分类: 光学 >> 量子光学 提交时间: 2023-02-24
摘要: Photoacoustic spectroscopy (PAS) based gas sensors with high sensitivity, wide dynamic range, low cost, and small footprint are desirable across a broad range of applications in energy, environment, safety, and public health. However, most works have focused on either acoustic resonator to enhance acoustic wave or optical resonator to enhance optical wave. Herein, we develop a gas sensor based on doubly resonant PAS in which the acoustic and optical waves are simultaneously enhanced using combined optical and acoustic resonators in a centimeter-long configuration. Not only the lower detection limit is enhanced by the double standing waves, but also the upper detection limit is expanded due to the short resonators. As an example, we developed a sensor by detecting acetylene (C2H2), achieving a noise equivalent absorption of 5.7*10-13 cm-1 and a dynamic range of eight orders. Compared to the state-of-the-art PAS gas sensors, the developed sensor increases the sensitivity by two orders of magnitude and extends the dynamic range by three orders of magnitude. Besides, a laser-cavity-molecule locking strategy is proposed to provide additional flexibility of fast gas detection.
分类: 光学 >> 量子光学 提交时间: 2023-02-19
摘要: Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever-increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural networks with faster data processing and lower energy usage compared to digital computers. Photonics technology is naturally capable of implementing complex-valued neural networks at no additional hardware cost. Here, we demonstrate the capability of photonic neural networks for predicting the quantum mechanical properties of molecules. To the best of our knowledge, this work is the first to harness photonic technology for machine learning applications in computational chemistry and molecular sciences, such as drug discovery and materials design. We further show that multiple properties can be learned simultaneously in a photonic chip via a multi-task regression learning algorithm, which is also the first of its kind as well, as most previous works focus on implementing a network in the classification task.