Here, we show that the charge of the nanopores in the nanometer-thin shells of hollow porous nanocapsules can regulate the transport of charged molecules. By changing the pH of external aqueous solution, we can entrap charged molecules in nanocapsules and trigger the release of encapsulated content.

The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. However, EHF signals face challenges such as higher attenuation, diffraction, and reflective losses caused by obstacles in outdoor environments. To overcome these challenges, 6G networks must focus on system designs that enhance propagation characteristics by predicting and mitigating diffraction, reflection, and scattering losses. Strategies such as proper handovers, antenna orientation, and link adaptation techniques based on losses can optimize the propagation environment. Among the network components, aerial networks, including unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOL), are particularly susceptible to diffraction losses due to surrounding buildings in urban and suburban areas. Traditional statistical models for estimating the height of tall objects like buildings or trees are insufficient for accurately calculating diffraction losses due to the dynamic nature of user mobility, resulting in increased latency unsuitable for ultra-low latency applications. To address these challenges, this paper proposes a deep learning framework that utilizes easily accessible Google Street View imagery to estimate building heights and predict diffraction losses across various locations. The framework enables real-time decision-making to improve the propagation environment based on users’ locations. The proposed approach achieves high accuracy rates, with an accuracy of 39% for relative error below 2%, 83% for relative error below 4%, and 96% for both relative errors below 7% and 10%. Compared to traditional statistical methods, the proposed deep learning approach offers significant advantages in height prediction accuracy, demonstrating its efficacy in supporting the development of 6G networks. The ability to accurately estimate heights and map diffraction losses before network deployment enables proactive optimization and ensures real-time decision-making, enhancing the overall performance of 6G systems.

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Madanam field in Cauvery basin in the east coast of India, has fractured gneissic basement. As exploration focus moved to unconventional reservoirs, the gneissic basement of Madanam was seen as a potential reservoir. However, ambiguity existed about the fluid flow through the basement. For example, in Madanam field, one well (well A) flowed whereas another well (well B) located 8.5 km away had minor flow from the basement reservoir that lasted 2 days. The main purpose of this study was to find possible reasons for this anomalous behavior. This study was conducted by integrating sonic and image measurements with a geomechanics workflow to identify critically stressed open fractures. Further, this work aims to provide a fit-for-purpose solution to optimize and prioritize testing zone selection in near real time.

As oil and gas exploration and development forays into unconventional plays, more specifically, basement exploration, its characterization and understanding have become very important. The present study aims at understanding the reservoir quality in terms of complex mineralogy and lithology variations, porosity, fracture properties and distribution near and away from the borehole using an integrated approach with the help of elemental spectroscopy, borehole acoustic imager, borehole micro-resistivity imager, nuclear magnetic resonance and borehole acoustic reflection survey. A comprehensive petrophysical characterization of different mineralo-facies of basement was carried out using elemental spectroscopy, formation micro-resistivity imager, borehole acoustic imager and combinable magnetic resonance along with basic open-hole data. Two distinct rock groups were identified – silica rich, iron poor zones having open fractures with good fracture density, porosity and aperture and silica poor, iron rich zones with no open fractures, poor fracture density, porosity and apertures. The zones with open fractures were the prime zones identified for further testing and completion. However, the near well bore analysis could not explain the oil flow from one zone having open fractures, whereas another similar zone showed no flow. Borehole Acoustic Reflection Survey processing was attempted to understand how extent of fractures beyond the borehole wall contributed to productivity from a well. The presence of laterally continuous fracture network at an interval that coincides with the depths from which the well is flowing, in turn validated from production log data, explained fluid flow from basement. Furthermore, the absence of such network can cause no flow even though near well-bore possible open fractures are present. Present study established the fact that, identification of potential open fractured zones in basement is a lead for reservoir zone delineation, however, a lateral extent of such basement reservoir facies is the key for successful basement hydrocarbon exploration.