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[Objective] Photovoltaic (PV) power generation is an important starting point for China to implement the clean energy transition towards achieving the dual carbon goals, but its large installed capacity has brought new e-waste management challenges, and research on future e-waste generation and resource recovery potential is of great importance for the scientific and effective management of scraps. [Methods] Based on the distributed PV data of the National Energy Administration from 2013 to 2021, this study used the market supply A model, logistic model, and resource estimation model to predict the scrap volume and recoverable resource volume of distributed PV power generation equipment in the Beijing-Tianjin-Hebei region from 2022 to 2060. [Results] The results show that the cumulative scrap volume of distributed PV modules in Beijing, Tianjin, and Hebei could reach 4.8984 million kW, 3.930 million kW, and 83.3778 million kW respectively from 2022 to 2060. From 2022 to 2060, the cumulative total amount of recyclable materials in scrap photovoltaic modules in the Beijing-Tianjin-Hebei region will be 2.9737 million tons, and the cumulative total amount of recyclable materials in scrap photovoltaic installation systems will be 76500 to 100100 tons. Among them, other materials in the photovoltaic modules amount to 2.7926 million tons, basic metal 0.1395 million tons, key metal 37500 tons, precious metal silver 3900 tons, special metal 200 tons, and toxic metal 1100 tons; The weight of aluminum, copper, steel, and zinc of the photovoltaic installation systems is 6700-8800 tons, 12800-16800 tons, 56800-74400 tons, and 130-170 tons, respectively. [Conclusion] In the Beijing-Tianjin-Hebei region, there is a significant presence of distributed photovoltaic waste, requiring prompt and efficient management strategies. Additionally, the wide variety of materials found in photovoltaic waste presents substantial opportunities for resource recycling. A well-designed recycling system is indispensable.

Alkaloid enantiomers have different physiological and pharmacological effects, and different chiral compositions have a large impact on cigarette sensory and use safety. Studying the effects of different configurations of alkaloid enantiomers on cigarette sensory is important for the development of tobacco products, but the chiral separation of alkaloid enantiomers has become the core problem. Here, the application of a C18 column coupled with a polysaccharide-based chiral column to separate and detect four pairs of alkaloid enantiomers in tobacco and cigarette smoke of different brands was investigated. Results showed that the tandem technology of the two columns enabled the baseline separation of four pairs of alkaloid enantiomers. The resolution of enantiomers of alkaloids in different tobacco samples differed in content and ratio, showing different chiral distribution characteristics. According to the different distribution characteristics of alkaloid enantiomers, tobacco samples of different brands can be distinguished and real cigarettes can be identified.

The induced polarization (IP) method holds a strong potential to better characterize the critical zone of our planet especially in areas characterized by multi‐phase flow. Power‐law relationships between the bulk, surface, and quadrature conductivities versus the pore water saturation are potentially useable to map the subsurface water content distribution. However, the saturation exponents n and p in these power‐law relationships have been observed to vary with the texture of geomaterials and the wettabilities of pore fluids. Traditional experimental setups in the laboratory do not allow to independently visualize the pore fluid distribution. Therefore, the physical interpretations of the two saturation exponents have remained unclear. We developed a novel milli‐fluidic micromodel using clay‐coated glass beads that exhibit excellent visibility and high IP response. Through laboratory experiments, we simultaneously determined the micromodel complex conductivity and acquired the corresponding pore‐scale fluid distributions generated by drainage and imbibition through such class of porous materials. Finite‐element simulations of complex conductivity based on the upscaling of the complex surface conductance of grains were conducted to determine the saturation exponents under ideal pore fluid distributions. Results indicate that saturation exponents n and p vary depending on the ganglia size of the insulating fluids. The saturation exponents n and p exhibit power‐law relationships with the change rate of pore water connectivity with saturation, which is calculated through the computation of the derivative of Euler characteristics. These findings provide a new physical explanation to the relationships between the saturation exponents and the microscopic fluid distributions within the geomaterials. Plain Language Summary: Water saturation of porous bodies can be related to the complex conductivity through power‐law relationships. The existence of these power‐law relationships has been clearly documented in the literature. They are critical in the realm of hydrogeophysics to better characterize the critical zone of the solid Earth. However, the values of the saturation exponents in these power‐laws have been observed to vary with different material textures and no underlying mechanisms have been able to explain these variations to date. This lack of physical understanding could limit the applicability of the induced polarization method to characterize the critical zone especially when immiscible fluid phases are present. To tackle this, we developed a milli‐fluidic pore model that allows investigating the saturation exponents while monitoring the pore‐scale fluid distributions. Together with numerical simulations for ideal fluid distribution cases, we found a relationship between the saturation exponents and a microscopic pore parameter called "change rate of the pore water connectivity with saturation." These findings suggest that when estimating subsurface water saturation from electrical parameters, taking the pore fluid distribution into account can significantly improve the estimation accuracy, therefore enhancing the efficiency of geo‐electrical applications for a better characterization of hydrocarbon contaminated aquifers and oil reservoirs. Key Points: A micromodel setup allows for simultaneous pore fluid visualization and complex electrical conductivity measurementThe two saturation exponents for the in‐phase and quadrature conductivities are correlated with the ganglia size of the insulating phaseA power‐law‐type relationship between the saturation exponents and the change rate of water connectivity with saturation is demonstrated [ABSTRACT FROM AUTHOR]

The mass discharge emanating from dense non‐aqueous phase liquid (DNAPL) source zones (SZs) is often used as a key metric for risk assessment. To predict the temporal evolution of mass discharge, upscaled models have been developed to approximate the relationship between the depletion of SZ and the mass discharge. A significant challenge stems from the choice of the SZ parameterization, so that a limited number of domain‐averaged SZ metrics can suffice as an input and accurately predict the complex mass‐discharge behavior. Moreover, existing deterministic upscaled models cannot quantify prediction uncertainty stemming from modeling parameterization. To address these challenges, we propose a method based on a Bayesian Neural Network (BNN) which learns the non‐linear relationship between SZ metrics and mass discharge from multiphase‐modeling training data. The proposed BNN‐based upscaled model allows uncertainty quantification since it treats trainable parameters as distributions, and does not require a manual parameterization of the SZ a‐priori. Instead, the BNN model chooses three physically meaningful SZ quantities related to mass discharge as input features. Then, we use the expected gradients method to identify the feature importance for mass‐discharge prediction. We evaluated the proposed model on laboratory‐scale DNAPL dissolution experiments. The results show that the BNN model accurately reproduces the multistage mass‐discharge profiles with fewer parameters than existing upscaled models. Feature importance analysis shows that all chosen features are important and sufficient to reproduce complex mass discharge. This model provides accurate mass‐discharge predictions and uncertainty estimation, therefore holds a great potential for probabilistic risk assessments and decision‐making. Key Points: We demonstrate a predictive Bayesian Neural Network (BNN) upscaled model for contaminant mass discharge from heterogeneous dense non‐aqueous phase liquid (DNAPL) source zones (SZs)The proposed model accurately captures complex multistage discharge and provides uncertainty bounds for its predictionsThe results highlight the importance of a minimum set of SZ features and parameterization required for accurate mass discharge prediction [ABSTRACT FROM AUTHOR]