Optical Printing of Silicon Nanoparticles as Strain-Driven Nanopixels.

Silicon nanoparticles (Si NPs) supporting Mie resonances exhibit vivid structural colors on the subwavelength scale. For future wearable devices, next generation Si-based optical units need to be dynamic and stretchable for display, sensing, or signal processing required by human-computer interaction. Here, by utilizing the distance-sensitive electromagnetic coupling of Mie resonances, we maximize the active tuning effect of Si NP-based structures including dimers, oligomers, and NPs on WS2, which we called Si nanopixels. Through the optical tweezers-assisted printing of Si nanopixels, patterns can be formed on arbitrary flexible substrates. The strain-sensitive tuning of scattering spectra indicates their promising application on strain sensing of various stretchable substrates via a simple "spray and test" process. In the case of Si nanopixels on polydimethylsiloxane (PDMS), local strains around 1% can be detected by a scattering measurement. Moreover, we demonstrate that the scattering intensity variation of Si nanopixels printed on wrinkled tungsten disulfide (WS2) is pixel-dependent and wavelength-dependent. This property facilitates the application of information encryption, and we demonstrate that three barcodes can be independently encoded into the R, G, and B scattering channels through ternary logic represented by the strain-tuning effects of scattering.

Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm.

With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.

Hooped Amino-Group Chains in Porous Organic Polymers for Enhancing Heavy Metal Ion Removal.

By adjusting the stretch state of a triethylenetetramine (TETA) chain in an amine-functionalized porous organic polymer (POP), two adsorbents were designed to study the rational microenvironment for heavy metal ion removal. The quantum calculation elucidated that the hooped amino chains in FC-POP-CH2TETA-H exhibited stronger interactions with Pb(II) than the extended one in FC-POP-CH2TETA-E, not only through metal-ligand chelation but also metal coordination. The high binding energy of -2624 kJ mol-1 as well as the constructed microenvironment by the hooped amino chains ensured an extremely high Pb(II) capacity of 1134 mg g-1 on FC-POP-CH2TETA-H. Meanwhile, no more than 5 min to approach adsorption equilibrium revealed its ultrafast adsorption rate. It also showed excellent broad removal capability for multiple metal ions and nonsensitivity to pH. Therefore, by controlling the microenvironmental structures with suitable porosity, functional group stretching states, and coordination modes, the removal efficiency of heavy metal ions would be significantly enhanced, which further provided a promising strategy for designing a rational microenvironment to improve the task-specific separation properties.

Catalyst, Emulsion Stabilizer, and Adsorbent: Three Roles In One for Synergistically Enhancing Interfacial Catalytic Oxidative Desulfurization.

A Pickering emulsion catalytic system was proposed to reduce the transfer limitation between two immiscible reactant phases for enhancing the kinetics of heterogenetic oxidative desulfurization (ODS). By loading phosphotungstic acid (HPW) nanoparticles on a novel pyridine-based porous organic polymer of P[tVPB-VP x], the amphiphilic catalysts were produced and used as the stabilizer for Pickering emulsions. Specifically, an ultrafast ODS rate was realized in the HPW/P[tVPB-VP1]-stabilized Pickering emulsion catalytic system, and just within 15 min, 100 ppm dibenzothiophene (DBT) was completely oxidized by H2O2. Because the obtained hierarchical porous HPW/P[tVPB-VP x] catalysts showed both high adsorption capacity of DBT and excellent catalytic ODS performance, the catalysts assembling at the interface of emulsions provided this fastest reaction dynamics. Playing three roles of catalyst, emulsion stabilizer, and adsorbent, the synergistic functional catalytic emulsions can be a promising approach to significantly boost the heterogeneous catalytic ODS performance.