Helicobacter pylori infection is closely associated with chronic gastritis, peptic ulcers, and gastric cancer, rendering rapid and noninvasive diagnostic technologies clinically essential. Current breath tests, such as the 13C-urea breath test (13C-UBT), typically rely on breath collection bags followed by offline analysis, which limits real-time monitoring capabilities. To overcome this constraint, we presented a mid-infrared photoacoustic spectroscopy (MIR-PAS) system for real-time detection of CO2 isotopes and evaluation of 13C-UBT responses. A dual-channel differential resonant photoacoustic cell (DPAC) with a minimal sample volume of 10.3 mL was designed to enhance acoustic signal collection, achieving a resonance frequency of 3775.7 Hz and a Q-factor of 27. Target absorption lines of 12CO2 (2299.64 cm-¹) and 13CO2 (2299.80 cm-¹) were selected within the strong ν3 band to ensure high-resolution isotopic discrimination using a 4.35 μm quantum cascade laser. The sensor demonstrated excellent linear response (R2 > 0.994) across 500–2500 ppm and achieved detection limits of 8.98 ppb for 12CO2 and 2.81 ppb for 13CO2 with the optimal averaging. δ13C measurements exhibited a precision of 0.066 ‰ at 76 s averaging time. Breath-sampling tests further revealed distinct temporal release patterns of CO2 isotopes during exhalation. These results confirmed that the developed MIR-PAS system provides a compact, sensitive, and robust platform for isotopic CO2 analysis and demonstrates strong potential for point-of-care H. pylori diagnostics.
Maxime Lavaud, Yosef Shokeeb, Juliette Lacherez
et al.
Characterizing anomalous diffusion (AnDi) is crucial in order to understand the evolution of complex stochastic systems, from molecular interactions to cellular dynamics. Here, we evaluate the performances regarding such a task of Bi-Mamba, a novel state-space deep-learning architecture articulated around a bidirectional scanning mechanism. Our implementation is tested on the AnDi-2 challenge datasets. Designed for regression tasks, the Bi-Mamba architecture infers efficiently the effective diffusion coefficient and anomalous exponent from single, short trajectories. As such, our results indicate the potential practical use of the Bi-Mamba architecture towards the characterization of AnDi.
Md. Towhiduzzaman, Md. Abdul Al Mohit, A. Z. M Asaduzzaman
The (3 + 1)-dimensional Zakharov-Kuznetsov-Burgers (ZKB) equation plays a pivotal role in modeling nonlinear wave propagation and dissipation phenomena in plasma dynamics. In this study, we develop a Physics-Informed Neural Network (PINN) framework tailored to accurately solve the ZKB equation with appropriate initial and boundary conditions. The proposed method successfully reconstructs lump and multi-soliton wave structures, exhibiting excellent agreement with analytical benchmarks. High-resolution 2D, 3D surface, and contour plots illustrate the dynamic evolution of nonlinear waves, while error heatmaps and training loss curves validate the model’s accuracy and stability. The PINN framework is implemented on [specify hardware, e.g., Intel i9 CPU, NVIDIA RTX GPU, 64 GB RAM], achieving efficient training times for high-dimensional computations. Moreover, potential applications in spatiotemporal structured light and coupled nonlinear systems are discussed, highlighting the broader significance of this approach in modern plasma physics and optics. This work emphasizes the robustness, efficiency, and versatility of PINNs in handling high-dimensional nonlinear partial differential equations, offering a promising computational alternative for future research in plasma physics, soliton theory, and applied mathematics.
Feng-Chun Hsu, Chun-Yu Lin, Chia-Yuan Chang
et al.
The beam profile and wavefront characteristics of laser beams are essential for numerous laser applications, including micromachining and microfabrication. However, conventional wavefront sensors, such as the Shack-Hartmann wavefront sensor (SHWS), are limited by reduced accuracy in detecting local distortions and sensitivity to non-uniform beam profiles. Additionally, beam profile information is crucial for such applications. This paper introduces a new methodology that utilizes an SHWS-like structure to overcome these limitations. By employing a physical constraint learning approach, the proposed method simultaneously provides highly accurate wavefront and beam profile data. We first develop a pretrained network using microlens array (MLA) simulation datasets. To implement a practical MLA-based measurement system, this pretrained network is further fine-tuned with datasets modulated by a spatial light modulator in the system setup. Experimental results demonstrate that the proposed network can reconstruct both beam profiles and wavefronts in real-time. Compared to traditional SHWS reconstruction techniques, our approach enhances computation speed by over 100 times, while also providing beam intensity profile information and increasing wavefront sensing accuracy by approximately fivefold.
