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Unlocking the potential of hydrogen deuterium exchange via an iterative continuous-flow deuteration process
Self-control moderates the impacts of physical activity on the sleep quality of university students
The assembly factor Reh1 is released from the ribosome during its initial round of translation
Object identity representation occurs early in the archerfish visual system
Early tolerance and late persistence as alternative drug responses in cancer
Research on cutting mechanism and process optimization method of gear skiving
Abstract The cutting force and cutting temperature have a significant impact on the service life and durability of gear skiving cutters. Due to unreasonable design, the existing process parameters lead to dramatically nonuniform cutting force and cutting temperature, which aggravates the rapid wear of gear skiving cutters. To address this issue, this paper first establishes a finite element model of skiving the internal circular arc tooth in pin wheel housing, and the simulation model is simplified to improve computation efficiency. Next, the impact of single process parameter on cutting force and cutting temperature is analyzed by controlling variable. Then, an orthogonal experiment is designed and the method of range analysis is employed to evaluate the significance of each process parameter. Furthermore, a prediction model of cutting force and cutting temperature is established using a neural network optimized by genetic algorithm. This prediction model allows for the construction of a multi-objective optimization model for the process parameters. By solving this model, the optimal combination of process parameters within the given ranges can be obtained to achieve reasonable and balanced cutting force and cutting temperature.
MXene-Assisted NiFe sulfides for high-performance anion exchange membrane seawater electrolysis
Distinctive blood and salivary proteomics signatures in Qatari individuals at high risk for cardiovascular disease
Non-Markovian quantum exceptional points
Biosynthesis of a range of ZnO nanoparticles utilising Salvia hispanica L. seed extract and evaluation of their bioactivity
Cardiac repair using regenerating neonatal heart tissue-derived extracellular vesicles
Clinical impact of direct rotational atherectomy in patients with complex coronary artery lesions
Giant energy storage density with ultrahigh efficiency in multilayer ceramic capacitors via interlaminar strain engineering
Bauhinia coccinea extract prevents memory loss induced by scopolamine through activation of antiapoptotic and antioxidant pathways in mice
Optical widefield nuclear magnetic resonance microscopy
Abstract Microscopy enables detailed visualization and understanding of minute structures or processes. While cameras have significantly advanced optical, infrared, and electron microscopy, imaging nuclear magnetic resonance (NMR) signals on a camera has remained elusive. Here, we employ nitrogen-vacancy centers in diamond as a quantum sensor, which converts NMR signals into optical signals that are subsequently captured by a high-speed camera. Unlike traditional magnetic resonance imaging, our method records the NMR signal over a wide field of view in real space. We demonstrate that our optical widefield NMR microscopy can image NMR signals in microfluidic structures with a ~10 μm resolution across a ~235 × 150 μm2 area. Crucially, each camera pixel records an NMR spectrum providing multicomponent information about the signal’s amplitude, phase, local magnetic field strengths, and gradients. The fusion of optical microscopy and NMR techniques enables multifaceted imaging applications in the physical and life sciences.