Article
Nanoscience & Nanotechnology
Youngju Son, Byung Hyo Kim, Back Kyu Choi, Zhen Luo, Joodeok Kim, Ga-Hyun Kim, So Jung Park, Taeghwan Hyeon, Shafigh Mehraeen, Jungwon Park
Summary: The formation mechanism and diffusion behavior of silver nanoparticles in a phase-separated medium are investigated using liquid phase transmission electron microscopy and many-body dissipative particle dynamics simulations. The results suggest that the motion of silver nanoparticles at the interfaces is highly influenced by polymer interaction and exhibits superdiffusive dynamics.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Nanoscience & Nanotechnology
Utkarsh Anand, Tanmay Ghosh, Zainul Aabdin, Nandi Vrancken, Hongwei Yan, XiuMei Xu, Frank Holsteyns, Utkur Mirsaidov
Summary: A machine learning-based approach was developed to accurately identify collapsed nanostructures, and a method to reverse pattern collapse was demonstrated, providing a versatile platform for the high-yield fabrication of high-aspect-ratio nanoscale semiconductor devices.
ACS APPLIED NANO MATERIALS
(2021)
Article
Chemistry, Multidisciplinary
Hansen Zhao, Feng Ge, Sichun Zhang, Xinrong Zhang, Yan He
Summary: The study introduces a modeless preprocessing framework for single particle tracking (SPT) analysis based on the historical information of the particles. By assigning history to each data point and performing unsupervised clustering in the vector space, the inner heterogeneity of particle motion emerges as a colored trajectory, providing valuable information for further model-based analysis.
SCIENCE CHINA-CHEMISTRY
(2021)
Article
Chemistry, Multidisciplinary
Maria A. Vratsanos, Nathan C. Gianneschi
Summary: This study presents the direct observation and quantification of a water-in-oil emulsion and its destabilization, as well as the impact of additives on these processes. The use of liquid phase transmission electron microscopy allows for excellent spatial and temporal resolution, providing insights into the behavior of emulsions at the nanoscale. The findings highlight the significance of understanding emulsion behavior for improving performance and formulations.
Article
Computer Science, Artificial Intelligence
Miguel Fabian Romero Rondon, Lucile Sassatelli, Ramon Aparicio-Pardo, Frederic Precioso
Summary: This paper investigates the prediction of user's head motion in 360 degrees videos using only past user's positions and the video content. The authors re-examine existing deep-learning approaches for this problem and identify flaws. Based on the analysis, they propose a new deep neural architecture, named TRACK, which achieves state-of-the-art performance on various datasets and prediction horizons.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Multidisciplinary Sciences
Kadi L. Saar, Alexey S. Morgunov, Runzhang Qi, William E. Arter, Georg Krainer, Alpha A. Lee, Tuomas P. J. Knowles
Summary: Research has shown that proteins prone to liquid-liquid phase separation are more disordered, less hydrophobic, and have lower Shannon entropy than other protein sequences. By using machine learning models and neural network language models, it is possible to predict and understand protein phase behavior effectively.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Automation & Control Systems
Filippo Cacace, Alfredo Germani
Summary: This article studies the identification problem of linear systems from a set of noisy input-output trajectories. The problem is formulated and solved as a least-square regularized estimate on a suitable function space of finite-bandwidth operators. This abstract setting is well suited to represent a broad class of finite- and infinite-dimensional linear systems. We determine the value of the regularization parameter as a function of the amount of noise on the learning trajectories and we show how to obtain recursive and causal estimates for the case of linear dynamical systems.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Biochemical Research Methods
Yixiang Mao, Hejian Liu, Yao Wang, Eric D. Brenner
Summary: In this paper, a deep learning-based model is proposed to track the circumnutating flowering apices in the plant Arabidopsis thaliana from time-lapse videos. The proposed model significantly improves the tracking time and accuracy over other baseline tracking methods.
Article
Ecology
Simone Olivetti, Michael A. Gil, Vamsi K. Sridharan, Andrew M. Hein
Summary: Understanding how migratory animals interact with dynamic physical environments is a major challenge in migration biology. A new methodology using computational fluid dynamic modeling and animal tracking data can predict migratory movements and locomotion behavior accurately. The framework can help understand how migrants respond to local-flow conditions and how migratory behavior changes over time and space.
