This study presents the development of a solar-driven thermally regenerative electrochemical cell (STREC) for continuous power generation. Key innovations include dual-function carbon-based electrodes for efficient solar absorption and electrochemical reactions, a transparent and ultrainsulating silica aerogel to maximize solar spectrum transmission while minimizing heat loss, and a compact heat exchanger to recover heat from hot cell streams. Under 1 sun conditions, the STREC achieves a power density of 912.1 mW/m, doubling the performance compared to a nonintegrated system. At higher solar concentrations (2 suns), the power density further increases to 1214.4 mW/m. Our findings indicate that this combination holds significant promise for efficient and continuous solar power generation. The application of state-of-the-art redox couples with high-temperature coefficients suggests a potential solar electricity efficiency of 12.6%, comparable to that of industrial photovoltaic technologies, providing a viable pathway for enhancing the renewable energy share in the global energy mix.
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http://dx.doi.org/10.1021/acsami.4c14299 | DOI Listing |
Nat Biomed Eng
January 2025
Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China.
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions.
View Article and Find Full Text PDFNat Comput Sci
January 2025
Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.
The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges.
View Article and Find Full Text PDFBiomed Microdevices
January 2025
Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
Wearable and implantable biosensors have rapidly entered the fields of health and biomedicine to diagnose diseases and physiological monitoring. The use of wired medical devices causes surgical complications, which can occur when wires break, become infected, generate electrical noise, and are incompatible with implantable applications. In contrast, wireless power transfer is ideal for biosensing applications since it does not necessitate direct connections between measurement tools and sensing systems, enabling remote use of the biosensors.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt.
The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by using Supervisory Control and Data Acquisition (SCADA) data of wind turbines, wherein conventional fault pattern identification is a time-consuming, guesswork process. This work proposed an intelligent automated approach to early fault detection through the implementation of the HARO (Huber Adam Regression Optimizer) model, which combines Transformer networks with Lasso Regression and the Adam optimizer.
View Article and Find Full Text PDFSci Rep
January 2025
School of Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
The use of winglet devices is an efficient technique for enhancing aerodynamic performance. This study investigates the effects of winglet cant angles on both the aerodynamics and aeroacoustics of a commercial wing, comparing them to other significant parameters using a parametric analysis. A Full Factorial Design method is employed to generate a matrix of experiments, facilitating a detailed exploration of flow physics, with lift-to-drag ratio (L/D) and the integral of Acoustic Power Level (APL) as the primary representatives of aerodynamic and acoustic performance, respectively.
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