Research on the Dynamic Model of Fireball Thermal Dose Based on the Effective Band Integral Method.

In the brief combustion process of an explosive fireball, the fireball can release considerable radiant energy. Aiming at the problem that the Stephen-Boltzmann formula calculates the fireball surface radiant energy (full band), it does not match the working bands of most infrared thermal imagers. So, in this paper, we obtain dynamic parameters such as the temperature, diameter, and height of the fireball from the infrared thermal image of the thermobaric explosive fireball, achieve on-site atmospheric transmittance by the temperature calibration target, and integrate within the effective wavelength band of the infrared thermal imager, and a precise dynamic model of the fireball's thermal radiation dose was finally established. According to the fireball test data of the infrared thermal imaging camera in the 2-5 µm band, the heat dose of the fireball at different distances is calculated, which is about 1/2.5 of the calculation result of the Stephen-Boltzmann full-band integral formula. The calculations in this paper are more accurate than measurements from existing static models and provide a better assessment of the thermal damage performance of various types of munitions.

Spectroscopic measurement of the two-dimensional flame temperature based on a perovskite single photodetector.

Existing non-contact flame temperature measuring methods depend on complex, bulky and expensive optical instruments, which make it difficult for portable applications and high-density distributed networking monitoring. Here, we demonstrate a flame temperature imaging method based on a perovskite single photodetector. High-quality perovskite film epitaxy grows on the SiO2/Si substrate to fabricate the photodetector. Duo to the Si/MAPbBr3 heterojunction, the light detection wavelength is extended from 400 nm to 900 nm. Then, a perovskite single photodetector spectrometer has been developed using the deep-learning method for spectroscopic measurement of flame temperature. In the temperature test experiment, the spectral line of doping element K+ has been selected to measure the flame temperature. The photoresponsivity function of the wavelength was learned based on a commercial standard blackbody source. The spectral line of element K+ has been reconstructed using the photocurrents matrix by the regression solving photoresponsivity function. As a validation experiment, the "NUC" pattern is realized by scanning the perovskite single-pixel photodetector. Finally, the flame temperature of adulterated element K+ has been imaged with the error of 5%. It provides a way to develop high precision, portable, low-cost flame temperature imaging technology.

Triboelectric-Electromagnetic Hybrid Wind-Energy Harvester with a Low Startup Wind Speed in Urban Self-Powered Sensing.

Wind energy as a renewable energy source is easily available and widely distributed in cities. However, current wind-energy harvesters are inadequate at capturing energy from low-speed winds in urban areas, thereby limiting their application in distributed self-powered sensor networks. A triboelectric-electromagnetic hybrid harvester with a low startup wind speed (LSWS-TEH) is proposed that also provides output power within a wide range of wind speeds. An engineering-implementable propeller design method is developed to reduce the startup wind speed of the harvester. A mechanical analysis of the aerodynamics of the rotating propeller is performed, and optimal propeller parameter settings are found that greatly improved its aerodynamic torque. By combining the high-voltage output of the triboelectric nanogenerator under low-speed winds with the high-power output of the electromagnetic generator under high-speed winds, the harvester can maintain direct current output over a wide wind-speed range after rectification. Experiments show that the harvester activates at wind speeds as low as 1.2 m/s, powers a sensor with multiple integrated components in 1.7 m/s wind speeds, and drives a Bluetooth temperature and humidity sensor in 2.7 m/s wind speeds. The proposed small, effective, inexpensive hybrid energy harvester provides a promising way for self-powered requirements in smart city settings.

A Flexible Pressure Sensor Based on Silicon Nanomembrane.

With advances in new materials and technologies, there has been increasing research focused on flexible sensors. However, in most flexible pressure sensors made using new materials, it is challenging to achieve high detection sensitivity across a wide pressure range. Although traditional silicon-based sensors have good performance, they are not formable and, because of their rigidity and brittleness, they are not suitable for fitting with soft human skin, which limits their application in wearable devices to collect various signals. Silicon nanomembranes are ultra-thin, flexible materials with excellent piezoresistive properties, and they can be applied in various fields, such as in soft robots and flexible devices. In this study, we developed a flexible pressure sensor based on the use of silicon nanomembranes (with a thickness of only 340 nm) as piezoresistive units, which were transferred onto a flexible polydimethylsiloxane (PDMS) substrate. The flexible pressure sensor operated normally in the range of 0-200 kPa, and the sensitivity of the sensor reached 0.0185 kPa-1 in the low-pressure range of 0-5 kPa. In the high-pressure range of 5-200 kPa, the sensitivity of the sensor was maintained at 0.0023 kPa-1. The proposed sensor exhibited a fast response and excellent long-term stability and could recognize human movements, such as the bending of fingers and wrist joints, while maintaining a stable output. Thus, the developed flexible pressure sensor has promising applications in body monitoring and wearable devices.

