Considerations in Applying Neutron Depth Profiling (NDP) to Li-Ion Battery Research

Li distribution within micron-scale battery electrode materials is quantified with neutron depth profiling (NDP). This method allows the determination of intra- and inter-electrode parameters such as lithiation efficiency, electrode morphology...

Chemiresistors Based on Li-Doped CuO–TiO2 Films

Chemiresistors based on thin films of the Li-doped CuO–TiO2 heterojunctions were synthesized by a 2-step method: (i) repeated ion beam sputtering of the building elements (on the Si substrates and multisensor platforms); and (ii) thermal annealing in flowing air. The structure and composition of the films were analyzed by several methods: Rutherford Backscattering (RBS), Neutron Depth Profiling (NDP), Secondary Ion Mass Spectrometry (SIMS), and Atomic Force Microscopy (AFM), and their sensitivity to gaseous analytes was evaluated using a specific lab-made device operating in a continuous gas flow mode. The obtained results showed that the Li doping significantly increased the sensitivity of the sensors to oxidizing gases, such as NO2, O3, and Cl2, but not to reducing H2. The sensing response of the CuO–TiO2–Li chemiresistors improved with increasing Li content. For the best sensors with about 15% Li atoms, the detection limits were as follows: NO2 → 0.5 ppm, O3→ 10 ppb, and Cl2→ 0.1 ppm. The Li-doped sensors showed excellent sensing performance at a lower operating temperature (200 ∘C); however, even though their response time was only a few minutes, their recovery was slow (up to a few hours) and incomplete.

NEUTRON DEPTH PROFILE CALCULATIONS USING ARTIFICIAL NEURAL NETWORKS

Neutron depth profiling (NDP) is a non-destructive technique used for identifying the concentration of impurity isotopes below the sample surface. NDP is carried out by detection of the emitted charged particles resulting from bombarding the sample with neutrons. NDP specifies the isotopic concentration versus the sample depth for a few micrometers below the surface. The sample is bombarded inside a research reactor using a thermal neutron beam. Charged particles like alpha particles or protons are produced from the neutron induced reactions in the sample. Each neutron isotopic interaction produces a certain Q, indicating a specific kinetic energy for the emitted charged particle. As the charged particle travels through the sample to eject the surface, it loses energy to atoms (electrons) on its path. The charged particle energy loss holds information regarding the number of atoms by which the emitted particle passed, thus indicating its original depth. The purpose of this work is to check the capability of Artificial Neural Networks (ANNs) in predicting the boron concentration profile across a boro-silicate sample of thickness 3.5 μm divided into 10 layers. Each layer included different boron concentration than the other. Also, the boron concentration had the values {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. Training, validation, and test data were generated synthetically using MCNP6 in which the boron concentrations varied in the layer number from one sample to another. MCNP6 model consisted of a silicon barrier detector, boro-silicate sample, chamber body and an idealized thermal neutron source. The detector, sample, and the source were located in a voided chamber. The samples were irradiated with a 0.025 eV monoenergetic thermal neutron beam from a monodirectional disk source. To cover the whole area of the samples, the thermal neutron beam had a radius of 3 cm. The silicon detector active volume was modelled as a 100 μm thick and 3 cm radius facing the sample directly. The sample, beam, and the detector were placed on the same axis. Ten ANN regression models were developed, one for each layer boron concentration prediction where the input for each model was the alpha spectrum read by the detector, while the output was the boron concentration for each layer. Results showed regression values higher than 0.94 for all of the developed models. ANNs proved its capability of predicting the boron profile form the alpha spectrum read by the detector regarding neutron depth profiling in a boro-silicate samples.