Multi-channel broadband optical chaos generation assisted by phase modulation and CFBG feedback.

In this paper, we propose a novel and simple multi-channel broadband optical chaos generation scheme based on phase modulation and chirped fiber Bragg grating (CFBG). Firstly, phase modulation is introduced to generate more new frequency components to broaden the spectrum of the phase chaos. Meanwhile, the accumulated dispersion from CFBG distorts the intensity chaos, converts phase chaos to intensity chaos, and weakens the laser relaxation oscillation. This process would lead to energy redistribution in the power spectrum, effectively increasing the chaotic bandwidth. Then, the wavelength detuning between CFBG and the semiconductor laser is introduced to enhance the chaotic bandwidth further. The experiment results show that the 10 dB bandwidths of the five channels are up to 31.0 GHz, 34.3 GHz, 36.3 GHz, 40 GHz, and 40 GHz, respectively. Note that the maximum bandwidth of the PD in our experiment is limited to 40 GHz. In addition, the multi-channel chaotic signals obtained from the experiment system are used to generate multi-channel physical random numbers. After the post-processing operations, the total rate of five parallel high-speed physical random number generation channels is 4.64 Tbit/s (160 GSa/s × 5bit × 1 channel + 160 GSa/s × 6bit × 4 channels). As far as we know, this is the highest record of using external cavity feedback semiconductor lasers to generate random numbers, which has great potential to meet the security requirements of next-generation Tbit/s optical communication systems.

Application of protein engineering to ene-reductase for the synthesis of chiral compounds through asymmetric reaction.

Ene-reductase (ER) has been widely applied for asymmetrical synthesis of chiral intermediates due to its substrate promiscuity, photoexcited reactivity, and excellent property with producing two chiral centers at a time. Natural ERs often exhibit the same stereoselectivity, and they need to be engineered for opposite configuration of chiral compounds. The hydrogenation process toward activated alkenes by ERs is composed of reductive half reaction and oxidative half reaction, which are dependent upon two cofactors NAD(P)H and flavin mononucleotide. The catalytic activity of ERs will be affected by the size of the substrate, the activating strength of the electron-withdrawing groups, redox potential of cofactors, and the loop flexibility around catalytic cavity. Currently, protein engineering to ERs has been successfully employed to enhance various catalytic properties, including photoexcited asymmetric synthesis. This review summarizes the approaches to reverse the stereoselectivity and enhance catalytic activity of ERs and new applications of the engineered ERs in photobiocatalytic asymmetric synthesis, besides the discussion with the existing molecular mechanisms of mutants regarding the improved catalytic performance.

Alkalinity control in sludge propels the conversion of concrete slurry waste into micro- and nano-sized biogenic CaCO3.

The utilization of Bacillus sp. for the production of bio-CaCO3 in concrete crack repair and strength enhancement has attracted considerable attention. However, microbial-induced calcium carbonate precipitation (MICP) has yet to be explored as a precedent with activated sludge. Here calcium sourced from concrete slurry waste (CSW) and carbon from sludge microbial ß-oxidation under alkaline were used to generate micro/nano CaCO3. The results indicate that the main crystalline form of the generated precipitated particles is calcite, with a particle size ranging from 0.7 to 10 µm. Minimal heavy metals were found in the supernatant following settling. And at the optimum pH of 8.5-9, carbon capture reached 743 mg L-1, and CaCO3 production reached 1,191 mg L-1, and dominant phylum were Proteobacteria and Bacteroidota, with Thauera being a prevalent genus adept in ß-oxidation. Mass balance analysis showed that alkali promotes microbial ß-oxidation of organisms to produce CO2 and facilitate storage. Thus, the alkaline regulation of metabolism between microbe and CSW provides a novel way of sludge to initiate MICP.

Enhancing the Enantioselectivity and Catalytic Efficiency of Esterase from Bacillus subtilis for Kinetic Resolution of l-Menthol through Semirational Design.