Perovskite single-detector visible-light spectrometer.

We demonstrate a perovskite single-phototransistor visible-light spectrometer based on a deep-learning method. The size of the spectrometer is set to the scale of the phototransistor. A photoresponsivity matrix for the deep-learning system is learned from the characteristic parameters of the visible-light wavelength, gate voltage, and power densities of a commercial standard blackbody source. Unknown spectra are reconstructed using the corresponding photocurrent vectors. As a confirmatory experiment, a 532-nm laser and multipeak broadband spectrum are successfully reconstructed using our perovskite single-phototransistor spectrometer. The resolution is improved to 1 nm by increasing the number of sampling points from 80 to 400. In addition, a way to further improve the resolution is provided by increasing the number of sampling points, characteristic parameters, and training datasets. Furthermore, artificial intelligence technology may open pathways for on-chip visible-light spectroscopy.

Flame Imaging Technology Based on 64-Pixel Area Array Sensor.

High-resolution flame temperature images are essential indicators for evaluating combustion conditions. Tunable diode laser absorption spectroscopy (TDLAS) is an effective combustion diagnostic method. In actual engineering, due to the limitation of line-of-sight (LOS) measurement, TDLAS technology has the problems of small data volume and low dimensionality in measuring combustion fields, which seriously limits the development of TDLAS in combustion diagnosis. This article demonstrates a TDLAS imaging method based on a 64-pixel area array sensor to reconstruct the two-dimensional temperature field of the flame. This paper verifies the robustness of the Algebraic Reconstruction Technique (ART) algorithm through numerical simulation and studies the effects of temperature, concentration, and pressure on the second harmonic intensity based on the HITRAN database. The two-dimensional temperature field of the flame was reconstructed, and reconstruction accuracy was verified using thermocouples. The maximum relative error was 3.71%. The TDLAS detection system based on a 64-pixel area array sensor provides a way to develop high-precision, high-complexity flame temperature measurement technology.

Flame Edge Detection Method Based on a Convolutional Neural Network.

In this study, an improved flame edge detector based on convolutional neural network (CNN) was proposed. The proposed method can generate edge graphs and extract edge graphs relatively effectively. Our network architecture was based on VGG16 primarily, the last two max-pooling operators and all full connection layers of the VGG16 network were deleted, and the rest was taken as the basic network. The images output by the five convolution layers were upsampled to the size of the input images and finally fused to the edge image. Error calculation and back propagation of the fusion image and label image are carried out to form a weakly supervised model. Using the open datasets BSDS500 to train the network, the ODS F-measure can reach 0.810. Various experiments were carried out on different flame and fire images, including butane-air flame, oxygen-ethanol flame, energetic material flame, and oxygen-acetylene premixed jet flame, and the infrared thermogram was also verified by our method. The results demonstrate the effectiveness and robustness of the proposed algorithm.

Effect of Plane Mirrors Combined with Au-Nanoparticle Confinement on the Spectral Properties of Fe Plasma Induced by Laser-Induced Breakdown.

To overcome the shortcomings of low detection sensitivity and high spectral line background noise of traditional laser-induced breakdown spectroscopy (LIBS), a method of combining flat mirrors with gold nanoparticles (Au-NPs) was proposed. First, independent plane mirror and Au-NPs experiments were performed by using aluminum alloy samples. After that, the samples were placed under four conditions (None-LIBS; Three mirrors-LIBS; 20 nm Au-NPs-LIBS; 20 nm Au-NPs and Three mirrors-LIBS), and the differences between various spectral parameters were analyzed. The experimental results show that the optimal number of plane mirrors is 3, and the optimal size of gold nanoparticles is 20 nm. When 20 nm Au-NPs and Three mirrors are used in combination, the plasmonic spectral intensity can be effectively enhanced. The enhancement factor is up to 2.98 (Fe II 240.45 nm), and the signal-to-noise ratio (SNR) is significantly improved up to 10.03. The variation of the plasma temperature between 1 and 5 µs was also investigated, and the experimental results showed that the plasma temperature could be increased by the flat mirror, while the electron temperature was almost unchanged under the action of Au-NPs. It is shown that the combination of the two enhancement methods can effectively increase the spectral intensity and improve the signal-to-noise ratio, which will help to improve the detection performance of the LIBS system.

Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography.

