Hussaini Zahra, Lin Pin Ann, Natarajan Bharath, Zhu Wenhui, Sharma Renu
Center for Nanoscale Science and Technology, National Institute of Standards and Technology, Gaithersburg, MD 20899-6203, USA.
Center for Nanoscale Science and Technology, National Institute of Standards and Technology, Gaithersburg, MD 20899-6203, USA; Maryland NanoCenter, University of Maryland, College Park, MD 20742, USA.
Ultramicroscopy. 2018 Mar;186:139-145. doi: 10.1016/j.ultramic.2017.12.018. Epub 2017 Dec 28.
For many reaction processes, such as catalysis, phase transformations, nanomaterial synthesis etc., nanoscale observations at high spatial (sub-nanometer) and temporal (millisecond) resolution are required to characterize and comprehend the underlying factors that favor one reaction over another. The combination of such spatial and temporal resolution (up to 600 µs), while rich in information, produces a large number of snapshots, each of which must be analyzed to obtain the structural (and thereby chemical) information. Here we present a methodology for automated quantitative measurement of real-time atomic position fluctuations in a nanoparticle. We leverage a combination of several image processing algorithms to precisely identify the positions of the atomic columns in each image. A geometric model is then used to measure the time-evolution of distances and angles between neighboring atomic columns to identify different phases and quantify local structural fluctuations. We apply this technique to determine the atomic-level fluctuations in the relative fractions of metal and metal-carbide phases in a cobalt catalyst nanoparticle during single-walled carbon nanotube (SWCNT) growth. These measurements provided a means to obtain the number of carbon atoms incorporated into and released from the catalyst particle, thereby helping resolve carbon reaction pathways during SWCNT growth. Further we demonstrate the use of this technique to measure the reaction kinetics of iron oxide reduction. Apart from reducing the data analysis time, the statistical approach allows us to measure atomic distances with sub-pixel resolution. We show that this method can be applied universally to measure atomic positions with a precision of 0.01 nm from any set of atomic-resolution video images. With the advent of high time-resolution direct detection cameras, we anticipate such methods will be essential in addressing the metrology problem of quantifying large datasets of time-resolved images in future.
对于许多反应过程，如催化、相变、纳米材料合成等，需要在高空间（亚纳米）和时间（毫秒）分辨率下进行纳米尺度观测，以表征和理解有利于一种反应而非另一种反应的潜在因素。这种空间和时间分辨率（高达600微秒）的结合虽然信息丰富，但会产生大量快照，每个快照都必须进行分析才能获得结构（进而化学）信息。在此，我们提出一种用于自动定量测量纳米颗粒中实时原子位置波动的方法。我们利用多种图像处理算法的组合来精确识别每张图像中原子列的位置。然后使用几何模型来测量相邻原子列之间距离和角度的时间演化，以识别不同相并量化局部结构波动。我们应用此技术来确定钴催化剂纳米颗粒在单壁碳纳米管（SWCNT）生长过程中金属和金属碳化物相相对比例的原子级波动。这些测量提供了一种获取掺入催化剂颗粒和从催化剂颗粒释放的碳原子数量的方法，从而有助于解析SWCNT生长过程中的碳反应途径。此外，我们展示了该技术用于测量氧化铁还原反应动力学的应用。除了减少数据分析时间外，统计方法还使我们能够以亚像素分辨率测量原子距离。我们表明，这种方法可以普遍应用于从任何一组原子分辨率视频图像中精确测量原子位置，精度可达0.01纳米。随着高时间分辨率直接检测相机的出现，我们预计此类方法对于解决未来量化大量时间分辨图像数据集的计量问题将至关重要。
