This study investigates the effect of using date seed-based additive on the performance of water-based drilling fluids (WBDFs). Specifically, the effects of date pit (DP) fat content, particle size, and DP loading on the drilling fluids density, rheological properties, filtration properties, and thermal stability were investigated. The results showed that dispersion of particles less than 75 μm DP into the WBDFs enhanced the rheological as well as fluid loss control properties. Optimum fluid loss and filter cake thickness can be achieved by addition of 15–20 wt % DP loading to drilling fluid formulation.

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Flame spray pyrolysis (FSP) can rapidly synthesize nanomaterials with desired physical and chemical properties. However, the broad parameter space of FSP and postcharacterizations of the synthesized nanomaterials can slow down the material discovery process. In this work, we applied laser-induced breakdown spectroscopy (LIBS) and machine learning (ML) models to characterize catalysts' properties, which can decrease the number of postcharacterizations. The LIBS spectra are used as the descriptor to predict crystalline phase information, lattice constants (Å), and oxygen vacancy percentages (OV % s) of Ce−Mn−Zr solid solution catalysts by support vector classifiers and regressors. The balanced accuracies of making a correct prediction of phase information can reach 0.796, 0.829, 0.765, and 0.886 for tetragonal ZrO 2 , tetragonal α-MnO 2 , tetragonal β-MnO 2 , and tetragonal Mn 3 O 4 phases, respectively. The lattice constants and OV % s of the testing samples can be predicted with root-mean-squared errors of 0.04 and 0.05, respectively. These accuracies indicate that ML models can identify an inferred relation between emission spectra and catalysts' quality to determine the phase information, lattice constant (Å), and OV % of solid solution samples. Implementing LIBS and ML to FSP can provide rapid optimization of process parameters and rational guidance on novel material synthesis.

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