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.
The oxidation of organic compounds by nitrate radicals (NO3) in the atmospheric aqueous phase makes significant contributions to the production of secondary organic aerosols (SOA) and brown carbon (BrC). However, relevant kinetic parameters remain scarce, particularly in aerosol liquid water (ALW), where high concentrations of inorganic ions coexist. In this study, we developed a predictive model to estimate the aqueous-phase reaction rate constants between NO3 and organic compounds (kNO3) using a novel machine learning (ML)-based approach. By simultaneously considering the chemical properties of the reactants and experimental conditions, this model enables an accurate prediction of kNO3 across ionic strength (I) ranges of 0–6 M, while also accounting for the influence of different ionic species. The model's predictive accuracy, generalization ability, and applicability domain (AD) are evaluated, followed by a mechanistic interpretation via Shapley additive explanation (SHAP) analysis. In summary, this study provides a valuable supplementary tool for estimating kinetic parameters in both cloud droplets and ALW. As an application, the model is employed to predict kNO3 values for phenolic compounds emitted from biomass burning (BB), extending the currently available data set for atmospheric modeling.
We propose a three-stage framework named as Recovery-Informed Strategy Enhancement (RISE) to forecast the recovery of Chinese outbound tourism following the coronavirus disease 2019 pandemic. The framework decomposes the forecasts into three parts: the initial forecasts, the terminal forecasts and the recovery curve forecasts that connect the two points. We integrate multiple sources of information and employ forecast combination techniques in all stages, enhancing both the accuracy and robustness of recovery forecasts. Compared with conventional forecasting approaches, our framework provides a structured and transparent pipeline to integrate model-based forecasts with expert-informed judgment under structural breaks and high uncertainty. Our findings demonstrate the effectiveness of this framework, offering an adaptable tool for recovery trajectory forecasting in post-crisis contexts.