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Ti 2 AlNb alloys are expected to have applications in the aerospace field because of their excellent high-temperature performance and moderate density. Multi-wire arc-directed energy deposition (MWA-DED) technology enables the in-situ formation of Ti 2 AlNb alloys. However, Ti 2 AlNb alloys fabricated by MWA-DED face challenges such as compositional segregation and poor mechanical properties. In this study, high-performance Ti 2 AlNb alloys were efficiently prepared using MWA-DED technology, and their microstructure evolution was systematically analyzed. Hot-wire technology was proposed to assist in melting the TiNb wire to minimize the difference in the physical properties of the two wires. A pre-alloy droplet transfer mode with a large arc length was developed to eliminate composition segregation. The results showed that the Ti 2 AlNb alloys exhibited a homogeneous composition that matched the target Ti-22Al-23 Nb. Lots of strengthening phases (O phases higher than 90%) were precipitated throughout the sample. Temperature fields calculated from finite element simulation revealed that the precipitated phases were attributed to the "in-situ" heat treatment during deposition. The ultimate tensile strength and elongation reached 1002 MPa and 8% at room temperature, and 756 MPa and 8.3% at high temperature (650 ℃), respectively. The outstanding mechanical properties of Ti 2 AlNb alloys fabricated by MWA-DED are superior to those of cast and previously reported additively manufactured alloys. This study proposes novel ideas for additive manufacturing of high-performance Ti 2 AlNb alloys. [Display omitted] • Ti 2 AlNb alloys were efficiently in-situ manufactured by the multi-wire arc-directed energy deposition technique. • Hot-wire technology was proposed to eliminate the difference in physical properties of the two wires. • A pre-alloy droplet transfer model with high arc-length was explored to overcome compositional segregation. • Numerical simulations of heat transfer were developed to reveal the microstructure evolution mechanism. • High-performance Ti 2 AlNb alloys were obtained and the strengthening mechanisms were revealed. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a common liver malignancy with limited treatment options. Previous studies expressed the potential synergy of sorafenib and NK cell immunotherapy as a promising approach against HCC. MRI is commonly used to assess response of HCC to therapy. However, traditional MRI-based metrics for treatment efficacy are inadequate for capturing complex changes in the tumor microenvironment, especially with immunotherapy. In this study, we investigated potent MRI radiomics analysis to non-invasively assess early responses to combined sorafenib and NK cell therapy in a HCC rat model, aiming to predict multiple treatment outcomes and optimize HCC treatment evaluations. Methods: Sprague Dawley (SD) rats underwent tumor implantation with the N1-S1 cell line. Tumor progression and treatment efficacy were assessed using MRI following NK cell immunotherapy and sorafenib administration. Radiomics features were extracted, processed, and selected from both T1w and T2w MRI images. The quantitative models were developed to predict treatment outcomes and their performances were evaluated with area under the receiver operating characteristic (AUROC) curve. Additionally, multivariable linear regression models were constructed to determine the correlation between MRI radiomics and histology, aiming for a noninvasive evaluation of tumor biomarkers. These models were evaluated using root-mean-squared-error (RMSE) and the Spearman correlation coefficient. Results: A total of 743 radiomics features were extracted from T1w and T2w MRI data separately. Subsequently, a feature selection process was conducted to identify a subset of five features for modeling. For therapeutic prediction, four classification models were developed. Support vector machine (SVM) model, utilizing combined T1w + T2w MRI data, achieved 96% accuracy and an AUROC of 1.00 in differentiating the control and treatment groups. For multi-class treatment outcome prediction, Linear regression model attained 85% accuracy and an AUC of 0.93. Histological analysis showed that combination therapy of NK cell and sorafenib had the lowest tumor cell viability and the highest NK cell activity. Correlation analyses between MRI features and histological biomarkers indicated robust relationships (r = 0.94). Conclusions: Our study underscored the significant potential of texture-based MRI imaging features in the early assessment of multiple HCC treatment outcomes. [ABSTRACT FROM AUTHOR]