This study reports a novel 3D-printed interface designed for efficient solvent elimination in HPLC-FTIR. The interface was fabricated and optimized for material, geometry, and solvent elimination. Validation using ascorbic acid determination in a dietary supplement demonstrates superior solvent elimination, improved spectral quality, and practical applicability under the optimal conditions of isocratic elution with water–methanol (60:40, v/v), column temperature of 25 °C, pressure of 95 bar, flow rate of 1 mL min−1, UV detection at 285 nm, and a monolithic RP-18e C18 column (100 mm × 4.6 mm). During solvent elimination N2 was used with a flow of 180 mL min−1 and heating temperature of 50 °C.This cost-effective and adaptable interface enhances HPLC-FTIR coupling for real-world applications.

This article proposes a novel ultra-sensitive fiber optic temperature sensor based on a compact cascaded Fabry-Perot interferometer (FPI) and Vernier effect (VE). Firstly, a conical ultraviolet (UV) adhesive Fabry-Perot (F-P) cavity is prepared on the end face of a single-mode fiber (SMF). When the F-P cavity based on UV adhesive is subjected to thermal effects, the large thermal effect of the UV adhesive causes a variation of the optical path difference of the light transmitted in the F-P cavity, resulting in spectral drift. Temperature measurement is achieved by demodulating the spectral wavelength drift. Firstly, FPI1 and FPI3 are two UV adhesive based FPIs prepared at the SMF tip using this method. Then, they are inserted separately into two capillary tubes, and forming the compact cascaded FPI structure with the SMF located at the other end of the capillary tube. With the help of the charge coupled device (CCD) imaging system, the length of the air-cavity of two compact cascaded FPI structures is carefully adjusted, forming separately the VE sensor S1 and the harmonic vernier effect (HVE) sensor S2. S1 can further amplify the sensitivity of FPI1, and S2 can further amplify the sensitivity of FPI3. The experimental results show that the temperature sensitivities of FPI1 and FPI3 are 0.4631 nm/°C and −0.1110 nm/°C, respectively, and the temperature sensitivities of S1 and S2 are 3.0099 nm/°C and 3.5080 nm/°C. S1 increased the sensitivity of FPI1 by 6.5 times, and S2 improved the sensitivity of FPI3 by 31.6 times. The proposed sensor boasts a compact structure, simple manufacturing, durability, and high sensitivity. Meanwhile, the small sensing head is particularly useful for temperature detection in narrow spaces.

Precise determination of dissolved phosphorus concentration in antibiotic fermentation broths is essential for microbial growth and metabolism during fermentation. The conventional method often relies on manual operations, leading to low reproducibility. Herein, we applied the phosphomolybdenum blue reaction to flow injection analysis (FIA) to create an automated approach for phosphorus quantification in antibiotic fermentation broths. Key parameters for the FIA system, including flow rate (1.75 mL/min), ammonium molybdate concentration (20 g/L), and sulfuric acid concentration (0.30 mol/L), were optimized, resulting in a reliable standard curve. Compared to the conventional method, which requires a 10-min incubation in a 50 °C water bath, the FIA method enables measurements in approximately 20 s (sample throughput = 178 h−1). Furthermore, this method offers a wider linear range (0.53–200 mg/L). With good accuracy and precision (relative standard deviation (RSD)=0.35%) and a low limit of detection (LOD, 0.16 mg/L) and limit of quantification (LOQ, 0.53 mg/L), this FIA method presents significant potential for microbial fermentation monitoring. It offers advantages such as automation, low reagents consumption, and the capacity to produce high-precision data at large scale.

Uranium ore is a vital energy mineral resource, and its accurate exploration is of great significance in ensuring energy security and promoting the development of the industry. Laser-induced breakdown spectroscopy (LIBS) enables real-time quantitative analysis without the need for complex sample pretreatment, offering a unique advantage in elemental analysis. However, accurate quantification is still challenging for LIBS due to complex matrix effects. Herein, a method based on LIBS combined with a convolutional neural network (CNN) is reported for the accurate determination of uranium in uranium ore. The root mean square error of prediction (RMSEP), coefficient of determination (R2), relative error prediction (REP), and relative standard deviation (RSD) values of prediction on the test groups were used to evaluate the quantitative performance of CNN. The results showed that the RMSEP and R2 of the prediction of a two-dimensional convolutional neural network (2D-CNN) model were 0.0347 and 0.9874, respectively. In addition, the performance of 2D-CNN was compared with that of one-dimensional convolutional neural networks (1D-CNN), partial least squares regression (PLSR), and artificial neural networks (ANN). The results demonstrate that LIBS coupled with 2D-CNN achieved accurate quantification of the uranium and showed potential in the characterization of uranium ore.

A fiber-optic sensor is reported for simultaneous measurement of temperature and pressure. The structure is cascaded by a Mach-Zehnder interferometer (MZI) of a single mode fiber (SMF)-thin core fiber (TCF)–SMF unit and anti-resonance reflective optical waveguide (ARROW) of an SMF-capillary-SMF unit. The sensing principles and properties of the MZI and the ARROW were analyzed and the response to temperature and pressure were characterized. The experimental results show temperature sensitivity of ∼0.130 nm/°C accompanied by a linear fitting coefficient of ∼0.997, and pressure sensitivity of ∼3.32 nm/MPa with a linearity of ∼0.994. This work demonstrates an easy-to-implement structure that provides novel and reliable performance for environmental monitoring and engineering practice.

