Automatic 3D image registration for nano-resolution chemical mapping using synchrotron spectro-tomography.
Jin ZhangJun HuZhisen JiangKai ZhangPeng LiuChaonan WangQingxi YuanPiero PianettaYijin LiuPublished in: Journal of synchrotron radiation (2021)
Nano-resolution synchrotron X-ray spectro-tomography has been demonstrated as a powerful tool for probing the three-dimensional (3D) structural and chemical heterogeneity of a sample. By reconstructing a number of tomographic data sets recorded at different X-ray energy levels, the energy-dependent intensity variation in every given voxel fingerprints the corresponding local chemistry. The resolution and accuracy of this method, however, could be jeopardized by non-ideal experimental conditions, e.g. instability in the hardware system and/or in the sample itself. Herein is presented one such case, in which unanticipated sample deformation severely degrades the data quality. To address this issue, an automatic 3D image registration method is implemented to evaluate and correct this effect. The method allows the redox heterogeneity in partially delithiated LixTa0.3Mn0.4O2 battery cathode particles to be revealed with significantly improved fidelity.
Keyphrases
- deep learning
- single molecule
- high resolution
- single cell
- electronic health record
- machine learning
- electron microscopy
- big data
- dual energy
- artificial intelligence
- high intensity
- computed tomography
- molecular dynamics simulations
- gold nanoparticles
- quality improvement
- ionic liquid
- neural network
- drug discovery
- high density
- electron transfer