METHODS IN ECOLOGY AND EVOLUTION
(2021)
Article
Chemistry, Multidisciplinary
Rui Serra-Maia, Pawan Kumar, Andrew C. Meng, Alexandre C. Foucher, Yijin Kang, Khim Karki, Deep Jariwala, Eric A. Stach
Summary: Liquid-cell scanning/transmission electron microscopy (S/TEM) has been used to study nanostructure nucleation and growth, electrochemistry, and corrosion. By generating a gas bubble during electrochemical water splitting, the thickness of the liquid can be reduced to allow for atomic-scale analysis. This method enables multi-modal, nanoscale understanding of solid-state samples in liquid media.
Article
Chemistry, Multidisciplinary
Shanfei Zhang, Yizhuo Xu, Zhuofan Li, Qi Wang, Yike Li, Xiaojun Chen, Peng Chen, Zhongjiu Lu, Bin Su
Summary: This study demonstrates the on-demand adjustability of the 3D spatial distribution of magnetic permeability by controlling the flowing of Fe3O4 nanoparticle liquid. By changing the flowing routes, the 3D spatial distribution of magnetic permeability can be modified, and characteristic induction voltage signals can be detected using coils. Machine learning allows for the accurate recognition of micro-cavity structures.
Article
Multidisciplinary Sciences
Keshav Patil, Earl Joseph Jordan, Jin H. Park, Krishna Suresh, Courtney M. Smith, Abigail A. Lemmon, Yael P. Mosse, Mark A. Lemmon, Ravi Radhakrishnan
Summary: Kinases are crucial in various cellular processes and commonly mutated in cancer, their activation status can be effectively predicted through computational studies despite mutations occurring throughout the kinase domain. The results provide insights into convergent activation mechanisms in majority of studied mutations.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Chemistry, Multidisciplinary
Joanna Korpanty, Lucas R. Parent, Nathan C. Gianneschi
Summary: In the study, the radiolytic environment experienced by a polymer in water during LCTEM was examined, showing the significant mitigation effect of IPA on polymer damage and the enhancement effect of GNPs. The combination of experiments and simulations provided a generalizable strategy for assessing radiolysis mitigation or enhancement. The study highlights the caution needed for LCTEM experiments involving inorganic nanoparticles and suggests an increased use of scavengers for various LCTEM studies.
Article
Mathematics, Applied
Swati Chauhan, Swarnendu Mandal, Vijay Yadav, Prabhat K. Jaiswal, Madhu Priya, Manish Dev Shrimali
Summary: This article demonstrates the effectiveness of reservoir computing, a famous machine learning architecture, in learning a high-dimensional spatiotemporal pattern. The authors used an echo-state network to predict the phase ordering dynamics of 2D binary systems and emphasized the competence of a single reservoir in processing a large number of state variables at minimal computational training cost.
Article
Engineering, Multidisciplinary
Yuna Bae, Sungsu Kang, Byung Hyo Kim, Kitaek Lim, Sungho Jeon, Sangdeok Shim, Won Chul Lee, Jungwon Park
Summary: Nanobubbles have garnered attention in industrial applications for their long lifetime and potential as carriers at the nanoscale. The presence of surfactants greatly affects the stability and properties of nanobubbles, with real-time observations revealing the dynamic behaviors and interfacial stability of interacting nanobubbles in the presence of soluble surfactants.
Review
Multidisciplinary Sciences
Shan Jiang, Xiang Wu, Nicholas J. Rommelfanger, Zihao Ou, Guosong Hong
Summary: This review article provides an overview of recent advances in optical neuromodulation and its applications in addressing physical constraints of in vivo light delivery. The article presents the latest optical techniques for neuromodulation and explores new methods for minimal invasiveness and footprint.
NATIONAL SCIENCE REVIEW
(2022)
Article
Multidisciplinary Sciences
Fan Yang, Xiang Wu, Han Cui, Zihao Ou, Shan Jiang, Sa Cai, Qi Zhou, Bryce G. Wong, Hans Huang, Guosong Hong
Summary: The study used a bioinspired demineralization (BID) strategy to synthesize stable colloidal solutions, which can be used for multi-color luminescent tagging applications in vivo.