Tunable diode laser absorption tomography (TDLAT) is a well-established method for combustion diagnostics. However, conventional linear reconstruction algorithms tend to yield unsatisfactory results in scenarios where optical access is severely restricted. Although nonlinear tomography combined with line-of-sight measurements can fully exploit the nonlinear relationship between the reconstructed target field and the projection data to image temperature distributions, its high computational cost restricts its practical application. The advent of deep learning technology has provided a new avenue for addressing this challenge. Neural network algorithms can effectively establish a mapping relationship between the projection data and the reconstructed target field, enabling fast and accurate reconstruction. Leveraging this progress, this paper proposes a novel temperature field reconstruction algorithm based on a deep learning framework. This approach offers a new solution to the nonlinear laminar problem associated with two-dimensional temperature field reconstruction in laser absorption spectroscopy. The results demonstrate that the reconstruction quality of the temperature field steadily improves with an increasing number of projection angles (N). When N = 16, the average error of the reconstructed temperature field is only 1.38%, and the peak signal-to-noise ratio (PSNR) reaches 40.69. Moreover, the algorithm maintains robust reconstruction performance even with fewer projection angles. Compared to existing popular neural network reconstruction algorithms, the proposed approach demonstrates superior noise immunity, making it particularly well-suited for real-time monitoring of actual combustion environments.

Temperature is an important indicator for assessing the state of combustion. With the development of technology, the application of noncontact temperature measurement techniques is becoming increasingly widespread. The tunable diode laser absorption spectroscopy (TDLAS) technique is noninvasive, highly sensitive, and capable of continuous measurement. However, the TDLAS technology has limitations in terms of spatial resolution, and existing studies are homogeneous in terms of the selection of characteristic spectral lines. In this paper, two absorption lines of oxygen (O2) were innovatively selected. A 64-element array sensor was used to acquire signals. A standard high-temperature tube furnace was used to calibrate the sensor, and the mathematical relationship between the integral absorbance of the absorption lines and temperature was obtained. Finally, the accuracy and stability of the calibration results were verified. This study is important for the further investigation of temperature distribution in high-resolution combustion fields.

The planar flexible Koch fractal eddy current probe exhibits high consistency in detecting short cracks in all orientations. However, it is limited by a weak signal caused by the small number of coil turns. This paper utilizes multilayer flexible circuit board technology to address the issue. The probe is designed with a mutual inductance coil structure and is arranged on a 10-layer flexible circuit board. This board includes one layer for the excitation coil and nine layers for the pick-up coil. The probe’s performance was studied through C-scan experiments, which examined cracks of varying depths, widths, orientations, and lengths. The experimental results show that the probe can detect minimum crack sizes of 0.5 mm × 0.15 mm × 1 mm for 5083-aluminum alloy, 3 mm × 0.15 mm × 1 mm for GH4169 superalloy, and 1 mm × 0.15 mm × 1 mm for Q235 steel. The results demonstrate that the flexible fractal eddy current probe has significant advantages in detecting small cracks.

Mosquitoes, as major vectors of dangerous diseases, demand effective control measures for public health. Larvicides, while offering a potential solution, can exhibit varying effects on different mosquito species and developmental stages during efficacy studies. Standardizing mosquito developmental stages and ensuring optimal, synchronized development rates of various larval stages are crucial for larvicidal experiments and maximizing pupal production. Manual separation of different stages of larvae is labor intensive, time consuming and error prone. Therefore, this study aimed to investigate the use of a stereolithography 3D-printed device for the separation of mosquito larvae. This device is designed to have three modules to collect different larval stages based on their radial size differences. Each module of the device was equipped with small vibration motors to enable vibration-assisted filtration to prevent clogging. The accuracy of the larvae separation device was studied by repeated experiments and collected samples were analyzed under an optical microscope. The 3D-printed device was demonstrated to be orders of magnitude faster and more accurate compared to manual separation process. A larvicidal bioassay was also designed to investigate larvicidal assay impact on the survival of collected L3 and L4 specimens. Overall, it was shown that the presented device carries an important potential for rapid and accurate separation of different size larvae for larvicidal studies.

A critical limitation in developing portable Fourier transform-infrared (FTIR) stand-off chemical detectors is the library optimization which reduces the number of false alarms and maintains the performance. Ideally, a library should be established through high-resolution (0.5 cm−1) FTIR spectroscopy in the laboratory. However, due to the weight requirements of a portable FTIR stand-off detector, its optical system and mirror-moving range have limitations. Consequently, most portable FTIR stand-off detectors have been developed with a low resolution of approximately 4 to 16 cm−1. In this study, we developed a portable FTIR stand-off detector with a dual library of SF6. Moreover, we investigated its detection efficiency depending on the single library condition through realistic long-range (3 km) SF6 spectra in a field test.