<bold>W</bold>e propose the graphene film with trapezoid-shaped nanoparticles (GTNAs) to transport particles. In our design, the conversion of plasmon surface resonances can be realized without changing the excitation light source. By sequentially activating three closely packed potential wells, nanoparticles can be transported between adjacent traps in a creeping manner. Three adjacent potential wells form a linearly repeating array structure, forming a nano-optical conveyor belt. When the resonant wavelength is 5.5 μm, and the power density is 0.4 mW/μm<sup>2</sup>, we verified that the target particle can move along the direction of the hot spots. In addition, the movement of nanoparticles in a liquid environment will be interfered with by viscous resistance and the random Brownian motion process. Since particles produce hysteresis or derailment during transmission, we also analyzed the time interval of switching the Fermi level to manipulate the particle in real-time. The three-dimensional finite-difference time-domain method has been used to verify that the design of this paper provides a conveyor belt in tunable graphene without rotating the polarization angle of the light source and has broad application prospects in biomedical diagnostics.
AbstractTransparent objects are invisible to traditional cameras because they can only detect intensity fluctuations, necessitating the need for interferometry followed by computationally intensive digital image processing. Now it is shown that the necessary transformations can be performed optically by combining machine learning and diffractive optics, for a direct in-situ measurement of transparent objects with conventional cameras.
Optical neural networks present distinct advantages over traditional electrical counterparts, such as accelerated data processing and reduced energy consumption. While coherent light is conventionally employed in optical neural networks, our study proposes harnessing spatially incoherent light in all-optical Fourier neural networks. Contrary to numerical predictions of declining target recognition accuracy with increased incoherence, our experimental results demonstrate a surprising outcome: improved accuracy with incoherent light. We attribute this unexpected enhancement to spatially incoherent light's ability to alleviate experimental errors like diffraction rings, laser speckle, and edge effects. Our controlled experiments introduced spatial incoherence by passing monochromatic light through a spatial light modulator featuring a dynamically changing random phase array. These findings underscore partially coherent light's potential to optimize optical neural networks, delivering dependable and efficient solutions for applications demanding consistent accuracy and robustness across diverse conditions.
We introduce electrically-poled small molecule assemblies that can serve as the active electro-optic material in nano-scale guided-wave circuits such as those of the silicon photonics platform. These monolithic organic materials can be vacuum-deposited to homogeneously fill nanometer-size integrated-optics structures, and electrically poled at higher temperatures to impart an orientational non-centrosymmetric order that remains stable at room temperature. An initial demonstration using the DDMEBT molecule and corona poling delivered a material with the required high optical quality, an effective glass transition temperature of the order of $\sim 80$ $^\circ$C, and an electro-optic coefficient of $20$~pm/V.
Acoustic properties of buried graphitized layers in diamond formed by ion implantation followed by annealing were studied using the picosecond ultrasonic technique with spatial resolution. Two methods of elastic pulse generation were used: heating an aluminum film deposited on a diamond sample by femtosecond laser pulses and direct illumination of the graphitized layers by these pulses. We applied a multilayered model of the acousto-optical response to fit experimental results and estimate the distribution of the acoustical parameters (wave resistance, viscoelastic damping, and longitudinal sound speed) of the structures under study in depth. It was found that unique sets of spectral lines are present in the Fourier spectra of measured responses in regions with different internal structures. Mapping of the Fourier spectra made it possible to visualize regions with different internal structures. The combined use of depth profiling and mapping can serve as a tool for hypersound tomography.
Qianyi Zhang, Antoine Boniface, Virendra K. Parashar
et al.
