RESUMEN
Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines.
RESUMEN
The significant features of carbon dots (CDs), such as bright and tunable photoluminescence, high thermal stability, and low toxicity, endow them with tremendous potential for application in next generation optoelectronics. Despite great progress achieved in the design of high-performance CDs so far, the practical applications in solid-state lighting and displays have been retarded by the aggregation-caused quenching (ACQ) effect ascribed to direct π-π interactions. This review provides a comprehensive overview of the recent progress made in solid-state CD emitters, including their synthesis, optical properties and applications in light-emitting diodes (LEDs). Their triplet-excited-state-involved properties, as well as their recent advances in phosphor-converted LEDs and electroluminescent LEDs, are mainly reviewed here. Finally, the prospects and challenges of solid-state CD-based LEDs are discussed with an eye on future development. We hope that this review will provide critical insights to inspire new exciting discoveries on solid-state CDs from both fundamental and practical standpoints so that the realization of their potential in optoelectronic areas can be facilitated.
RESUMEN
Carbon dots (CDs), as emerging carbon nanomaterials, have been regarded as promising alternatives for electroluminescent light-emitting diodes (LEDs) owing to their distinct characteristics, such as low toxicity, tuneable photoluminescence, and good photostability. In the last few years, despite remarkable progress achieved in CD-based LEDs, their device performance is still inferior to that of well-developed organic, heavy-metal-based QDs, and perovskite LEDs. To better exploit LED applications and boost device performance, in this review, a comprehensive overview of currently explored CDs is presented, focusing on their key optical characteristics, which are closely related to the structural design of CDs from their carbon core to surface modifications, and to macroscopic structural engineering, including the embedding of CDs in the matrix or spatial arrangement of CDs. The design of CD-based LEDs for display and lighting applications based on the fluorescence, phosphorescence, and delayed fluorescence emission of CDs is also highlighted. Finally, it is concluded with a discussion regarding the key challenges and plausible prospects in this field. It is hoped that this review inspires more extensive research on CDs from a new perspective and promotes practical applications of CD-based LEDs in multiple directions of current and future research.
RESUMEN
Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.