Enzymatic kinetic resolution is a promising way to produce l-menthol. However, the properties of the reported biocatalysts are still unsatisfactory and far from being ready for industrial application. Herein, a para-nitrobenzylesterase (pnbA) gene from Bacillus subtilis was cloned and expressed to produce l-menthol from d,l-menthyl acetate. The highest enantiomeric excess (ee) value of the product generated by pnbA was only approximately 80%, with a high conversion rate (47.8%) of d,l-menthyl acetate with the help of a cosolvent, indicating high catalytic activity but low enantioselectivity (E = 19.95). To enhance the enantioselectivity and catalytic efficiency of pnbA to d,l-menthyl acetate in an organic solvent-free system, site-directed mutagenesis was performed based on the results of molecular docking. The F314E/F315T mutant showed the best catalytic properties (E = 36.25) for d,l-menthyl acetate, with 92.11% ee and 30.58% conversion of d,l-menthyl acetate. To further improve the properties of pnbA, additional mutants were constructed based on the structure-guided triple-code saturation mutagenesis strategy. Finally, four mutants were screened for the best enantioselectivity (ee > 99%, E > 300) and catalytic efficiency at a high substrate concentration (200 g/L) without a cosolvent. This work provides several generally applicable biocatalysts for the industrial production of l-menthol.

Research on Friction Performance of Friction Stir Welding Tools Based on Non-Smooth Structure.

In this study, based on the principles of bionics, we fabricated a bionic non-smooth concave pit structure on the shoulders of friction stir welding tools and detected the thermal cycling curve, downforce, and torque of the tool in the welding process. We tested the wear loss weight and analyzed the surface morphology of the shoulder surfaces after welding for 200 m. This study found that as the distance between the concave pits decreased and the number of concave pits increased, the maximum downforce, torque, and temperature in the welding process showed a decreasing trend. As the speed increased, no matter how the tool structure changed, the downforce and torque decreased, while the peak thermal cycle temperature increased. The experimental welding results show that the wear loss weight of the non-smooth structure tool significantly reduced. The lowest wear loss weight of the tool with a concave pit interval of 1.125 mm was only 0.1529 g, which is 27% lower than that of the conventional tool. Our observations of the surface morphology of the tool shoulder after welding showed that the amount of aluminum swarf on the tool shoulder of the welding tool gradually declined with the increasing density of the uneven pits. The lowest number of aluminum chips adhered to a welding tool with a pit distance of 1.125 mm. Therefore, friction stir welding tools with biomimetic structures have better wear resistance and adhesion resistance.

Can three-dimensional nitrate structure be reconstructed from surface information with artificial intelligence? - A proof-of-concept study.

Nitrate is one of the essential variables in the ocean that is a primary control of the upper ocean pelagic ecosystem. Its three-dimensional (3D) structure is vital for understanding the dynamic and ecosystem. Although several gridded nitrate products exist, the possibility of reconstructing the 3D structure of nitrate from surface data has never been exploited. In this study, we employed two advanced artificial intelligence (AI) networks, U-net and Earthformer, to reconstruct nitrate concentration in the Indian Ocean from surface data. Simulation from an ecosystem model was utilized as the labeling data to train and test the AI networks, with wind vectors, wind stress, sea surface temperature, sea surface chlorophyll-a, solar radiation, and precipitation as the input. We compared the performance of two networks and different pre-processing methods. With the input features decomposed into climatology and anomaly components, the Earthformer achieved optimal reconstruction results with a lower normalized mean square error (NRMSE = 0.1591), spatially and temporally, outperforming U-net (NRMSE = 0.2007) and the climatology prediction (NRMSE = 0.2089). Furthermore, Earthformer was more capable of identifying interannual nitrate anomalies. With a network interpretation technique, we quantified the spatio-temporal importance of every input feature in the best case (Earthformer with decomposed inputs). The influence of different input features on nitrate concentration in the adjacent Java Sea exhibited seasonal variation, stronger than the interannual one. The feature importance highlighted the role of dynamic factors, particularly the wind, matching our understanding of the dynamic controls of the ecosystem. Our reconstruction and network interpretation technique can be extended to other ecosystem variables, providing new possibilities in studies of marine environment and ecology from an AI perspective.