The urgent need to mitigate rising global CO₂ emissions demands the development of efficient carbon capture technologies. This study addresses the persistent challenge of sintering-induced performance degradation in CaO-based sorbents during high-temperature CO₂ capture. A novel solvent/nonsolvent synthetic strategy to fabricate CaO/CaAl-layered double oxide (LDO) composites was developed, where CaAl-LDO serves as a nanostructural stabilizer. The CaAl-LDO precursor enables atomic-level dispersion of components, which upon calcination forms a Ca₁₂Al₁₄O₃₃ “rigid scaffold” that spatially confines CaO nanoparticles and effectively mitigates sintering. Thermogravimetric analysis results demonstrate exceptional cyclic stability; the composite achieves an initial CO₂ uptake of 14.5 mmol/g (81.5% of theoretical capacity) and retains 87% of its capacity after 30 cycles. This performance significantly outperforms pure CaO and CaO/MgAl-LDO composites. Physicochemical characterization confirms that structural confinement preserves mesoporous channels, ensuring efficient CO₂ diffusion. This work establishes a scalable, instrumentally simple route to high-performance sorbents, offering an efficient solution for carbon capture in energy-intensive industries such as power generation and steel manufacturing.

A novel verifiable privacy preservation scheme for outsourcing medical image to cloud through ROI based crypto-watermarking is proposed in the paper. In the proposed scheme, data owner firstly carries out substitution of S-box for the region of interest (ROI) of medical image, and then separates the image into 4 most significant bits (MSBs) plane and 4 least significant bits (LSBs) plane images. Secondly, the hash value of ROI is embedded into the two separated bit plane images using reversible watermarking algorithm. Lastly, some selected hash values are transformed into the initial parameters of chaotic maps, and the two sharing secrets, which are produced through chaos based encryption algorithm, are finally outsourced to two different cloud servers. For authorized users, they can get shares from different cloud servers, and then can losslessly recover the original medical image through a series of decryption operations and extraction of watermarking. Furthermore, the users can verify whether the original image is completely reconstructed or not, they even can locate the tampered parts inside ROI if anyone of the sharing secrets is damaged. Some experiments analyses and comparisons are given to show the security and effectiveness of proposed scheme.