A Metal-Organic Complex Constructed from Co(II), Azo-amide-pyridyl and Benzenetricarboxylate Mixed Ligands: Efficient Catalysis for Selective Oxidation of Benzyl Alcohols to Benzyl Acids.

By using one-step hydrothermal synthesis, a novel metal-organic complex containing Co(II), the azo-amide-pyridyl ligand (E)-4,4'-(diazene-1,2-diyl)bis(N-(pyridin-3-yl)benzamide (DABA) and benzenetricarboxylate was synthesized, with a molecular formula of [Co2 (DABA)0.5 (MTC)(µ3 -OH)(H2 O)2 ] ⋅ 2H2 O (namely 1, DABA=(E)-4,4'-(diazene-1,2-diyl)bis(N-(pyridin-3-yl)benzamide, H3 MTC=1,2,4-benzenetricarboxylic acid) which was characterized by single crystal X-ray diffraction, PXRD, IR spectroscopy, TGA, and XPS. In the structure of complex 1, tetranuclear Co(II) clusters were connected by MTC to form a 2D bilayer structure and further constructed a 3D structure with DABA ligand. Complex 1 was used as an efficient heterogeneous catalyst for the oxidation of benzyl alcohol, and the conversion rate of benzyl alcohol reached 98.6 % and the selectivity of benzoic acid reached 94.8 %. In addition, complex 1 can be reused 5 times without significant loss of activity. The oxidation of benzyl alcohol with different substituents also showed satisfactory conversion and selectivity, indicating that complex 1 had good catalytic performance.

Design and fabrication of SU-8 polymer arrayed waveguide gratings based on flexible PDMS substrates.

We investigated and fabricated 1×8 and 1×16 traditional/saddle arrayed waveguide grating (AWG) wavelength division multiplexing devices on flexible substrates. The core layer was made of the negative epoxy photoresist SU-8, and polydimethylsiloxane (PDMS) material was selected for the cladding based on the high refractive index difference. A chemical modification method of PDMS was proposed to enhance the film-forming characteristics of the SU-8 core layer on PDMS substrates. After fabricating and characterizing the optical properties of the AWGs, the dimensions of the proposed 1×8 and 1×16 AWGs were 0.9×0.8cm2 and 1.1×0.9cm2, respectively. Experimental results showed that the saddle-type 1×8 and 1×16 AWGs had good signal transmission characteristics, and the insertion losses were only 5.1 dB and 6.8 dB, respectively, which were lower than those of traditional-type AWGs with the same dimensions and number of waveguides.

Classification of electrocardiogram signals with waveform morphological analysis and support vector machines.

Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple features by combining waveform morphology and frequency domain statistical analysis, which offer improved classification accuracy and minimise the time spent for classifying signals. A wavelet packet is used to decompose a denoised ECG signal, and the singular value, maximum value, and standard deviation of the decomposed wavelet packet coefficients are calculated to obtain the frequency domain feature space. The slope threshold method is applied to detect R peak and calculate RR intervals, and the first two RR intervals are extracted as time-domain features. The fusion feature space is composed of time and frequency domain features. A combination of support vector machine (SVM) with the help of grid search and waveform morphological analysis is applied to complete nine types of ECG signal classification. Computer simulations show that the accuracy of the proposed algorithm on multiple types of arrhythmia databases can reach 96.67%.

Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG.

Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%.

SOI Waveguide Bragg Grating Photonic Sensor for Human Body Temperature Measurement Based on Photonic Integrated Interrogator.

A waveguide Bragg grating (WBG) provides a flexible way for measurement, and it could even be used to measure body temperature like e-skin. We designed and compared three structures of WBG with the grating period, etching depth, and duty cycle. The two-sided WBG was fabricated. An experimental platform based on photonic integrated interrogator was set up and the experiment on the two-sided WBG was performed. Results show that the two-sided WBG can be used to measure temperature changes over the range of 35-42 °C, with a temperature measurement error of 0.1 °C. This approach has the potential to facilitate application of such a silicon-on-insulator (SOI) WBG photonic sensor to wearable technology and realize the measurement of human temperature.