## 1 Introduction  
Anthropogenic greenhouse gas (GHG) emissions have recently reached critically high levels, significantly contributing to global warming and causing long-term changes in Earth's climate system. To mitigate these effects, reducing atmospheric CO\({}_{2}\) concentrations has become an urgent priority [1]. One effective solution is carbon capture and storage (CCS), where CO\({}_{2}\) is either utilized for enhanced oil recovery (EOR) or permanently stored in deep saline aquifers. During this process, CO\({}_{2}\) diffuses into the surrounding brine due to concentration gradients, following the natural drive toward thermodynamic equilibrium [2]. This dissolution reduces the buoyancy-driven vertical migration of CO\({}_{2}\), leading to greater plume stability and more secure sequestration. A detailed understanding of the diffusion process enhances the accuracy of CO\({}_{2}\) transport models, supports optimized storage design,and reduces the potential for long-term leakage--ultimately improving the environmental and operational safety of CCS and EOR initiatives [3]. The diffusion coefficient (DC) is a crucial parameter in determining the rate of CO2 movement into the brine. Higher DC values correspond to faster and more efficient diffusion, contributing to improved storage stability [4]. Since DC governs the transition of CO2 from a free to a dissolved phase, its accurate determination is essential for evaluating sequestration efficiency. Precise measurement under varying reservoir conditions--particularly temperature, pressure, and salinity--is vital for predicting CO2 behavior in saline aquifers. Three main approaches are used to determine DC: (a) experimental methods [5], (b) empirical or semi-empirical correlations [6], and (c) molecular simulations [7]. In addition, artificial intelligence (AI) techniques offer a powerful alternative by integrating data from all three sources to build robust predictive models capable of handling diverse and non-linear conditions. Several experimental methods have been developed to measure DC of gases, as reviewed in multiple studies. These methods, illustrated in Figure 1, include Taylor dispersion [8], laser-induced fluorescence [9], and pressure decay techniques [10]. Among them, the pressure decay method is widely used due to its reliability, simplicity, and suitability for controlled environments. Since the 1930s, the PVT method has also been extensively applied--often in combination with pressure decay--to assess CO2 diffusivity [11]. Experimental results show that DC is sensitive to pressure, temperature, and salinity. For example, [PERSON] et al. (2015) [12] reported values between 1.3 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s to 2.7 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s in NaCl solutions over 0.5-2.0 MPa and 25-70 \({}^{\circ}\)C. [PERSON] and [PERSON] (2006) [13] observed much higher DC values (170.7-269.8 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s) in brines at 2.6-7.5 MPa and 27-58 \({}^{\circ}\)C, likely due to natural convection. [PERSON] et al. (2013) [14], using Raman spectroscopy at 10-45 MPa, found nearly pressure-independent behavior. [PERSON] et al. (2017) [15] showed that increased salinity (20-80 ppm NaCl) at 68 \({}^{\circ}\)C significantly reduces diffusivity. Figure 1: Overview of direct and indirect methods used to measure CO2 DC in aqueous systems. The CO\({}_{2}\) diffusion coefficient in brine and pure water has been widely measured and reported. For example, [PERSON] et al. (2014) [16] used the Taylor dispersion method and reported diffusivity values approximately 16% higher than those by [PERSON] and [PERSON] (1963) [17], who applied the wetted sphere absorber technique--particularly under low salinity (1 mol\(\cdot\)L\({}^{-1}\)) and similar temperature-pressure conditions. [PERSON] et al. (2015) [12] studied CO\({}_{2}\) diffusion in 3 wt% brine under offshore conditions (0.1-5 MPa, 286.15-303.15 K), reporting values from 1.3 to 2.7 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s and observing a linear increase with both pressure and temperature.