Cian F. Twomey, Gabriele Biagi, Albert A. Ruth
et al.
We report an all-fiber laser gas analyzer (LGA) based on quartz-enhanced photoacoustic spectroscopy (QEPAS) that exploits strong evanescent wave (EW) enhancement using a dielectric coating on side-polished fiber. The dielectric coating increases the fraction of the evanescent field in air, significantly amplifying light–gas interaction within the polished region. A single-mode fiber with a 17 mm polished section passes through two millimeter-scale resonator tubes and a custom quartz tuning fork (QTF) with 0.8 mm prong spacing. The optimized EW coupling efficiently generates photoacoustic waves that excite the QTF’s fundamental flexural mode. Methane–nitrogen mixtures at 800 mbar were used to evaluate performance, achieving a detection limit of 2.5 ppmv for CH4 with 300 ms integration time. By enhancing the evanescent interaction within a compact, robust fiber geometry, this EW-QEPAS sensor eliminates free-space optics and offers a miniaturized, field-deployable solution for gas detection in industrial and agricultural environments.
Abhijeet Upadhya, Anu Goel, Vivek Kumar Dwivedi
et al.
The research work invokes the long short-term memory (LSTM) deep learning model for combating the issue of outdated channel state information (CSI) during channel estimation on the wireless medium. For demonstration of the concept, the downlink free space optical (FSO)/radio frequency (RF) relaying strategy with outdated CSI has been contemplated. In the considered amplify-and-forward (AF) cooperative relaying, the channel gain has been extracted based on the available outdated CSI at the relay node. Of course, the performance of the FSO/RF downlink system is inferior due to low correlation between the previous (original) channel state and the measured CSI during the next time interval. Since the LSTM can use larger input data sequentially to predict the next target probabilities based on correlation among input variables, they become most suited for CSI estimation from the available outdated CSI. The trained LSTM model becomes accomplished to estimate the previous state, thus serving the relay node to adjust the gain more accurately. The trained LSTM model in the present research work is highly accurate with mean square error (MSE) and root mean square error (RMSE) of <inline-formula><tex-math notation="LaTeX">$MSE=-43.49$</tex-math></inline-formula> dB and <inline-formula><tex-math notation="LaTeX">$RMSE=-13.42$</tex-math></inline-formula> dB, respectively. The performance of the downlink FSO/RF relay has been presented in terms of outage probability, ergodic capacity and bit error rate (BER). It has been shown in the paper that using the trained deep learning LSTM model, the performance of the relaying system can be made equivalent to that when timing delay exists between the original and the estimated sample values.
Abstract The quest for mechanoluminescence (ML) in zinc sulfide (ZnS) spans more than a century, initially sparked by observations of natural minerals. There has been a resurgence in research into ML materials in recent decades, driven by advances in optoelectronic technologies and a deeper understanding of their luminescent properties under mechanical stress. ZnS, in particular, has garnered attention owing to its remarkable ability to sustain luminescence after more than 100,000 mechanical stimulations, positioning it as a standout candidate for optoelectronic applications. In contrast to conventional photoluminescent and electroluminescent light sources, ZnS composite elastomers have emerged as flexible, stretchable self‐powered light sources with considerable practical implications. This review introduces the development history, ML mechanisms, prototype ML devices, ZnS‐based ML material preparation methods, and their diverse applications spanning environmental mechanical‐to‐optical energy conversion, E‐signatures, anti‐counterfeiting, wearable information sensing devices, advanced battery‐free displays, biomedical imaging, and optical fiber sensors for human–computer interactions, among others. By integrating insights from ML‐optics, mechanics, and flexible optoelectronics, and by summarizing pertinent perspectives on current scientific challenges, application technology hurdles, and potential solutions for emerging scientific frontiers, this review aims to furnish fundamental guidance and conceptual frameworks for the design, advancement, and cutting‐edge application of novel mechanoluminescent materials.
Materials of engineering and construction. Mechanics of materials, Biotechnology
Bound states in the continuum (BICs) have exhibited extraordinary properties in photonics for enhanced light-matter interactions that enable appealing applications in nonlinear optics, biosensors, and ultrafast optical switches. The most common strategy to apply BICs in a metasurface is by breaking symmetry of resonators in the uniform array that leaks the otherwise uncoupled mode to free space and exhibits an inverse quadratic relationship between quality factor (Q) and asymmetry. Here, we propose a scheme to further reduce scattering losses and improve the robustness of symmetry-protected BICs by decreasing the radiation density with a hybrid BIC lattice. We observe a significant increase of radiative Q in the hybrid lattice compared to the uniform lattice with a factor larger than 14.6. In the hybrid BIC lattice, modes are transferred to Г point inherited from high symmetric X, Y, and M points in the Brillouin zone that reveal as multiple Fano resonances in the far field and would find applications in hyperspectral sensing. This work initiates a novel and generalized path toward reducing scattering losses and improving the robustness of BICs in terms of lattice engineering that would release the rigid requirements of fabrication accuracy and benefit applications of photonics and optoelectronic devices.