Article
Chemistry, Multidisciplinary
Fan Yang, Xiang Wu, Han Cui, Shan Jiang, Zihao Ou, Sa Cai, Guosong Hong
Summary: Mechanoluminescent fluids with stable colloidal solutions containing bright emitting nanocrystals have been synthesized using a suppressed dissolution approach. These rechargeable fluids can store photon energy in a reversible manner and have the potential to provide light in tissues for biological applications.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
(2022)
Article
Chemistry, Multidisciplinary
Xiaohui Song, Xingyu Zhang, Qiang Chang, Xin Yao, Mufan Li, Ruopeng Zhang, Xiaotao Liu, Chengyu Song, Yun Xin Angel Ng, Edison Huixiang Ang, Zihao Ou
Summary: The rapid advancement of transmission electron microscopy has led to revolutions in various fields, revealing the 3D information of each atom in nanoparticles and capturing the atomic structural kinetics in metal nanoparticles after phase transformation. Quantitative measurements of physical and chemical properties have been made, but high dose rates are incompatible with other ultrathin morphologies, limiting atomic electron tomography primarily to quasi-spherical nanoparticles. This study demonstrates the 3D atomic structure of a complex core-shell nanowire and shows that a new superthin noble metal layer can mitigate electron beam damage on ultrathin nanowires.
Article
Biochemistry & Molecular Biology
Gabriel R. Burks, Lehan Yao, Falon C. Kalutantirige, Kyle J. Gray, Elizabeth Bello, Shreyas Rajagopalan, Sarah B. Bialik, Jeffrey E. Barrick, Marianne Alleyne, Qian Chen, Charles M. Schroeder
Summary: This study investigates the structural and mechanical properties of leafhopper brochosomes using a combination of techniques including atomic force microscopy, electron microscopy, electron tomography, and machine learning-based image analysis. The results reveal that brochosomes are rigid hollow spheres with characteristic dimensions and morphologies determined by the leafhopper species. The study also provides insights into the compression modulus of brochosomes, which is consistent with crystalline proteins. These findings contribute to a better understanding of the nanostructured biological materials.
Review
Chemistry, Multidisciplinary
Zhiheng Lyu, Lehan Yao, Wenxiang Chen, Falon C. Kalutantirige, Qian Chen
Summary: This review summarizes recent research efforts in the application of electron microscopy (EM) to soft nanomaterials, including biological materials. The authors demonstrate how advancements in both EM hardware and software have provided new insights into the formation, assembly, and functioning of these materials at the nanometer scale or higher resolution. The review covers various techniques, such as standard two-dimensional imaging, electron tomography, electron diffraction, and in situ EM, as well as state-of-the-art technologies for characterizing soft materials and their applications. The integration of machine learning with EM is also discussed. The authors hope to inspire more development and utilization of EM-based characterization methods for soft nanomaterials in academic research and industry.
Article
Multidisciplinary Sciences
Ahyoung Kim, Thi Vo, Hyosung An, Progna Banerjee, Lehan Yao, Shan Zhou, Chansong Kim, Delia J. Milliron, Sharon C. Glotzer, Qian Chen
Summary: This study demonstrates quantitative control over asymmetric polymer grafting on triangular Au nanoprisms based on polymer scaling theory. The authors show that polymers can selectively adsorb onto nanoparticle surfaces already partially coated by other chains, resulting in the formation of patchy nanoparticles with broken symmetry. These asymmetric nanoparticles exhibit intriguing plasmonic properties and their formation can be accurately predicted by a developed theory.
NATURE COMMUNICATIONS
(2022)
Article
Polymer Science
Hao Yu, Falon C. Kalutantirige, Lehan Yao, Charles M. Schroeder, Qian Chen, Jeffrey S. Moore
Summary: Recent advances in chemical synthesis have provided new methods for synthesizing sequence-controlled synthetic polymers, but designing monomer sequences for desired properties is still challenging. In this study, researchers synthesized periodic polymers with repetitive segments using a sequence-controlled ring-opening metathesis polymerization (ROMP) method inspired by proteins. They found that the repetitive segment architecture greatly impacts the self-assembly behavior of these materials.
Article
Nanoscience & Nanotechnology
Binbin Luo, Ziwei Wang, Tine Curk, Garrett Watson, Chang Liu, Ahyoung Kim, Zihao Ou, Erik Luijten, Qian Chen
Summary: The crystallization of nanoparticles is studied using electron microscopy and computer simulations, providing insights into the growth modes and unifying the understanding of crystallization across different scales. The observations of crystal growth in nanoscale systems reveal layer-by-layer and rough growth modes, which can be independently controlled and have received limited attention before.
