We report on the synthesis and characterization of catalytic palladium nanoparticles (Pd NPs) and their immobilization in microfluidic reactors fabricated from polydimethylsiloxane (PDMS). The Pd NPs were stabilized with D-biotin or 3-aminopropyltrimethoxysilane (APTMS) to promote immobilization inside the microfluidic reactors. The NPs were homogeneous with narrow size distributions between 2 and 4 nm, and were characterized by transmission electron microscopy (TEM), selected-area electron diffraction (SAED), and x-ray diffraction (XRD). Biotinylated Pd NPs were immobilized on APTMS-modified PDMS and glass surfaces through the formation of covalent amide bonds between activated biotin and surface amino groups. By contrast, APTMS-stabilized Pd NPs were immobilized directly onto PDMS and glass surfaces rich in hydroxyl groups. Fourier transform infrared spectroscopy (FT-IR) and x-ray photoelectron spectroscopy (XPS) results showed successful attachment of both types of Pd NPs on glass and PDMS surfaces. Both types of Pd NPs were then immobilized in situ in sealed PDMS microfluidic reactors after similar surface modification. The effectiveness of immobilization in the microfluidic reactors was evaluated by hydrogenation of 6-bromo-1-hexene at room temperature and one atmosphere of hydrogen pressure. An average first-run conversion of 85% and selectivity of 100% were achieved in approximately 18 min of reaction time. Control experiments showed that no hydrogenation occurred in the absence of the nanocatalysts. This system has the potential to provide a reliable tool for efficient and high throughput evaluation of catalytic NPs, along with assessment of intrinsic kinetics.

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Compressed sensing (CS) can be used to obtain a signal through undersampling and reconstruction, which enables the atomic force microscope (AFM) to spatially under-sample the topography information to increase the imaging rate and reduce the amount of probesample interaction. However, the imaging mode of the AFM, which would result in the huge occupation of computing resources including computing time and memory space, makes it inefficient and time-consuming to apply the normal image reconstruction method directly to recover the sample topography from undersampled data. And it is unrealistic to recover a high-solution image by the normal compressed sensing. Here, a novel image reconstruction method based on Bayesian compressing sensing for the undersampled AFM data with noise is proposed to significantly reduce the occupation of computing resources while guaranteeing a high-quality image reconstruction. In the proposed method, the AFM image is regarded as a collection of independent vectors and each vector (a subset of the pixels) is recovered separately. The Bayesian compressed sensing is introduced to provide a better reconstruction performance. The reconstruction experiments demonstrate that the proposed method can significantly reduce the occupation of computing resources while achieving high-quality AFM image reconstruction from the undersampled data with noise. The reconstruction time has been shortened from tens of minutes to less than one minute and the RAM used is reduced to only 1/n 2 of the normal algorithms, which allows the AFM image reconstruction from undersampled data to be easily and conveniently achieved in any personal computer.

Nonwovens are widely applied in air filtration field for their multi-layered fibrous structures and inter-connected pores. Despite intensively used, conventional microfiber nonwovens usually suffer from low filtration efficiencies due to...

A quartz tuning fork (QTF) has been widely used as a force sensor of the frequency modulation atomic force microscope due to its ultrahigh stiffness, high quality factor and self-sensing nature. However, due to the bulky structure and exposed surface electrode arrangement, its application is limited, especially in liquid imaging of in situ biological samples, ionic liquids, electrochemical reaction, etc. Although the complication can be resolved by coating insulating materials on the QTF surface and then immersing the whole QTF into the liquid, it would result in a sharp drop of the quality factor, which will reduce the sensitivity of the QTF. To solve the problem, a novel method, called the balanced trolling quartz tuning fork (BT-QTF), is introduced here. In this method, two same probes are glued on both prongs of the QTF separately while only one probe immersed in the liquid. With the method, the hydrodynamic interaction can be reduced, thus the BT-QTF can retain a high quality factor and constant resonance frequency. The stable small vibration of the BT-QTF can be achieved in the liquid. Initially, a theoretical model is presented to analyze the sensing performance of the BT-QTF in the liquid. Then, the sensing performance analysis experiments of the BT-QTF have been performed. At last, the proposed method is applied to atomic force microscope imaging different samples in the liquid, which proves its feasibility.