Resolution is an important index for evaluating the reconstruction performance of temperature distributions in a combustion environment, and a higher resolution is necessary to obtain more precise combustion diagnoses. Tunable diode laser absorption tomography (TDLAT) has proven to be a powerful combustion diagnosis method for efficient detection. However, restricted by the line-of-sight (LOS) measurement, the reconstruction resolution of TDLAT was dependent on the size of the detection data, which made it difficult to obtain sufficient data for extreme environmental measurements. This severely limits the development of TDLAT in combustion diagnosis. To overcome this limitation, we proposed a super-resolution reconstruction method based on the super-resolution residual U-Net (SRResUNet) to improve the reconstruction resolution using a software method that could take full advantage of residual networks and U-Net to extract the deep features from the limited data of TDLAT to reconstruct the temperature distribution efficiently. A simulation study was conducted to investigate how the parameters would affect the performance of the super-resolution model and to optimize the reconstruction. The results show that our SRResUNet model can effectively improve the accuracy of reconstruction with super-resolution, with good antinoise performance, with the errors of 2-, 4-, and 8-times super-resolution reconstructions of approximately 5.3, 7.4, and 9.7%, respectively. The successful demonstration of SRResUNet in this work indicates the possible applications of other deep learning methods, such as enhanced super-resolution generative adversarial networks (ESRGANs) for limited-data TDLAT.

Flame Temperature Measurement Based on Laser-Induced Breakdown Spectroscopy and Element Doping.

In this study, based on the existing high-temperature measurement and calibration equipment, calibration experiments using the spectral emissivity of intrinsic element particles in the field were designed to achieve the accurate measurement of a temperature field. Laser-induced breakdown spectroscopy was used to select the corresponding elements, and the element doping method was used to approximate the real temperature field. After calibrating the camera, the temperature distribution and spectral emissivity distribution of the flame were calculated. The range of calculated values was determined to be well-consistent with data collected using an infrared thermal imager, which verified the accuracy of the experiment.

Design of Flexible Pressure Sensor Based on Conical Microstructure PDMS-Bilayer Graphene.

As a new material, graphene shows excellent properties in mechanics, electricity, optics, and so on, which makes it widely concerned by people. At present, it is difficult for graphene pressure sensor to meet both high sensitivity and large pressure detection range at the same time. Therefore, it is highly desirable to produce flexible pressure sensors with sufficient sensitivity in a wide working range and with simple process. Herein, a relatively high flexible pressure sensor based on piezoresistivity is presented by combining the conical microstructure polydimethylsiloxane (PDMS) with bilayer graphene together. The piezoresistive material (bilayer graphene) attached to the flexible substrate can convert the local deformation caused by the vertical force into the change of resistance. Results show that the pressure sensor based on conical microstructure PDMS-bilayer graphene can operate at a pressure range of 20 kPa while maintaining a sensitivity of 0.122 ± 0.002 kPa-1 (0-5 kPa) and 0.077 ± 0.002 kPa-1 (5-20 kPa), respectively. The response time of the sensor is about 70 ms. In addition to the high sensitivity of the pressure sensor, it also has excellent reproducibility at different pressure and temperature. The pressure sensor based on conical microstructure PDMS-bilayer graphene can sense the motion of joint well when the index finger is bent, which makes it possible to be applied in electronic skin, flexible electronic devices, and other fields.

Blind system identification of two-thermocouple sensor based on cross-relation method.

In dynamic temperature measurement, the dynamic characteristics of the sensor affect the accuracy of the measurement results. Thermocouples are widely used for temperature measurement in harsh conditions due to their low cost, robustness, and reliability, but because of the presence of the thermal inertia, there is a dynamic error in the dynamic temperature measurement. In order to eliminate the dynamic error, two-thermocouple sensor was used to measure dynamic gas temperature in constant velocity flow environments in this paper. Blind system identification of two-thermocouple sensor based on a cross-relation method was carried out. Particle swarm optimization algorithm was used to estimate time constants of two thermocouples and compared with the grid based search method. The method was validated on the experimental equipment built by using high temperature furnace, and the input dynamic temperature was reconstructed by using the output data of the thermocouple with small time constant.

Low energy-density recording with a high-repetition-rate laser beam in gold-nanorod-embedded discs.

In this paper, we report on the low energy-density recording with a high-repetition-rate femtosecond pulsed beam in homogenous gold-nanorod-dispersed discs by using low numerical aperture (NA) micro-optics. By focusing a femtosecond pulsed beam at a repetition rate of 82 MHz using a low NA DVD optical head, the spatially-stretched energy density introduces a temperature rising of the polymer matrix. This temperature rising facilitates the surface melting of gold nanorods, which leads to over one-order-of-magnitude reduction in the energy-density threshold for recording, compared with that by focusing single pulses through a high NA objective. Applying this finding, we demonstrate the dual-layer recording in gold-nanorod-dispersed discs with an equivalent capacity of 69 GB. Our results demonstrate the potential of ultra-high density three-dimensional optical memory with a low-cost and DVD-compatible apparatus.