NATURE NANOTECHNOLOGY
(2023)
Review
Chemistry, Physical
Zhangying Xu, Zihao Ou
Summary: The crystallization pathways in materials have been traditionally studied through classical methods, but recent advancements in nanoscale microscopy techniques have allowed for the visualization of crystal nucleation and growth at the nanoscale. This review summarizes several crystallization pathways observed through liquid-phase transmission electron microscopy and compares them with computer simulations. Besides the classical nucleation pathway, nonclassical pathways such as formation of amorphous clusters, nucleation of crystalline phase from amorphous intermediates, and transition between multiple crystalline structures are highlighted. The importance of theory and simulation in understanding experimental systems is emphasized, and the challenges and future perspectives for investigating nanoscale crystallization pathways with in situ imaging techniques are discussed, along with potential applications in biomineralization and protein self-assembly research.
Review
Materials Science, Multidisciplinary
Qiang Chang, Yun Xin Angel Ng, Dahai Yang, Junhao Chen, Tong Liang, Sheng Chen, Xingyu Zhang, Zihao Ou, Juyeong Kim, Edison Huixiang Ang, Hongfa Xiang, Xiaohui Song
Summary: With the increasing reliance on renewable energy sources, the interest in energy storage systems, particularly for solar cells, redox flow batteries, and lithium batteries, has grown. Various diagnostic techniques, including electrochemical tests, have been used to study battery performance. In situ and operando electron microscopy methods have proven to be effective in characterizing battery materials and interfaces. This review emphasizes the importance of electron microscopy in battery research and discusses the advancements and future opportunities in this field.
ACS MATERIALS LETTERS
(2023)
Article
Biochemistry & Molecular Biology
Lihan Zhao, Wen-Jian Xie, Yin-Xiao Du, Yi-Xuan Xia, Kang-Lun Liu, Chuen Fai Ku, Zihao Ou, Ming-Zhong Wang, Hong-Jie Zhang
Summary: The dichloromethane extract of Bridelia balansae roots showed potential anticancer activity against HCT116 colorectal cancer cells. A phytochemical investigation led to the identification of 14 compounds, including the first aryltetralin lignan compound from this species. Compound 1 demonstrated a significant cytotoxic effect on HCT116 cells and inhibited cell migration, suggesting its potential application against cancer metastasis.
Article
Chemistry, Multidisciplinary
Qiang Chang, Dahai Yang, Xingyu Zhang, Zihao Ou, Juyeong Kim, Tong Liang, Junhao Chen, Sheng Cheng, Lixun Cheng, Binghui Ge, Edison Huixiang Ang, Hongfa Xiang, Mufan Li, Xiaohui Song
Summary: In this research, in situ liquid phase TEM was used to study the etching mechanism of colloidal zeolitic imidazolate framework (ZIF) nanoparticles. The etching process involves two distinct stages, resulting in the development of porous structures as well as partially and fully hollow morphologies. This study provides valuable insights into MOF particle morphology control and has the potential to lead to the development of novel MOF-based materials.
Review
Materials Science, Multidisciplinary
Qiang Chang, Yun Xin Angel Ng, Dahai Yang, Junhao Chen, Tong Liang, Sheng Chen, Xingyu Zhang, Zihao Ou, Juyeong Kim, Edison Huixiang Ang, Hongfa Xiang, Xiaohui Song
Summary: With the increasing reliance on renewable energy sources, there has been a growing interest in energy storage systems, particularly for solar cells, redox flow batteries, and lithium batteries. Various diagnostic techniques, such as electrochemical tests and electron microscopy methods, have been used to characterize battery performance and properties. This review focuses on in situ and operando electron microscopy characterization of battery materials, discussing techniques like TEM, SEM, Cryo-TEM, and 3D electron tomography. The use of these advanced electron microscopy techniques has led to significant advances in understanding battery materials and has the potential to drive future advancements in the field.
ACS MATERIALS LETTERS
(2023)
Article
Chemistry, Multidisciplinary
Lehan Yao, Hyosung An, Shan Zhou, Ahyoung Kim, Erik Luijten, Qian Chen
Summary: This study develops an automated, descriptor-free analysis workflow for TEM data, utilizing convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, revealing the synthesis-nanomorphology relationships. The method can be applied to both 2D and 3D TEM data sets and is applicable to both inorganic and organic nanomaterials.
