Van der Waals two-color infrared photodetector

With the increasing demand for multispectral information acquisition, infrared multispectral imaging technology that is inexpensive and can be miniaturized and integrated into other devices has received extensive attention. However, the widespread usage of such photodetectors is still limited by the high cost of epitaxial semiconductors and complex cryogenic cooling systems. Here, we demonstrate a noncooled two-color infrared photodetector that can provide temporal-spatial coexisting spectral blackbody detection at both near-infrared and mid-infrared wavelengths. This photodetector consists of vertically stacked back-to-back diode structures. The two-color signals can be effectively separated to achieve ultralow crosstalk of ~0.05% by controlling the built-in electric field depending on the intermediate layer, which acts as an electron-collecting layer and hole-blocking barrier. The impressive performance of the two-color photodetector is verified by the specific detectivity (D*) of 6.4 × 109 cm Hz1/2 W−1 at 3.5 μm and room temperature, as well as the promising NIR/MWIR two-color infrared imaging and absolute temperature detection.

Engineering sensitivity and spectral range of photodetection in van der Waals materials and hybrids

Exploration of van der Waals heterostructures in the field of optoelectronics has produced photodetectors with very high bandwidth as well as ultra-high sensitivity. Appropriate engineering of these heterostructures allows us to exploit multiple light-to-electricity conversion mechanisms, ranging from photovoltaic, photoconductive to photogating processes. These mechanisms manifest in different sensitivity and speed of photoresponse. In addition, integrating graphene-based hybrid structures with photonic platforms provides a high gain-bandwidth product, with bandwidths >> 1 GHz. In this review, we discuss the progression in the field of photodetection in 2D hybrids. We emphasize the physical mechanisms at play in diverse architectures and discuss the origin of enhanced photoresponse in hybrids. Recent developments in 2D photodetectors based on room temperature detection, photon-counting ability, integration with Si and other pressing issues, that need to be addressed for these materials to be integrated with industrial standards have been discussed.

Multiple Linear Regression and Deep Learning in Body Temperature Detection and Mask Detection

In this new normal era, many activities began to operate again, such as offices, malls, etc. This creates a potential mass crowd. The public must follow health protocols as recommended by the government, including wearing masks and checking the temperature to anticipate the spread of the coronavirus. This study tested a tool that included image processing and artificial intelligence to help implement health protocols as recommended by the government. This tool connects Raspberry PI, Thermal Camera (amg8833), Pi Camera, an ultrasonic sensor with Multiple Linear Regression and Deep Learning algorithms. The purpose of this tool is to detect body temperature and detect the use of masks. The system will check on the pi camera frame whether the person is wearing a mask or not. The system is trained using the Deep Learning method to detect the use of masks. The system will check the temperature of the human body and the distance between humans and the tool. Temperature and distance data are entered in multiple linear regression formulas to get more accurate results. The processed results of the system will be displayed on the monitor screen if detected using a mask and the normal temperature will be green and if it is not detected it will be red and give a warning sound. The data is sent to the server and displayed via the web. We found that this tool succeeded in detecting body temperature within a distance of 1 to 3 meters with an accuracy of 99.49%, detecting people using masks with an accuracy of 94.71%, and detecting people not wearing masks with an accuracy of 97.7%.

On how the mechanochemical and co-precipitation synthesis method changes the sensitivity and operating range of the Ba2Mg1-xEuxWO6 optical thermometer

The suitability of Ba2MgWO6 (BMW) double perovskite doped with Eu3+ for the construction of an optical thermometer was tested. It has been shown that by controlling the conditions of BMW synthesis, the sensitivity of the optical thermometer and the useful range of its work can be changed. Pure BMW and doped with Eu3+ samples were prepared using the mechano-chemical and co-precipitation methods. Both the absolute sensitivity and the relative sensitivity in relation to the synthesis route were estimated. The findings proved that the relative sensitivity can be modulated from 1.17%K−1 at 248 K, to 1.5%K−1 at 120 K for the co-precipitation and the mechanochemical samples, respectively. These spectacular results confirm the applicability of the Ba2MgWO6: Eu3+ for the novel luminescent sensors in high-precision temperature detection devices. The density-functional theory was applied to elucidate the origin of the host emission.

High-Temperature Thermal Diffusivity Measurements Using a Modified Ångström's Method with Transient Infrared Thermography

This work reports a method to measure thermal diffusivity of thin disk samples at high temperatures (900 -1150K) using a modified Angstrom's method. Conventionally, samples are heated indirectly from the surroundings to reach high temperatures for such measurements, and this process is time-consuming, typically requiring hours to reach stable temperatures. In this work samples are heated directly in a custom instrument by a concentrated light source and are able to reach high steady-periodic temperatures in 10 mins, thus enabling rapid thermal diffusivity characterization. Further, existing Angstrom's methods for high temperatures use thermocouples for temperature detection that are commonly attached to samples via drilling and welding, which are destructive to samples and introduce thermal anomalies. In this work we use an infrared camera calibrated to 2000 C for non-contact, non-destructive and data-rich temperature measurements. We present an image analysis approach to process the IR data that significantly reduces random noise in temperature measurements. We extract amplitude and phase from processed temperature profiles and demonstrate that these metrics are insensitive to uncertainty in emissivity. Previous studies commonly use regression approaches for parameter estimation that are ill-posed (i.e., non-unique solutions) and lack rigorous characterization of parameter uncertainties. Here, we employ a surrogate-accelerated Bayesian framework and a 'No-U-Turn' sampler for uncertainty quantification. The reported results are validated using graphite and copper disks and exhibit excellent agreement within 5% as compared to reference values obtained by other methods.