Cement production contributes 8 % of global industrial carbon emissions, underscoring the urgent need for innovative strategies to mitigate its environmental impact. Super Sulfated Cement (SSC) is a promising low-carbon alternative, but its carbon sequestration potential remains underexplored. This study integrates biochar and zeolite into SSC to create a near-zero-carbon, high-performance composite with hierarchical transport pathways, enhancing compressive and flexural strength by 63.1 % and 43.8 %. A comprehensive mechanism for the composite's carbon sequestration is proposed, leveraging biochar's tunnel-like channels and zeolite's nano-pores, along with molecular sieve properties, to create a hierarchical pore structure. This structure facilitates CO₂ transmission to greater depths and enables lateral diffusion, increasing carbonation by 37 % and CO₂ uptake to 41.7 kg·CO₂/kg. Its Global Warming Potential is 51.08 kg·CO₂/kg, reducing emissions by 87 % and 51.1 % compared to Ordinary Portland Cement (OPC) and SSC, respectively. This study provides an innovative, scalable pathway to developing ultra-low-carbon cementitious materials, leveraging industrial and agricultural waste to enhance environmental sustainability. The findings offer actionable insights for advancing carbon capture technologies and achieving negative-carbon cement production. Synopsis: Integrating biochar and zeolite into supersulfated cement enhances CO₂ sequestration, reducing lifecycle carbon emissions and addressing solid waste valorization and air quality challenges.

Photoacoustic imaging (PAI) is an emerging functional and molecular imaging technology that has attracted much attention in the past decade. Recently, many researchers have used the vantage system from Verasonics for simultaneous ultrasound (US) and photoacoustic (PA) imaging. This was the motivation to write on the details of US/PA imaging system implementation and characterization using Verasonics platform. We have discussed the experimental considerations for linear array based PAI due to its popularity, simple setup, and high potential for clinical translatability. Specifically, we describe the strategies of US/PA imaging system setup, signal generation, amplification, data processing and study the system performance.

This work proposes a nonsingular adaptive fixed-time switching control method for a class of strict-feedback nonlinear dynamics subject to full state constraints. The peculiarity of this design lies in overcoming the singularity issue that typically appears in the existing backstepping-based fixed-time control methods caused by the iterative differentiation of fractional power terms as tracking errors approach to zero, while guaranteeing the nonviolation of full state constraints. Crucial in solving such singularity issue is to skillfully introduce a smooth switching between fractional power and integer power terms, which guarantees that fractional power term is confined within a positive interval all the time. An asymmetric time-varying barrier Lyapunov function is delicately incorporated into control design, rendering state variables to be within prescribed time-varying bounds. Besides, radial basis function neural network is employed to handle system unknown nonlinearities. It is rigorously proved that all the closed-loop signals eventually converge to small regions around origin within fixed-time. Comparative simulation results are finally given to validate the effectiveness and superiority of the proposed control strategy.