Articles
Publication Date: 2025
Optics Continuum (27700208)4(8)pp. 1676-1686
The Hα emission line at 656.3 nm is a crucial spectral wavelength for studying astrophysical and solar phenomena, such as ionized hydrogen regions and chromospheres’ activity. To facilitate precise and detailed observations at this wavelength, we designed four high-performance Hα filters. These filters exhibit a full width at half maximum of approximately 1 nm or less and achieve peak transmittance exceeding 95%. Utilizing multiple beam interference based on the Fabry-Perot principle, with niobium pentoxide (Nb2 O5) and magnesium fluoride (MgF2) layers, the design ensures exceptional spectral isolation within the 625–740 nm range. This outstanding performance makes the filters highly suitable for advanced astrophysical research and solar imaging. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
Publication Date: 2023
Optics Continuum (27700208)2(3)pp. 632-645
The turbulent atmosphere usually degrades the quality of images taken on Earth. Random variations of the refractive index of light cause distortion of wavefronts propagating to ground-based telescopes. Compensating these distortions is usually accomplished by adaptive optics (AO) approaches. The control unit of AO adjusts the phase corrector, such as deformable mirrors, based on the incoming turbulent wavefront. This can be done by different algorithms. Usually, these algorithms encounter real-time wavefront compensation challenges. Although many studies have been conducted to overcome these issues, we have proposed a method, based on the convolutional neural network (CNN) as a branch of deep learning (DL) for sensor-less AO. To this objective, thousands of wavefronts, their Zernike coefficients, and corresponding intensity patterns in diverse conditions of turbulence are generated and fed into the CNN to predict the wavefront of new intensity patterns. The predictions are done for considering the different number of Zernike terms, and the optimum number is achieved by comparing wavefront errors. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
Publication Date: 2022
Advanced Theory and Simulations (25130390)5(2)
Computational screening is highly fruitful for initial studies of unknown molecules due to the capability of this tool in molecular discovery and the continuous improvement of computational methods. This article reports how computational studies on more than 300 organic molecules can identify thermally activated delayed fluorescent (TADF) emitters that emit light within the range of red up to near-infrared (NIR) with high TADF rates. According to time-dependent density functional theory (TD-DFT) computations, the best compounds exhibit a low singlet-triplet energy gap of 0.03 eV and oscillator strength of 0.0425, leading to a high rate of delayed fluorescence decay of 1.4483 μs−1. Additionally, an experimental-theory calibration approach is used as an adjunct to experimental research in the future, and emission wavelengths in promising charge-transfer compounds are estimated as large as 689 nm. © 2021 Wiley-VCH GmbH
Publication Date: 2022
Optics Continuum (27700208)1(11)pp. 2347-2359
In the presence of high-strength turbulence, it is difficult to recognize close stars in ground-based imaging systems. Although adaptive optics could be helpful to reconstruct such images, there are always some remaining uncorrected phases for different turbulence conditions that could affect the recognition of close stars. Considering this, we have introduced a classification-based method by using a deep learning network to distinguish such star systems without correcting the wavefronts. To this aim, we have configured a Convolutional Neural Network (CNN). Five turbulence models are used to generate a dataset that includes thousands of images. Moreover, four metrics have been utilized to evaluate the CNN after the learning process. The accuracy of the network was upper than 80% for all of the turbulence models. The comparison of the five turbulence models is presented in detail, based on these metrics, and the robustness of the deep learning network is reported. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
The main idea of this work is to design a notch filter structure with a narrow notch width and maximum reflection while reducing fabrication challenges. In addition, using anti-reflection layers in the outermost part of the designed structure, the pass-band ripples are reduced. In this study, we considered [(nHnL)s (mHmL)p (nHnL)z] structure with n=5 and m=3. Using this form of design and combining 3 and 5 quarter-wave coefficients instead of 1 and 3, we could reach a narrower NW in fewer periods of HL layers. The stability of the deposition conditions and the density of the layers affect their quality and consequently the result of environmental tests. Hence, to construct the designed structure, we employed the sputtering method with RF and DC sources. In our experiments, we showed that the use of a simple shield prevents the oxidation of targets’ surfaces as well as reduces the deposition rate and increases the stability of deposition processes. Fabricated Samples have been subjected to a variety of environmental tests, including humidity, hard and soft abrasion, temperature, and adhesion tests with satisfactory results. © 2021