This work presents and evaluates different approaches of integrated optical sensors based on photonic integrated circuit (PIC) technologies for refractive index sensing. Bottlenecks in the fabrication flow towards an applicable system are discussed that hinder a cost-effective mass-production for disposable sensor chips. As sensor device, a waveguide coupled micro-ring based approach is chosen which is manufactured in an 8” wafer level process. We will show that the co-integration with a reproducible, scalable and low-cost microfluidic interface is the main challenge which needs to be overcome for future application of silicon technology based PIC sensor chips.
Photovoltaic (PV)‐assisted photoelectrochemical (PEC) tandem cells with elevated hydrogen (H2) production rates are a practical approach for carbon‐dioxide‐free, green H2 production. A semitransparent single‐cell‐based wide‐bandgap perovskite solar cell (PSC) coupled with an Si photocathode provides sufficient potential for H2 generation when combined with a sulfate oxidation reaction. While energetically favorable ZnO as an electron transport layer (ETL) increases the V OC to 1.19 V for mixed‐halide perovskite, phase decomposition is induced when Br ions contacted the ZnO ETL. The SnO2 interlayer shows improved passivation, superior operational stability, and excellent performance among the various atomic layer deposited metal oxides tested. Furthermore, the resulting semitransparent PSC demonstrates reproducibility of its enhanced PV parameters (i.e., V OC 1.17 ± 0.01 V, FF = 76.78 ± 1.39%, and PCE = 11.95 ± 1.13%) due to better interface quality. The precise calculation of light absorption from both PV and Si for the overall tandem device leads to optimized light harvesting in the top and bottom electrodes, maximizing H2 production. Overall, the PV‐PEC device incorporated with a chemically stable semitransparent top PSC and bottom Si photocathode allows to accomplish stable H2 production at 11.1 mA cm−2 under unbiased conditions.
Acoustic-resolution photoacoustic microscopy (AR-PAM) image resolution is determined by the point spread function (PSF) of the imaging system. Previous algorithms, including Richardson–Lucy (R–L) deconvolution and model-based (MB) deconvolution, improve spatial resolution by taking advantage of the PSF as prior knowledge. However, these methods encounter the problems of inaccurate deconvolution, meaning the deconvolved feature size and the original one are not consistent (e.g., the former can be smaller than the latter). We present a novel deep convolution neural network (CNN)-based algorithm featuring high-fidelity recovery of multiscale feature size to improve lateral resolution of AR-PAM. The CNN is trained with simulated image pairs of line patterns, which is to mimic blood vessels. To investigate the suitable CNN model structure and elaborate on the effectiveness of CNN methods compared with non-learning methods, we select five different CNN models, while R–L and directional MB methods are also applied for comparison. Besides simulated data, experimental data including tungsten wires, leaf veins, and in vivo blood vessels are also evaluated. A custom-defined metric of relative size error (RSE) is used to quantify the multiscale feature recovery ability of different methods. Compared to other methods, enhanced deep super resolution (EDSR) network and residual in residual dense block network (RRDBNet) model show better recovery in terms of RSE for tungsten wires with diameters ranging from 30 μmto 120 μm. Moreover, AR-PAM images of leaf veins are tested to demonstrate the effectiveness of the optimized CNN methods (by EDSR and RRDBNet) for complex patterns. Finally, in vivo images of mouse ear blood vessels and rat ear blood vessels are acquired and then deconvolved, and the results show that the proposed CNN method (notably RRDBNet) enables accurate deconvolution of multiscale feature size and thus good fidelity.
Joana Costa Martins, Ana R. N. Bastos, Rute A. S. Ferreira
et al.
The potential applications in disparate fields led to a rapid evolution of luminescence thermometry. In particular, luminescent thermometry based on trivalent lanthanide ions (Ln3+) has become very popular in the past decade due to the unique versatility, stability, and narrow emission band profiles covering a broad spectral range (from ultraviolet to the infrared) with relatively high emission quantum yields. Nevertheless, the reliability of Ln3+ ratiometric nanothermometry measurements is recently questioned in a few works reporting fake temperature readouts caused by experimental artifacts and even intrinsic effects. Using NaYF4:Er3+/Yb3+@NaNdF4@PAA (PAA stands for polyacrylic acid) core–shell nanoparticles, it is shown that how the primary luminescent thermometer concept can be used to correct the thermometric parameter (the intensity ratio of the Er3+ 2H11/2 → 4I15/2 and 4S3/2 → 4I15/2 transitions) from the interference of the intruding 2H9/2 → 4I13/2 emission ensuring, thereafter, reliable temperature measurements.
