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Silicon nitride (Si3N4) ceramic materials are increasingly being used in deep-sea pressure-resistant applications because of their high compressive strength-to-weight ratio. In the present study, Si3N4 ceramic floatation spheres with an outer diameter of approximately 101 mm are successfully batch produced and evaluated. The implementation method was to prepare Si3N4 ceramic hemispherical housings and pair them together. In order to improve the safety of the joint, the hemispherical Si3N4 housings were gradually thickened from 1.80 to 2.50 mm at the equator near the joining surface, based on a 3D model with additive manufacturing technology. The weight-to-displacement ratio of the prepared floatation sphere is approximately 0.34 g/cm3. The flexural strength, compressive strength of the material and the collapse strength of a number of Si3N4 floatation spheres were tested to be 1150, 3847, and 205 MPa, respectively, to confirm the reliability of the process. Additional sustained and cyclic hydrostatic pressure tests simulating the full ocean depth working conditions are carried out on several Si3N4 floatation spheres, which perform very well and do not fail.

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A Mars Surface Composition Detector (MarSCoDe) instrument mounted on Zhurong rover of Tianwen-1, adopts Laser-Induced Breakdown Spectroscopy (LIBS), with no sample preparation or dust and coatings ablation required, to conduct rapid multi-elemental analysis and characterization of minerals, rocks and soils on the surface of Mars. To test the capability of MarSCoDe LIBS measurement and quantitative analysis, some methods of multivariate analysis on olivine samples with gradient concentrations were inspected based on the spectra acquired in a Mars-simulated environment before the rover launch in 2020. Firstly, LIBS spectra need preprocessing, including background subtraction, random signal denoising, continuum baseline removal, spectral drift correction and wavelength calibration, radiation calibration, and multi-channel spectra subset merging. Then, the quantitative analysis with univariate linear regression (ULR) and multivariate linear regression (MLR) are performed on the characteristic lines, while principal component regression (PCR), partial least square regression (PLSR), ridge, least-absolute-shrinkage-and-selection-operator (LASSO) and elastic net, and nonlinear analysis with back-propagation (BP) are conducted on the entire spectral information. Finally, the performance on the quantitative olivine analyzed by MarSCoDe LIBS is compared with the mean spectrum and all spectra for each sample and evaluated by some statistical indicators. The results show that: (1) the calibration curve of ULR constructed by the characteristic line of magnesium and iron indicates the linear relationship between the spectral signal and the element concentration, and the limits of detection of forsterite and fayalite is 0.9943 and 2.0536 (c%) analyzed by mean spectra, and 2.3354 and 3.8883 (c%) analyzed by all spectra; (2) the R2 value on the calibration and validation of all the methods is close to 1, and the predicted concentration estimated by these calibration models is close to the true concentration; (3) the shrinkage or regularization technique of ridge, LASSO and elastic net perform better than the ULR and MLR, except for ridge overfitting on the testing sample; the best results can be obtained by the dimension reduction technique of PCR and PLSR, especially with PLSR; and BP is more applicable for the sample measured with larger spectral dataset.

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