Ultra-sparse view 3D tomography reconstruction with deep learning methods is an under explored area due to model limitations and insufficient training data. Herein, we introduce a learnable framework capable of reconstructing high-fidelity 3D microstructures from single-digit radiographic images (Tomo-NeRF). Tomo-NeRF is distinct from the quality enhancement and stochastic reconstruction methods and does not rely on the analytical knowledge of inverse transform for reconstruction. Instead, Tomo-NeRF is a physics based approach which utilizes the Beer-Lambert law to reconstructe samples. Enlightened by pix-elNeRF, a photorealistic novel view synthesis model, Tomo-NeRF aligns pixel and voxel in the 3D coordinate system via relating the x-ray attenuation to the material properties of the sample. The algorithm learns to predict the phase of each voxel with the grayscale value from 2D radiographic images. We train the model with a set of multi-view radiography projections along with the 3D voxel labels so that the model can learn the scene prior to reconstruction from two-view radiographic images. Obtaining thousands of 3D and 2D training datasets from real experiments for training is cost-prohibitive. Herein, we utilize a numerical simulator to create synthetic data. Synthetic reconstructions are combined with physics based equations (e.g. Beers-Lambert) to generate artificial radiographic images for the 2D training datasets. To demonstrate the functionality of Tomo-NeRF and the artificial training datasets, we conduct real full-view tomography imaging experiments on a two-phase granular ceramic proppants with mono-dispersed sizes. These ceramic proppants are made of bauxite and are commonly utilized for high temperature heat transfer from concentrated solar power plants. The bauxite particle heat exchangers are comprised of spheroid materials which can exhibit a range of aspect ratios and varying morphologies. Herein, we combine synthetic data with experimental data to evaluate the ability for Tomo-NeRF to reconstruct reconstruct 3D image from less than 10 views.