Multispectral diode laser sources are extensively used for a variety of applications involving the identification of small objects based on their spectral signature. Although the power scaling of single emitters is severely limited, they are easily stackable as diode laser bars and stacks, allowing the combination of wavelengths and power levels required for each application. However, a critical drawback given by this topology is the asymmetry between the fast and the slow axes in the beam profile, leading to poor beam quality and possibly poor fiber coupling efficiency. In this regard, a suitable beam shaping is required to maximize the power coupling in the smallest possible fiber core. In this work, we propose an innovative beam shaping method for the homogenization of the beam quality of six 8-bar diode laser stacks at wavelengths from 790 nm to 980 nm. We performed realistic simulations to examine the shaping method with, when possible, commercially available components. Fast-axis collimating (FAC) lenses and beam twisters are designed in Zemax to remodel the far-field beam emitted by each bar. The beam of each diode laser stack is halved in the vertical axis using polarization beam combiners, and then three quartz-plate stacks combine and rearrange the beams coming from each diode laser stack pair in the horizontal axis to eliminate the lightless regions. A single multispectral beam is then obtained by using reflective and dichroic mirrors and effectively coupled into an optical fiber with a core diameter of 1 mm and a numerical aperture (N.A.) of 0.5 using a doublet of cylindrical lenses. A maximum power density of ∼ 0.73 MW/cm2 is calculated at the output of the fiber with a fiber coupling of 89 %. A number of applications can benefit from the proposed topology, in particular biomedical applications using fiber probes are identified as potential candidates for the implementation of the proposed system.
Malek Stephanie C., Overvig Adam C., Shrestha Sajan
et al.
Actively tunable and reconfigurable wavefront shaping by optical metasurfaces poses a significant technical challenge often requiring unconventional materials engineering and nanofabrication. Most wavefront-shaping metasurfaces can be considered “local” in that their operation depends on the responses of individual meta-units. In contrast, “nonlocal” metasurfaces function based on the modes supported by many adjacent meta-units, resulting in sharp spectral features but typically no spatial control of the outgoing wavefront. Recently, nonlocal metasurfaces based on quasi-bound states in the continuum have been shown to produce designer wavefronts only across the narrow bandwidth of the supported Fano resonance. Here, we leverage the enhanced light-matter interactions associated with sharp Fano resonances to explore the active modulation of optical spectra and wavefronts by refractive-index tuning and mechanical stretching. We experimentally demonstrate proof-of-principle thermo-optically tuned nonlocal metasurfaces made of silicon and numerically demonstrate nonlocal metasurfaces that thermo-optically switch between distinct wavefront shapes. This meta-optics platform for thermally reconfigurable wavefront shaping requires neither unusual materials and fabrication nor active control of individual meta-units.
When two or more degrees of freedom become coupled in a physical system, a number of observables of the latter cannot be represented by mathematical expressions separable with respect to the different degrees of freedom. In recent years it appeared clear that these expressions may display the same mathematical structures exhibited by multiparty entangled states in quantum mechanics. In this work, we investigate the occurrence of such structures in optical beams, a phenomenon that is often referred to as ‘classical entanglement’. We present a unified theory for different kinds of light beams exhibiting classical entanglement and we indicate several possible extensions of the concept. Our results clarify and shed new light upon the physics underlying this intriguing aspect of classical optics.
Generalization of Fractional Schrödinger equation (FSE) into optics is fundamentally important, since optics usually provides a fertile ground where FSE-related phenomena can be effectively observed. Beam propagation management is a topic of considerable interest in the field of optics. Here, we put forward a simple scheme for the realization of propagation management of light beams by introducing a double-barrier potential into the FSE. Transmission, partial transmission/reflection, and total reflection of light fields can be controlled by varying the potential depth. Oblique input beams with arbitrary distributions obey the same propagation dynamics. Some unique properties, including strong self-healing ability, high capacity of resisting disturbance, beam reshaping, and Goos-Hänchen-like shift are revealed. Theoretical analysis results are qualitatively in agreements with the numerical findings. This work opens up new possibilities for beam management and can be generalized into other fields involving fractional effects.