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| 1 | Hydrophilic carbon nanotube membrane enhanced interfacial evaporation for desalination显示文摘Carbon nanotube-based(CNT-based) interfacial evaporation material is one of the most potential materials for solar desalination. Here, we studied the evaporation rate of the CNT-based membranes with different hydrophilic and hydrophobic chemical modified surfaces using molecular dynamic simulations.We found that the hydrogen bonding density among water molecules at the interface is a key factor in enhancing the evaporation rate. For a hydrophilic CNT-based membrane, the strong interactions between the membrane outer surface and the water molecules can destroy the water-water hydrogen bonding interactions at the interface, resulting in the reduction of the hydrogen bonding density, leading to an enhancement effect in evaporation rate. We also found that there is an optimal thickness for evaporation membrane. These findings could provide some theoretical guidance for designing and exploring advanced CNT-based systems with more beneficial performance in water desalination. | Yaqi Hou Qianxiao Wang Shuli Wang Miao Wang Xuemei Chen Xu Hou | 2022 | Chinese Chemical Letters2022，33，4： | 3 |
| 2 | Two-step machine learning enables optimized nanoparticle synthesis显示文摘In materials science,the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming.In this study,we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum.Combining a Gaussian process-based Bayesian optimization(BO)with a deep neural network(DNN),the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions.Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum,the DNN is used to predict the colour palette accessible with the reaction synthesis.While remaining interpretable by humans,the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties,such as the role of each reactant on the shape and amplitude of the absorbance spectrum. | Flore Mekki-Berrada Zekun Ren Tan Huang Wai Kuan Wong Fang Zheng Jiaxun Xie Isaac Parker Siyu Tian Senthilnath Jayavelu Zackaria Mahfoud Daniil Bash Kedar Hippalgaonkar Saif Khan Tonio Buonassisi Qianxiao Li Xiaonan Wang | 2021 | npj Computational Materials2021，，1： | 3 |
| 3 | Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics显示文摘Process optimization of photovoltaic devices is a time-intensive,trial-and-error endeavor,which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum.Herein,we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide(GaAs)solar cells that identifies the root cause(s)of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization.Our Bayesian network approach links a key GaAs process variable(growth temperature)to material descriptors(bulk and interface properties,e.g.,bulk lifetime,doping,and surface recombination)and device performance parameters(e.g.,cell efficiency).For this purpose,we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100×faster than numerical solvers.With the trained surrogate model and only a small number of experimental samples,our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist.As a demonstration of our method,in only five metal organic chemical vapor depositions,we identify a superior growth temperature profile for the window,bulk,and back surface field layer of a GaAs solar cell,without any secondary measurements,and demonstrate a 6.5%relative AM1.5G efficiency improvement above traditional grid search methods. | Zekun Ren Felipe Oviedo Maung Thway Siyu I.P.Tian Yue Wang Hansong Xue Jose Dario Perea Mariya Layurova Thomas Heumueller Erik Birgersson Armin G.Aberle Christoph J.Brabec Rolf Stangl Qianxiao Li Shijing Sun Fen Lin Ian Marius Peters Tonio Buonassisi | 2020 | npj Computational Materials2020，，1： | 2 |
| 4 | Author Correction:Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics显示文摘In the original version of the published Article,there was ambiguity in Eq.(1).To improve clarity,Eq.(1)has been corrected to the following. | Zekun Ren Felipe Oviedo Maung Thway Siyu I.P.Tian Yue Wang Hansong Xue Jose Dario Perea Mariya Layurova Thomas Heumueller Erik Birgersson Armin G.Aberle Christoph J.Brabec Rolf Stangl Qianxiao Li Shijing Sun Fen Lin Ian Marius Peters Tonio Buonassisi | 2020 | npj Computational Materials2020，，1： | 0 |
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