Renin imprinted Poly(methyldopa) for biomarker detection and disease therapy.

Conventional molecularly imprinted polymers (MIPs) perform their functions principally depended on their three dimensional (3D) imprinted cavities (recognition sites) of templates. Here, retaining the function of recognition sites resulted from the imprinting of template molecules, the role of functional monomers is explored and expanded. Briefly, a class of dual-functional renin imprinted poly(methyldopa) (RMIP) is prepared, consisting of a drug-type function monomer (methyldopa, clinical high blood pressure drug) and a corresponding disease biomarker (renin, biomarker for high blood pressure disease). To boost target-to-receptor binding ratio and sensitivity, the microstructure of recognition sites is beforehand calculated and designed by Density Functional Theory calculations, and the whole interfacial structure, property and thickness of RMIP film is regulated by adjusting the polymerization techniques. The dual-functional applications of RMIP for biomarker detection and disease therapy in vivo is explored. Such RMIP-based biosensors achieves highly sensitive biomarker detection, where the LODs reaches down to 1.31 × 10-6 and 1.26 × 10-6 ng mL-1 for electrochemical and chemical polymers, respectively, and the application for disease therapy in vivo has been verified where displays the obviously decreased blood pressure values of mice. No acute and long-term toxicity is found from the pathological slices, declaring the promising clinical application potential of such engineered RMIP nanostructure.

Real­time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm.

Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses Chimp Optimization Algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic Algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale Optimization Algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25 % and 99.11 % as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75 % and 1.01 % as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 s.

Synthesis of core-shell Ti@Ni-P spherical powder by Ni electroless plating.

Ni-P plated Ti powders with core-shell structure were prepared by Ni electroless plating. The micromorphology and growth mechanism of core-shell Ti@Ni-P powders were analyzed by field emission scanning electron microscopy (FESEM) with an energy dispersive spectrometer (EDS). The results show the overall uniformity of Ni-P coated Ti powders is improved by two pretreatments, namely NaOH preprocessing and alkaline electroless preplating, compared with a single alkaline electroless pretreatment. The surface morphology of the Ni-P plated powders is a typical spaced spherical nodular structure. Meanwhile, the growth mechanism of the Ni-P coated Ti powder is illuminated in detail.

Solution processed single-emission layer white polymer light-emitting diodes with high color quality and high performance from a poly(N-vinyl)carbazole host.

Low cost and high performance white polymer light-emitting diodes (PLEDs) are very important as solid-state lighting sources. In this research three commercially available phosphors were carefully chosen, bis[2-(4,6-difluorophenyl)pyridinato-N,C(2)](picolinate)iridium(III) (FIrpic), bis[2-(2-pyridinyl-N)phenyl-C](2,4-pentanedionato-O(2),O(4))iridium(III) [Ir(ppy)2(acac)], and bis(2-phenyl-benzothiazole-C(2),N)(acetylacetonate)iridium(III) [Ir(bt)2(acac)], plus a home-made red phosphor of tris[1-(2,6-dimethylphenoxy)-4-(4-chlorophenyl)phthalazine]iridium(III) [Ir(MPCPPZ)3], and their photophysical and morphological properties were systematically studied as well as their applications in single-emission layer white PLEDs comprising poly(N-vinylcarbazole) as host. Additionally, the electrochemical properties and energy level alignment, possible energy transfer process, and thin-film morphology were also addressed. The binary blue/orange complementary white PLEDs exhibit stable electroluminescence spectra, wide spectrum-covering region range from 380-780 nm, and high color rendering index (CRI) over 70 with Commission Internationale de l'Eclairage coordinates x,y (CIEx,y) of (0.388, 0.440), correlated color temperature (CCT) of around 4400, plus high efficiency of 25.5 cd A(-1). The optimized red-green-blue white PLEDs showed a satisfactory CRI of around 82.4, maximum current efficiency of 20.0 cd A(-1) and external quantum efficiency (EQE) of 10.8%, corresponding to a CCT of 3700-2800, which is a warm-white hue. At last, stable and high color quality, red-green-orange-blue four component white PLEDs, with a CRI of over 82, a high efficiency of 24.0 cd A(-1), EQE of 11.5%, and high brightness of 43,569.9 cd m(-2) have been obtained.