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| 1 | Ca^(2+)-supplying black phosphorus-based scaffolds fabricated with microfluidic technology for osteogenesis显示文摘Effective osteogenesis remains a challenge in the treatment of bone defects.The emergence of artificial bone scaffolds provides an attractive solution.In this work,a new biomineralization strategy is proposed to facilitate osteogenesis through sustaining supply of nutrients including phosphorus(P),calcium(Ca),and silicon(Si).We developed black phosphorus(BP)-based,three-dimensional nanocomposite fibrous scaffolds via microfluidic technology to provide a wealth of essential ions for bone defect treatment.The fibrous scaffolds were fabricated from 3D poly(L-lactic acid)(PLLA)nanofibers(3D NFs),BP nanosheets,and hydroxyapatite(HA)-porous SiO2 nanoparticles.The 3D BP@HA NFs possess three advantages:i)stably connected pores allow the easy entrance of bone marrow-derived mesenchymal stem cells(BMSCs)into the interior of the 3D fibrous scaffolds for bone repair and osteogenesis;ii)plentiful nutrients in the NFs strongly improve osteogenic differentiation in the bone repair area;iii)the photothermal effect of fibrous scaffolds promotes the release of elements necessary for bone formation,thus achieving accelerated osteogenesis.Both in vitro and in vivo results demonstrated that the 3D BP@HA NFs,with the assistance of NIR laser,exhibited good performance in promoting bone regeneration.Furthermore,microfluidic technology makes it possible to obtain high-quality 3D BP@HA NFs with low costs,rapid processing,high throughput and mass production,greatly improving the prospects for clinical application.This is also the first BP-based bone scaffold platform that can self-supply Ca^(2+),which may be the blessedness for older patients with bone defects or patients with damaged bones as a result of calcium loss. | Zhanrong Li Xingcai Zhang Jiang Ouyang Dandan Chu Fengqi Han Liuqi Shi Ruixing Liu Zhihua Guo Grace X.Gu Wei Tao Lin Jin Jingguo Li | 2021 | Bioactive Materials2021，6，11： | 4 |
| 2 | Developments in 4D-printing:a review on current smart materials,technologies,and applications显示文摘Recent advances in additive manufacturing(AM),commonly known as three-dimensional(3D)-printing,have allowed researchers to create complex shapes previously impossible using traditional fabrication methods.A research branch that originated from 3D-printing called four-dimensional(4D)-printing involves printing with smart materials that can respond to external stimuli.4Dprinting permits the creation of on-demand dynamically controllable shapes by integrating the dimension of time.Recent achievements in synthetic smart materials,novel printers,deformation mechanism,and mathematical modeling have greatly expanded the feasibility of 4D-printing.In this paper,progress in the 4Dprinting field is reviewed with a focus on its practical applications.We discuss smart materials developed using 4D-printing with explanations of their morphing mechanisms.Additionally,case studies are presented on self-constructing structures,medical devices,and soft robotics.We conclude with challenges and future opportunities in the field of 4D-printing. | Zhizhou Zhang Kahraman G.Demir Grace X.Gu | 2019 | International Journal of Smart and Nano Materials2019，10，3： | 2 |
| 3 | Machine Learning‑Based Detection of Graphene Defects with Atomic Precision显示文摘Defects in graphene can profoundly impact its extraordinary properties,ultimately influencing the performances of graphene-based nanodevices.Methods to detect defects with atomic resolution in graphene can be technically demanding and involve complex sample preparations.An alternative approach is to observe the thermal vibration properties of the graphene sheet,which reflects defect information but in an implicit fashion.Machine learning,an emerging data-driven approach that offers solutions to learning hidden patterns from complex data,has been extensively applied in material design and discovery problems.In this paper,we propose a machine learning-based approach to detect graphene defects by discovering the hidden correlation between defect locations and thermal vibration features.Two prediction strategies are developed:an atom-based method which constructs data by atom indices,and a domain-based method which constructs data by domain discretization.Results show that while the atom-based method is capable of detecting a single-atom vacancy,the domain-based method can detect an unknown number of multiple vacancies up to atomic precision.Both methods can achieve approximately a 90%prediction accuracy on the reserved data for testing,indicating a promising extrapolation into unseen future graphene configurations.The proposed strategy offers promising solutions for the non-destructive evaluation of nanomaterials and accelerates new material discoveries. | Bowen Zheng Grace X.Gu | 2020 | Nano-Micro Letters2020，12，12： | 1 |
| 4 | Deep learning framework for material design space exploration using active transfer learning and data augmentation显示文摘Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space.However,the applicability of conventional generative models is limited because they cannot access data outside the range of training sets.Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain.In this study,we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set.This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods.We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations.Results show that our proposed framework can provide excellent designs close to the global optima,even with the addition of a very small dataset corresponding to less than 0.5%of the initial training dataset size. | Yongtae Kim Youngsoo Kim Charles Yang Kundo Park Grace X.Gu Seunghwa Ryu | 2021 | npj Computational Materials2021，，1： | 1 |
| 5 | ?509C>T polymorphism in the TGF‐β1 gene promoter is not associated with susceptibility to and progression of colorectal cancer in Chinese显示文摘 | P.Qi C.‐P.Ruan H.Wang F.‐G.Zhou Y.‐P.Zhao X.Gu C.‐F.Gao | 2009 | Colorectal Disease2009，，： | 1 |
| 6 | Designing mechanically tough graphene oxide materials using deep reinforcement learning显示文摘Graphene oxide(GO)is playing an increasing role in many technologies.However,it remains unanswered how to strategically distribute the functional groups to further enhance performance.We utilize deep reinforcement learning(RL)to design mechanically tough GOs.The design task is formulated as a sequential decision process,and policy-gradient RL models are employed to maximize the toughness of GO.Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs.In addition,our RL approach reaches optimized functional group distributions within only 5000 rollouts,while the simplest design task has 2×10^(11)possibilities.Finally,we show that our approach is scalable in terms of the functional group density and the GO size.The present research showcases the impact of functional group distribution on GO properties,and illustrates the effectiveness and data efficiency of the deep RL approach. | Bowen Zheng Zeyu Zheng Grace X.Gu | 2022 | npj Computational Materials2022，，1： | 0 |
| 7 | Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials显示文摘It has proved challenging to represent the behavior of polymeric macromolecules as machine learning features for biomaterial interaction prediction.There are several approaches to this representation,yet no consensus for a universal representational framework,in part due to the sensitivity of biomacromolecular interactions to polymer properties.To help navigate the process of feature engineering,we provide an overview of popular classes of data representations for polymeric biomaterial machine learning while discussing their merits and limitations.Generally,increasing the accessibility of polymeric biomaterial feature engineering knowledge will contribute to the goal of accelerating clinical translation from biomaterials discovery. | Samantha Stuart Jeffrey Watchorn Frank X.Gu | 2023 | npj Computational Materials2023，，1： | 0 |
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