Light-based additive manufacturing holds great potential in the field of bioprinting due to its exceptional spatial resolution, enabling the reconstruction of intricate tissue structures. However, printing through biological tissues is severely limited due to the strong optical scattering within the tissues. The propagation of light is scrambled to form random speckle patterns, making it impossible to print features at the diffraction-limited size with conventional printing approaches. The poor tissue penetration depth of ultra-violet or blue light, which is commonly used to trigger photopolymerization, further limits the fabrication of high cell-density tissue constructs. Recently, several strategies based on wavefront shaping have been developed to manipulate the light and refocus it inside scattering media to a diffraction-limited spot. In this study, we present a high-resolution additive manufacturing technique using upconversion nanoparticles and a wavefront shaping method that does not require measurement from an invasive detector, i.e., it is a non-invasive technique. Upconversion nanoparticles convert near-infrared light to ultraviolet and visible light. The ultraviolet light serves as a light source for photopolymerization and the visible light as a guide star for digital light shaping. The incident light pattern is manipulated using the feedback information of the guide star to focus light through the tissue. In this way, we experimentally demonstrate that near-infrared light can be non-invasively focused through a strongly scattering medium. By exploiting the optical memory effect, we further demonstrate micro-meter resolution additive manufacturing through highly scattering media such as a 300-μm-thick chicken breast. This study provides a proof of concept of high-resolution additive manufacturing through turbid media with potential application in tissue engineering.
Alla Georgievna Polyakova, Anna Gennadievna Soloveva, Petr Vladimirovich Peretyagin
et al.
Burns are an actual problem of modern medicine. Oxidative stress, microcirculation, and hemostasis disorders are important links in the pathogenesis of burn disease. It is shown that these processes are significantly influenced by the point effect of low-intensity (LI) electromagnetic radiation (EMR) of the millimeter (MM) and submillimeter (subMM) ranges. However, the final opinion on the advantages of a particular range has not been formed. We have given a comparative assessment of the results of the effects of various frequency-energy parameters of microwaves on the indicators of adaptive reactions in rats under experimental thermal trauma and viscoelastic properties of blood in the case of burn disease.
Using a high throughput Photoreflectance (PR) spectroscopy setup, a significant temporal behavior of the PR spectroscopy from a wide range of silicon (Si) wafers is observed, induced by optical heating effect. The PR intensity and the built-in electric field enhance on a time scale of several minutes. A diffusion process-based time-dependent model is developed and validated on shallow Arsenic (As+) ion implanted Si layers. A theoretical form of the temporal built-in electric field is determined and used to fit the experimental PR intensity. Modeling the PR temporal effect enables the real time monitoring of As+ diffusion and the measurement of the diffusivity as a function of low implant energy (E: 3 – 7 keV) from which the lattice temperature is derived experimentally. Furthermore, the As+ Shockley-Read-Hall (SRH) carrier lifetime at 300 K is found to be strongly dependent over the same experimental E range. Using the SRH theory, defects formed by As+ implant are studied and related trap density (Ns) are measured versus E assuming unchanged As+ capture cross section.
Mateusz P. Mrozowski, Jonathan D. Pritchard, J. Jeffers
We present a high efficiency source of picosecond pulses derived from a dual cavity optical frequency comb generator. This approach overcomes the limitations of single cavity comb generators that are restricted to efficiencies of a few percent. We achieve picosecond pulses with GHz repetition rates offering over a hundred times higher output efficiency than a single cavity design and demonstrate tuning of pulse width by varying the modulation depth of the intra-cavity electro-optic modulator. These results provide a wavelength-agnostic design with a compact footprint for the development of portable picosecond pulsed laser systems for timing, metrology, and LIDAR applications.
Temperature measurement: optical barcodes from whispering-gallery sensors Extremely precise measurement of temperature using devices known as whispering-gallery mode (WGM) sensors can be greatly improved using a technique that simultaneously monitors different modes, or patterns, of the optical signals. WGM sensors rely on the sustained circulation of light within closed concave microstructures—discs, rings, or spheres—similar to the movement of sound waves in whispering galleries such as the dome of St. Paul’s Cathedral in London. Jie Liao and Lan Yang at Washington University in St. Louis, Missouri, USA, developed procedures to analyse the effect of temperature on the collective patterns of light signals in WGM sensors. The results are converted into optical barcodes which indicate the temperature directly. This multimode system overcomes significant limitations imposed by the restricted range and less direct monitoring methods of existing single-mode sensors.
