Nanoconfined water—ubiquitous across both engineered nanoporous adsorbents and subsurface geological formations—plays a pivotal yet underexplored role in carbon capture and storage (CCS). This review systematically examines the physicochemical properties and functional implications of water confined within nanoporous environments, emphasizing its dualistic impact on both CO2 capture and geological CO2 storage. We first summarize recent advances from computational simulations and experimental characterizations, highlighting the altered thermodynamic and structural features, dynamic behavior, dielectric properties, and chemical reactivity of nanoconfined water. We then integrate insights from surface chemistry, materials science, and geoscience to elucidate how nanoconfined water influences CCS processes through competitive adsorption, pore accessibility, wettability, solubility, and mineralization kinetics, spanning systems from nanoporous adsorbents such as zeolites, metal–organic frameworks (MOFs), and activated carbon (AC) to unconventional formations including shale and tight sandstone. These findings also suggest opportunities for practical applications, such as guiding the design of hydrophobic MOFs for improved CO2 capture and supporting strategies to preserve caprock integrity in subsurface storage. Finally, we identify key challenges in bridging molecular-level understanding with material- and reservoir-scale performance, emphasizing the need for multiscale experimental techniques, realistic molecular modeling, and cross-disciplinary strategies to fully harness the functional potential of nanoconfined water in CCS.
While Neural Audio Codecs (NAC) have revolutionized monaural audio compression, achieving high-fidelity dual-channel coding at low bitrates remains a significant challenge. Existing approaches often rely on naive independent channel quantization, leading to phase incoherence, or entangled latent modeling, which sacrifices spatial precision for spectral energy. This paper proposes a novel dual-channel coding framework based on contentspatial disentanglement. Reframing spatial reconstruction as an informed source separation task, our architecturesynergizes a frozen, pre-trained DAC encoder for robust mono content preservation with a parameter-efficient side information encoder that predicts fine-grained time-frequency masks. To ensure precise spatial imaging, we introduce explicit physical constraints into the end-to-end training. Experimental results indicate that at low bitrates of 9 and 11 kbps, the proposed method outperforms state-of-the-art dual-mono neural baselines and industry standards in both objective spatial metrics and subjective MUSHRA evaluations.
Binaural rendering is typically assessed via timbre and localization accuracy, while its intrinsic spatial resolution remains rarely quantified. This paper proposes a perceptual evaluation method based on Minimum Audible Angle (MAA) measurements to estimate the azimuthal just-noticeable difference (JND) introduced by binaural rendering algorithms. We systematically compared several rendering algorithms across eight reference azimuths using two participant-allocation paradigms. The results show that spatial resolution is significantly influencedby Ambisonic order and choice of the rendering algorithm, with MAA thresholds systematically decreasing as the truncation order increases. Furthermore, the proposed method successfully captures physiological spatial characteristics and identifies resolution limits imposed by reference angles. While both participant-allocation paradigms yield consistent qualitative trends, the repeated-measures design provides superior data stability. These findings demonstrate that the proposed MAA-based method is an effective tool for quantifying the spatial resolutionof binaural rendering algorithms.