We demonstrate a deep-learning neural network (DNN) method for the measurement of molecular alignment by using the molecular-alignment-based cross-correlation polarization-gating frequency resolved optical gating (M-XFROG) technique. Our network has the capacity for direct measurement of molecular alignment from the FROG traces. In a proof-of-principle experiment, we have demonstrated our method in O2 molecules. With our method, the molecular alignment factor ⟨cos2 θ⟩(t) of O2, impulsively excited by a pump pulse, was directly reconstructed. The accuracy and validity of the reconstruction have been verified by comparison with the simulations based on experimental parameters.
Indoor organic and perovskite photovoltaics (PVs) have been attracting great interest in recent years. The theoretical limit of indoor PVs has been calculated based on the detailed balance method developed by Shockley–Queisser. However, realistic losses of the organic and perovskite PVs under indoor illumination are to be understood for further efficiency improvement. In this work, the efficiency limit of indoor PVs is calculated to 55.33% under indoor illumination (2700 K, 1000 lux) when the bandgap (Eg) of the semiconductor is 1.77 eV. The efficiency limit was obtained on the basis of assuming 100% photovoltaic external quantum efficiency (EQEPV) when E ≥ Eg, there was no nonradiative recombination, and there were no resistance losses. In reality, the maximum EQEPV reported in the literature is 0.80–0.90. The proportion of radiative recombination in realistic devices is only 10-5–10-2, which causes the open-circuit voltage loss (ΔVloss) of 0.12–0.3 V. The fill factor (FF) of the indoor PVs is sensitive to the shunt resistance (Rsh). The realistic losses of EQEPV, nonradiative recombination, and resistance cause the large efficiency gap between the realistic values (excellent perovskite indoor PV, 32.4%; superior organic indoor PV, 30.2%) and the theoretical limit of 55.33%. In reality, it is feasible to reach the efficiency of 47.4% at 1.77 eV for organic and perovskite photovoltaics under indoor light (1000 lux, 2700 K) with VOC = 1.299 V, JSC = 125.33 µA/cm2, and FF = 0.903 when EQEPV = 0.9, EQEEL = 10-1, Rs = 0.5 Ω cm2, and Rsh = 104 kΩ cm2.
Large-bandwidth, high-sensitivity, and large dynamic range electric field sensors are gradually replacing their traditional counterparts. The lithium-niobate-on-insulator (LNOI) material has emerged as an ideal platform for developing such devices, owing to its low optical loss, high electro-optical modulation efficiency, and significant bandwidth potential. In this paper, we propose and demonstrate an electric field sensor based on LNOI. The sensor consists of an asymmetric Mach–Zehnder interferometer (MZI) and a tapered dipole antenna array. The measured fiber-to-fiber loss is less than -6.7 dB, while the MZI structure exhibits an extinction ratio of greater than 20 dB. Moreover, 64-QAM signals at 2 GHz were measured, showing an error vector magnitude (EVM) of less than 8%.
The ongoing quest for higher data storage density has led to a plethora of innovations in the field of optical data storage. This review paper provides a comprehensive overview of recent advancements in next-generation optical data storage, offering insights into various technological roadmaps. We pay particular attention to multidimensional and superresolution approaches, each of which uniquely addresses the challenge of dense storage. The multidimensional approach exploits multiple parameters of light, allowing for the storage of multiple bits of information within a single voxel while still adhering to diffraction limitation. Alternatively, superresolution approaches leverage the photoexcitation and photoinhibition properties of materials to create diffraction-unlimited data voxels. We conclude by summarizing the immense opportunities these approaches present, while also outlining the formidable challenges they face in the transition to industrial applications.
Ischemic stroke causes long-term disability and results in motor impairments. Such impairments are associated with structural changes in the neuromuscular junction (NMJ), including detailed morphology and three-dimensional (3D) distribution. However, previous studies only explored morphological changes of individual NMJs after stroke, which limits the understanding of their role in post-stroke motor impairment. Here, we examine 3D distributions and detailed morphology of NMJs in entire mouse muscles after unilateral and bilateral strokes induced by photothrombosis. The results show that 3D distributions and numbers of NMJs do not change after stroke, and severe unilateral stroke causes similar levels of NMJ fragmentation and area enlargement to bilateral stroke. This research provides structural data, deepening the understanding of neuromuscular pathophysiology after stroke.