Synergetic Role of Thermal Catalysis and Photocatalysis in CO2 Reduction on Cu2/MoS2.

Effective activation of CO2 is a primarily challenging issue in CO2 reduction to value-added hydrocarbon chemicals, due to the large energy gap between the highest-occupied and lowest-unoccupied molecular orbitals (HOMO-LUMO). Here, we employ state-of-the-art first-principles calculations to explore the synergetic role of thermal catalysis and photocatalysis in CO2 reduction, on typical single-atom scale catalyst, i.e., Cu2 magic cluster on a semiconducting two-dimensional MoS2 substrate. It is identified that only about 1% of the hot electrons excited from the MoS2 substrate by at least 6.3 eV photons may be trapped by the inert CO2 molecule at the expense of 400 fs. Moreover, the physisorption-to-chemisorption transition of CO2 can be observed within 500 fs upon overcoming an about 0.05 eV energy barrier. Contrastingly, upon chemisorption, the activated CO2δ- species may trap about 7% of the hot electron excited from the MoS2 substrate by about 2.5 eV visible photons, with a cost of 140 fs.

Biofilms Controlling in Industrial Cooling Water Systems: A Mini-Review of Strategies and Best Practices.

Biofilm formation and growth is a significant concern for water treatment professionals, as it can lead to the contamination of water systems and pose a threat to public health. Biofilms are complex communities of microorganisms that adhere to surfaces and are embedded in an extracellular matrix of polysaccharides and proteins. They are notoriously difficult to control, as they provide a protective environment for bacteria, viruses, and other harmful organisms to grow and proliferate. This review article highlights some of the factors that favor biofilm growth, as well as various strategies for controlling biofilm in water systems. Adopting the best available technologies, such as wellhead protection programs, proper industrial cooling water system maintenance, and filtration and disinfection, can prevent the formation and growth of biofilms in water systems. A comprehensive and multifaceted approach to biofilm control can reduce the occurrence of biofilms and ensure the delivery of high-quality water to the industrial process.

µ-Net: Medical image segmentation using efficient and effective deep supervision.

Although the existing deep supervised solutions have achieved some great successes in medical image segmentation, they have the following shortcomings; (i) semantic difference problem: since they are obtained by very different convolution or deconvolution processes, the intermediate masks and predictions in deep supervised baselines usually contain semantics with different depth, which thus hinders the models' learning capabilities; (ii) low learning efficiency problem: additional supervision signals will inevitably make the training of the models more time-consuming. Therefore, in this work, we first propose two deep supervised learning strategies, U-Net-Deep and U-Net-Auto, to overcome the semantic difference problem. Then, to resolve the low learning efficiency problem, upon the above two strategies, we further propose a new deep supervised segmentation model, called µ-Net, to achieve not only effective but also efficient deep supervised medical image segmentation by introducing a tied-weight decoder to generate pseudo-labels with more diverse information and also speed up the convergence in training. Finally, three different types of µ-Net-based deep supervision strategies are explored and a Similarity Principle of Deep Supervision is further derived to guide future research in deep supervised learning. Experimental studies on four public benchmark datasets show that µ-Net greatly outperforms all the state-of-the-art baselines, including the state-of-the-art deeply supervised segmentation models, in terms of both effectiveness and efficiency. Ablation studies sufficiently prove the soundness of the proposed Similarity Principle of Deep Supervision, the necessity and effectiveness of the tied-weight decoder, and using both the segmentation and reconstruction pseudo-labels for deep supervised learning.

Recording, erasing, and rewriting of ripples on metal surfaces by ultrashort laser pulses.

Recording, erasing, and rewriting of ripples are achieved by applying femtosecond laser pulses on tungsten surfaces. Ripples oriented perpendicular to the polarization direction of the writing beam can be recorded on a metal surface by exposing the sample to a series of linearly polarized pulses. When applying the second series of pulses with varied polarization direction on the same place, the original ripples can be erased, and new ripples are rewritten with the orientation perpendicular to the polarization of the second group of pulses. The simulation shows that when original ripples exist, laser intensity is focused above the grooves with polarization parallel to original ripples, which can erase the ripples. However, when the polarization is perpendicular to the existing ripples, laser intensity is almost confined in the grooves, which accelerates the formation of ripples.

Fast super-resolution imaging with ultra-high labeling density achieved by joint tagging super-resolution optical fluctuation imaging.

Previous stochastic localization-based super-resolution techniques are largely limited by the labeling density and the fidelity to the morphology of specimen. We report on an optical super-resolution imaging scheme implementing joint tagging using multiple fluorescent blinking dyes associated with super-resolution optical fluctuation imaging (JT-SOFI), achieving ultra-high labeling density super-resolution imaging. To demonstrate the feasibility of JT-SOFI, quantum dots with different emission spectra were jointly labeled to the tubulin in COS7 cells, creating ultra-high density labeling. After analyzing and combining the fluorescence intermittency images emanating from spectrally resolved quantum dots, the microtubule networks are capable of being investigated with high fidelity and remarkably enhanced contrast at sub-diffraction resolution. The spectral separation also significantly decreased the frame number required for SOFI, enabling fast super-resolution microscopy through simultaneous data acquisition. As the joint-tagging scheme can decrease the labeling density in each spectral channel, thereby bring it closer to single-molecule state, we can faithfully reconstruct the continuous microtubule structure with high resolution through collection of only 100 frames per channel. The improved continuity of the microtubule structure is quantitatively validated with image skeletonization, thus demonstrating the advantage of JT-SOFI over other localization-based super-resolution methods.