Carboxyamidotriazole ameliorates experimental colitis by inhibition of cytokine production, nuclear factor-κB activation, and colonic fibrosis.

Carboxyamidotrizole (CAI) has been reported to suppress the production of tumor necrosis factor-α (TNF-α) and interleukin (IL)-1ß and be effective in rats with adjuvant arthritis. The aim of this study was to investigate the role of CAI in inflammatory bowel disease (IBD). We assessed the effect of CAI in dextran sodium sulfate-induced colitis. Inflammation was scored histologically, and potential mediators of IBD were assessed by immunohistochemical and molecular biochemical approaches. CAI-treated colitis animals revealed much fewer signs of colitis with significantly decreased macroscopic and microscopic scores than vehicle-treated animals. CAI inhibited the production of TNF-α, IL-1ß, and IL-6 in serum, supernatant of peritoneal macrophages, and lamina propria. CAI also decreased the expression of intercellular adhesion molecule-1 in colonic tissues. Furthermore, CAI prevented nuclear factor-κB (NF-κB) activation and inhibitor of nuclear factor-κBα phosphorylation and degradation. In addition, CAI showed a beneficial effect on colonic fibrosis, possibly by reducing the production of the fibrogenic cytokine transforming growth factor-ß. The results support that CAI administration is effective in ameliorating experimental colitis and preventing colonic fibrosis. The inhibition of proinflammatory cytokines and adhesion molecules and suppression of NF-κB activation seem to contribute to this effect.

Fangchinoline induces autophagic cell death via p53/sestrin2/AMPK signalling in human hepatocellular carcinoma cells.

BACKGROUND AND PURPOSE: Fangchinoline is a novel anti-tumour agent with little known of its cellular and molecular mechanisms of action. Here we have investigated the mode of cell death induced by fangchinoline and its underlying mechanism in two human hepatocellular carcinoma cell lines, HepG2 and PLC/PRF/5. EXPERIMENTAL APPROACH: Apoptosis and autophagy were monitored in fangchinoline-treated HepG2 and PLC/PRF/5 cells by histological methods. The signal transduction pathways involved in activation of autophagy were examined, using immunoblotting, real-time PCR and siRNA techniques. KEY RESULTS: Fangchinoline did not induce apoptosis in HepG2 and PLC/PRF/5 cells but triggered, dose-dependently, autophagy, an alternative mode of cell death which may contribute to fangchinoline's anti-tumour action. Nuclear translocation of p53 was involved in induction of autophagy by fangchinoline, followed by selective transactivation of the autophagy-related gene sestrin2 and initiation of the autophagic process. Signalling by the AMP-activated protein kinase was also involved as a downstream target of sestrin2 and induced mTOR-independent autophagic cell death in both cell lines. siRNA for Atg 5 or pharmacological block of p53 abolished fangchinoline-induced autophagy and inhibition of autophagy switched cell death to apoptosis in these cells, suggesting that cell death is irreversible once autophagy is induced by fangchinoline. CONCLUSIONS AND IMPLICATIONS: Fangchinoline is a highly specific agent inducing autophagic cell death in hepatocellular carcinoma cells with a novel mechanism, which elucidates the potential of fangchinoline to potentiate programmed cell death in cancer cells.

[Anti-inflammatory and analgesic potency of carboxyamidotriazole, a tumoristatic agent].

OBJECTIVE: To explore the potential anti-inflammatory and analgesic activities of carboxyamidotriazole (CAI). METHODS: A variety of animal models, including the croton oil-induced ear edema, the cotton-induced granuloma, the rat adjuvant-induced arthritis, were used to evaluate anti-inflammatory effect of CAI. Vascular endothelial growth factor (VEGF)--or histamine-stimulated local vascular permeability in mouse modulated by CAI was also determined. In addition, we assessed the effect of CAI on the levels of proinflammatory cytokines tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 beta (IL-beta) at the site of inflammation and in sera. Moreover, antinociceptive effect of CAI on inflammatory pain was assessed using acetic acid-induced writhing model and the formalin test. RESULTS: CAI significantly inhibited acute and chronic phases of inflammation, reduced VEGF or histamine-induced vascular permeability, and showed marked inhibition of proinflammatory cytokines such as TNF-alpha and IL-1 beta. CAI also showed potential therapeutic effect on peripheral inflammatory pain. CONCLUSION: CAI is a promising anti-inflammatory and analgesic agent.