Enhancing sensitivity in absorption spectroscopy using a scattering cavity

Compact high-resolution FBG strain interrogator based on laser-written 3D scattering structure in flat optical fiber

We demonstrate a fiber Bragg grating (FBG) strain interrogator based on a scattering medium to generate stable and deterministic speckle patterns, calibrated with applied strain, which are highly dependent on the FBG back-reflection spectral components. The strong wavelength-dependency of speckle patterns was previously used for high resolution wavemeters where scattering effectively folds the optical path, but instability makes practical realization of such devices difficult. Here, a new approach is demonstrated by utilizing femtosecond laser-written scatterers inside flat optical fiber, to enhance mechanical stability. By inscribing 15 planes of pseudo-randomized nanovoids (714 [Formula: see text] 500 voids per plane) as a 3D array in a 1 [Formula: see text] 0.7 [Formula: see text] 0.16 mm volume, the intrinsic stability and compactness of the device was improved. Operating as a wavemeter, it remained stable for at least 60 h with 45 pm resolution over the wavelength range of 1040-1056 nm. As a reflection mode FBG interrogator, after calibrating speckle patterns by applying tensile strain to the FBG, the device is capable of detecting microstrain changes in the range of 0-200 [Formula: see text] with a standard error of 4 [Formula: see text], limited by the translation stage step size. All these characteristics make it an interesting technology for filling the niche of low-cost, high-resolution wavemeters and interrogators which offer the best available trade-off between resolution, compactness, price and stability.

Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis

Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine produce dynamic scattering patterns and require deep learning of spatial and temporal features. We hypothesized that bacteria with variable bacterial densities and different Gram staining reactions would generate different speckle images. After deep learning of speckle patterns generated by various densities of bacteria in artificial urine, we validated the model in an independent set of clinical urine samples in a tertiary hospital. Even at a low bacterial density cutoff (1,000 CFU/mL), the model achieved a predictive accuracy of 90.9% for positive urine culture. At a cutoff of 50,000 CFU/mL, it showed a better accuracy of 98.5%. The model achieved satisfactory accuracy at both cutoff levels for predicting the Gram staining reaction. Considering only 30 min of analysis, our method appears as a new screening tool for predicting the presence of bacteria before urine culture. IMPORTANCE This study performed deep learning of multiple laser scattering patterns by the bacteria in urine to predict positive urine culture. Conventional urine analyzers have limited performance in identifying bacteria in urine. This novel method showed a satisfactory accuracy taking only 30 min of analysis without conventional urine culture. It was also developed to predict the Gram staining reaction of the bacteria. It can be used as a standalone screening tool for urinary tract infection.

Borophene as a rising star in materials chemistry: synthesis, properties and applications in analytical science and energy devices

Borophene is a two-dimensional material that has shown outstanding applications in energy storage devices and analytical chemistry.