The desirable combination of ultra-high strength and good toughness enables the secondary hardening ultra-high strength steel widely used in aerospace and energy equipment. The influence mechanism of quenching temperatures on the microstructure and mechanical properties of Co-conserving 2.2 GPa ultra-high strength steel is investigated by using scanning electron microscope（SEM），transmission electron microscope（TEM），tensile and impact testing machine. The results show that when the quenching temperature is 950 ℃， there are many undissolved M₆C carbides and unrefined grains in the matrix， resulting in lower strength （tensile strength：2072 MPa， yield strength：1873 MPa）. As the quenching temperature increases， recrystallization promotes the refinement of the matrix grains， and the number of M₆C carbides gradually decreases； such partial dissolution favors the precipitation of hardening phases， resulting in a recovery of strength； when the quenching temperature is 1030 ℃， the experimental steel has excellent combination of strength-plasticity-toughness： the tensile strength is 2251 MPa， the yield strength is 1901 MPa， the elongation is 9%，and the V-notch impact absorbed energy is 9 J. By further increasing the quenching temperature， the rapid growth of austenite grains leads to severe plasticity attenuation， with elongation of only 4.5% at 1120 ℃. Between 1030-1090 ℃， there is a competitive relationship between the dissolution of M₆C carbides and grain growth. Although higher temperature quenching promotes dissolution， the severe coarsening of grains offsets the former’s beneficial effect on toughness to enable a stable performance of strength and toughness.

Welding of cryogenic 9Ni steel is performed using NiCrMo alloy systems with different Nb and C contents. The microstructure and mechanical properties of the welded joints are investigated， and the fracture toughness of the joints under ultra-cryogenic conditions is analyzed by crack tip opening displacement （CTOD） tests. The results show that the welded joint exhibits distinct zoning characteristics. The nickel-based weld metal primarily consists of an austenitic columnar crystal matrix and secondary phases. The secondary phases include fine nanoscale banded precipitates and Nb-rich solidification phases formed in the final stage of weld pool solidification. The precipitates are mainly composed of metal carbides （MC） and Laves phases. With the increase of Nb and C content， the number and average particle size of secondary phases in the nickel-based alloy increase， leading to an improved tensile strength of the joint， but reduced cryogenic impact toughness and fracture toughness. The load-notch opening displacement （F-V） curves show that the characteristic load F_m of the joint first increases and then decreases with the addition of Nb and C， while the corresponding characteristic plastic displacement value V_p decreases monotonically with the increase of secondary phases. The fracture surface of the CTOD specimens shows the same zoning characteristics. As the Nb and C content increases， the width of the stable crack propagation region on the fracture surface gradually decreases， indicating a deterioration in the fracture toughness of the weld.

Spectral computed tomography （spectral CT） is an emerging detection technology that acquires more comprehensive tissue composition information by measuring an object’s absorption of X-rays of different energies. It plays a pivotal role in various fields such as medical diagnosis， non-destructive testing， material analysis， and security monitoring. Material decomposition algorithms are the core of spectral CT technology， aiming to decompose the composition information of different tissues from multi-energy data. These algorithms are crucial for enhancing the quality and accuracy of decomposed images. This paper reviews the data acquisition methods and mathematical models for material decomposition in spectral CT. It focuses on discussing the research progress of spectral CT material decomposition algorithms in four aspects： projection domain， image domain， direct iteration， and deep learning-based methods. It conducts an in-depth comparative analysis of the theoretical advantages， technical limitations， and current application status of various algorithms. The paper points out that the future research trends in this field include hybrid decomposition optimization in the projection domain， fusion prior constraints and multi-model data in the image domain， convergence stability improvements in direct iteration， and transferability and high generalization in deep learning.