoulaye Diallo Amadou Dieye Amadou Lamine Diouf Courfia Kéba Diawara | 2022 | Journal of Environmental Science and Engineering(B)2022，11，6： | 0 |
| 2 | Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification显示文摘Machine learning(ML)has become a valuable tool to assist and improve materials characterization,enabling automated interpretation of experimental results with techniques such as X-ray diffraction(XRD)and electron microscopy.Because ML models are fast once trained,there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness,which creates broad opportunities for rapid learning and information extraction from experiments.Here,we demonstrate such a capability with the development of autonomous and adaptive XRD.By coupling an ML algorithm with a physical diffractometer,this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases.We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times.The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer.Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation. | Nathan J.Szymanski Christopher J.Bartel Yan Zeng Mouhamad Diallo Haegyeom Kim Gerbrand Ceder | 2023 | npj Computational Materials2023，，1： | 0 |
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