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BACKGROUND: Interface modification driven by supramolecular self‐assembly has been accepted as a valuable strategy for emulsion stabilization enhancement. However, there has been a dearth of comparative research on the effect of simple complexation and assembly from the perspective of the responsible mechanism. RESULTS: The present study selected zein and tannic acid (TA) as representative protein and polyphenol modules for self‐assembly (coined as TA‐modified zein particle and TA‐zein complex particle) to explore the surface properties and interfacial behavior, as well as the stability of constructed Pickering emulsions to obtain the regulation law of different modification methods on the interfacial behavior of colloidal particles. The results demonstrated that TA‐modified zein colloidal particles potentially improved the emulsifying properties. When the TA concentration was 3 mmol L−1, the optimized TA‐modified zein particle was nano‐sized (109.83 nm) and had advantageous interfacial properties, including sharply reduced surface hydrophobicity, as well as a low diffusion rate at the oil/water interface. As a result, the shelf life of Pickering emulsion containing 50% oil phase was extended to 90 days. CONCLUSION: Through multi‐angled research on the properties of the interfacial membrane, improvement of emulsion stability was a result of the formation of viscoelastic interfacial film that resulted from the decrease of absorption rate between particles and interface. Using refined regulation to investigate the role of different sample preparation methods from a mechanistic perspective. Overall, the present study has provided a reference for TA to regulate the surface properties and interface behavior of zein colloidal particles, enriched the understanding of colloidal interface assembly, and provided a theoretical basis for the quality control of interface‐oriented food systems. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Formation leak-off pressure, which sets the upper limit of the safe drilling fluid density window, is crucial for preventing wellbore accidents and ensuring safe and efficient drilling operations. The paper thoroughly examines models of drilling physics alongside artificial intelligence techniques. The study introduces a dual-driven method for predicting reservoir pore pressure by integrating long short-term memory (LSTM) and backpropagation (BP) neural networks, where the core component is the LSTM-BP neural network model. The input data for the LSTM-BP model include wellbore diameter, formation density, sonic time, natural gamma, mud content, and pore pressure. The study demonstrates the practical application of the method using two vertical wells in Block M, employing the M-1 well for training and the M-2 well for validation. Two distinct input layer configurations are devised for the LSTM-BP model to evaluate the influence of formation density on prediction accuracy. Notably, Scheme 2 omits formation density as a variable in contrast to Scheme 1. The study's results indicate that, for input layer configurations corresponding to Scenario 1 and Scenario 2, the LSTM-BP model exhibits relative error ranges of (−2.467%, 2.510%) and (−6.141%, 5.201%) on the test set, respectively. In Scenario 1, the model achieves mean squared error (MSE), mean absolute error (MAE), and R-squared (R2) values of 0.000229935, 0.011198329, and 0.92178272, respectively, on the test set. Conversely, for Scenario 2, the model demonstrates a substantial escalation of 992.393% and 240.674% in MSE and MAE, respectively, compared to Scenario 1; however, R2 diminishes by 66.920%. Utilizing the trained LSTM-BP model, predictions for formation lost pressure in Well M-2 reveal linear correlation coefficients of 0.8173 and 0.6451 corresponding to Scenario 1 and Scenario 2, respectively. These findings imply that the predictions from the Scenario 1 model demonstrate stronger alignment with results derived from formulaic calculations. These observations remain consistent for both the BP neural network algorithm and the random forest algorithm. The aforementioned research results not only highlight the elevated predictive precision of the LSTM-BP model for intelligent prediction of formation lost pressure, a product of this study, thereby furnishing valuable data points to enhance the security of drilling operations in Block M, but also underscore the necessity of deliberating both physical relevance and data correlation during the selection of input layer variables. [ABSTRACT FROM AUTHOR]

In order to address global warming, most countries in the world have established carbon neutral targets and are continuously taking action to achieve carbon neutrality. The building sector accounts for 36% of end-use energy consumption and 37% of energy-related CO2 emissions globally, so carbon mitigation in building sector is considered to be the most critical step in completing the "last mile" of global carbon neutrality. Low-carbon buildings and communities are the foundation for achieving low-carbon cities and the key transition to reach the goal of carbon neutrality. Therefore, this review aimed to: (a) provide a comprehensive review of countries' policies on low-carbon buildings and communities and a theoretical basis for improving the corresponding laws and regulations; (b) investigate low-carbon technologies related to building and community construction and operation, as well as identify the current technology gaps; (c) provide a comprehensive overview of low-carbon buildings and communities assessment systems to analyze and evaluate the economic, technical, environmental and social benefits of current building and community energy systems; and (d) identify enablers and barriers in low-carbon buildings and communities to provide direction for future research. The results of this paper can provide comprehensive insights in to further achieving low-carbon buildings and communities. [ABSTRACT FROM AUTHOR]