In this paper, we propose a super-resolution (pixel grid refinement) method for digital images. It is based on the linear filtering of a zero-padded discrete signal. We introduce a continuous-discrete observation model to create a reconstruction system. The proposed observation model is typical of real-world imaging systems - an initially continuous signal first undergoes linear (dynamic) distortions and then is subjected to sampling and the effect of additive noise. The proposed method is optimal in the sense of mean square recovery error minimization. In the theoretical part of the article, a general scheme of the linear super-resolution of the signal is presented and expressions for the impulse and frequency responses of the optimal reconstruction system are derived. An expression for the error of such restoration is also derived. For the sake of brevity, the entire description is presented for one-dimensional signals, but the obtained results can easily be generalized for the case of two-dimensional images. The experimental section of the paper is devoted to the analysis of the super-resolution reconstruction error depending on the parameters of the observation model. The significant superiority of the proposed method in terms of the reconstruction error is demonstrated in comparison with linear interpolation, which is usually used to refine the grid of image pixels.
On the basis of ghost imaging, this paper proposes a multiple-image encryption scheme combining public key cryptography and Hadamard basis pattern. In the encryption system, a plurality of light paths are set, and the Hadamard basis patterns are used for illumination, so that each light path is concentrated in a bucket detector to obtain intensity values of all images. Then, all the detected intensity values are encrypted by using public key cryptography algorithm to obtain the final ciphertext. The use of basis patterns allows for high-quality reconstruction. In addition, randomness in the permutation operation and public key cryptography provide a strong security feature for the encryption system. The multiple-image encryption scheme solves the crosstalk problem among images. The basic principle of the encryption scheme is theoretically analyzed, and the feasibility and security of the proposed method are verified by numerical simulation.
Functional near-infrared spectroscopy (fNIRS), a growing neuroimaging modality, has been utilized over the past few decades to understand the neuronal behavior in the brain. The technique has been used to assess the brain hemodynamics of impaired cohorts as well as able-bodied. Neuroimaging is a critical technique for patients with impaired cognitive or motor behaviors. The portable nature of the fNIRS system is suitable for frequent monitoring of the patients who exhibit impaired brain activity. This study comprehensively reviews brain-impaired patients: The studies involving patient populations and the diseases discussed in more than 10 works are included. Eleven diseases examined in this paper include autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, depressive disorders, anxiety and panic disorder, schizophrenia, mild cognitive impairment, Alzheimer’s disease, Parkinson’s disease, stroke, and traumatic brain injury. For each disease, the tasks used for examination, fNIRS variables, and significant findings on the impairment are discussed. The channel configurations and the regions of interest are also outlined. Detecting the occurrence of symptoms at an earlier stage is vital for better rehabilitation and faster recovery. This paper illustrates the usability of fNIRS for early detection of impairment and the usefulness in monitoring the rehabilitation process. Finally, the limitations of the current fNIRS systems (i.e., nonexistence of a standard method and the lack of well-established features for classification) and future research directions are discussed. The authors hope that the findings in this paper would lead to advanced breakthrough discoveries in the fNIRS field in the future.
R. E. Noskov, A. A. Zanishevskaya, A. A. Shuvalov
et al.
Optical fibers are widely used in bioimaging systems as flexible endoscopes capable of low-invasive penetration inside hollow tissue cavities. Here, we report on the technique which allows magnetic resonance imaging (MRI) of hollow-core microstructured fibers (HC-MFs), paving the way for combing MRI and optical bioimaging. Our approach is based on Layer-by-Layer assembly of oppositely charged polyelectrolytes and magnetite nanoparticles on the inner core surface of HC-MFs. Incorporation of magnetite nanoparticles into polyelectrolyte layers renders HC-MFs visible for MRI and induces the red-shift in their transmission spectra. Specifically, the transmission shifts up to 60 nm have been revealed for the several-layers composite coating along with the high-quality contrast of HC-MFs in MRI scans. Our results shed light on marrying fiber-based endoscopy with MRI that opens novel possibilities for minimally invasive clinical diagnostics and surgical procedures in vivo.