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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.

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.

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.

The development of new catalytic enantioselective access to CF3-containing stereogenicity is of great interest for the expediting discovery of lead compounds yet remains challenging. We devised a general and modular approach to facilely access enantioenriched α-CF3 ketones via nickel-catalyzed reductive cross-coupling of readily available acid chlorides and racemic α-CF3 alkyl bromides in an enantioconvergent fashion under mild conditions. This protocol featured neighboring directing group-free, high chemoselectivity, excellent functional group tolerance, facile scale-up, and notable amenability to straightforward downstream elaboration toward molecule complexity, thus constituting a reliable, direct, practical, and efficient synthetic alternative to furnish enantiopure α-CF3 carbonyls. Interestingly, an appropriate choice of the phosphine ligand as co-ligand plays an important role in high efficiency and asymmetric induction. Mechanistic studies suggest a radical chain pathway.

Supplemental material, Appendix_Figures_(1) for Neurological disorders in autism: A systematic review and meta-analysis by Pei-Yin Pan, Sven Bölte, Preet Kaur, Sadia Jamil and Ulf Jonsson in Autism

Supplemental material, Appendix_Table_S1-S3 for Neurological disorders in autism: A systematic review and meta-analysis by Pei-Yin Pan, Sven Bölte, Preet Kaur, Sadia Jamil and Ulf Jonsson in Autism

A new Λ-shaped α-cyanostilbene derivative (TSA) containing an active amino group with fluorescence emission both in solution and the solid state was designed, synthesized and characterized. Its colour and fluorescence emission can be switched by external stimuli including acid/base, trinitrophenol (picric acid, PA) and grinding. The 1H NMR spectra of TSA were recorded before and after alternately adding trifluoroacetic acid (TFA) and triethylamine (TEA), which confirmed that acidichromism originated in the transformation of protonation/deprotonation between TSA and TSA-H+. Powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC) and scanning electron microscopy (SEM) revealed the crystalline–amorphous state transformation between the original and ground compounds, and this transformation could reasonably account for the mechanochromic (MFC) luminescence properties. The results revealed that TSA can be used as a fluorescence sensor for acid/base and PA, and as a rewritable information storage material. Moreover, it can also be applied in white light-emitting diodes (w-LEDs) based on its desirable fluorescence emission in the solid state.