6 ‐ Chemo‐catalytic conversion of lignin

This chapter explains catalytic chemical methods of lignin transformation which include acidcatalyzed, base‐catalyzed and metal‐catalyzed methods. The chapter starts with a brief discussion of the lignin architecture as a background to understanding the subsequent subject of lignin’s chemocatalytic depolymerization methods. The general idea that lignin depolymerization involves the cleavage of the linkages holding its basic aromatic‐based monomers is discussed. Under each of the catalytic methods, the concept is first explained starting with the types of acid, base, or metal (as the case may be) employed, followed by the reaction conditions and then the reaction mechanism involved in method under discussion. Current research efforts are incorporated into each of the sections, as obtained from recent reviews. Under the metal‐catalyzed method, reductive and oxidative lignin depolymerization methods are explained. Extant challenges faced by lignin researchers are mentioned in the texts. A typical laboratory procedure for lignin depolymerization using the stainless steel autoclave is described, and the chapter ends with the future of lignin as a viable energy source and substitute for petroleum in the manufacture of chemicals and fuels

Palladium Cobalt-nickel mixed oxides Surface modification Synergistic interaction Lean methane combustion

The effective transformation of lignin is an essential part of realizing the comprehensive utilization of biomass. In this study, a one-pot method for the depolymerization of corn stover lignin used aluminum phosphate (NiAPO-5) zeolite catalyst contained Brønsted acid, Lewis acid and hydrogenation sites was proposed. It was found that the number of Brønsted acid sites was increased after NiAPO-5 was reduced with H2. The yield of monomers and residue were 35.70% and 38.09% at 235 ◦C for 3 h, respectively. The result of 2D HSQC NMR showed that the NiAPO-5 (H2) catalyst significantly affected the cleavage of β-O-4 bonds. The distribution of products and the stability of catalyst revealed that NiAPO-5 (H2) was an efficient catalyst for the depolymerization of lignin

Highly Active and Stable Palladium Catalysts Supported on Surfacemodified Ceria Nanowires for Lean Methane Combustion

An efficient strategy was presented to synthesize highly active palladium catalyst supported on ceria nanowires modified by organosilanes (abbreviated as Pd/CeO2NWs@SiO2) for lean methane combustion. It is found that such a surface-modified strategy can significantly improve the dispersion of surface palladium species and strengthen the concentration of active surfaceadsorbed oxygen species via reconstructing the surface microenvironment, invoking an efficient performance for methane oxidation. Under the space velocity of 60,000 mLg−1h−1, 0.5 wt% Pd/CeO2NWs@SiO2 displayed extraordinary catalytic activity with 90 % conversion rate at a temperature of around 327 °C, far lower than that of pristine Pd/CeO2NWs (378 °C) under the same conditions. What's more, unexpected stability was observed under high temperature and the presence of water vapor conditions owing to the intense metal support interaction of Pd/CeO2NWs@SiO2 catalyst. The possible reaction mechanism of lean methane oxidation was probed by in situ DRIFT spectra. It is observed that the pivotal intermediate products (carbonate and carbon oxygenates) generated on Pd/CeO2NWs@SiO2 surface are more readily decomposed into CO2. Importantly, the silicon hydroxyl groups (Si−OH) formed during the reaction can efficiently restrict the generation of the stable Pd(OH)x phase and release more active sites to facilitate the catalytic performance. This study provides a convenient method to design the highly reactive and durable palladium-based catalyst for methane combustion

Use of CeO2 Nanoparticles to Enhance UV-Shielding of Transparent Regenerated Cellulose Films

The major challenge in preparing polymer nanocomposites is to prevent the agglomeration of inorganic nanoparticles (NPs). Here, with regenerated cellulose (RC) films as supporting medium, UV-shielding and transparent nanocomposite films with hydrophobicity were fabricated by in situ synthesis of CeO2 NPs. Facilitated through the interaction between organic and inorganic components revealed by X-ray diffraction (XRD) and Fourier transformation infrared spectroscopy (FTIR) characterization, it was found that CeO2 NPs were uniformly dispersed in and immobilized by a cellulose matrix. However some agglomeration of CeO2 NPs occurred at higher precursor concentrations. These results suggest that the morphology and particle size of CeO2 and the corresponding performance of the resulting films are affected by the porous RC films and the concentrations of Ce(NO3)3·6H2O solutions. The optimized nanocomposite film containing 2.95 wt% CeO2 NPs had more than 75% light transmittance (550 nm), high UV shielding properties, and a certain hydrophobicity

A New Method for Extracting Laver Culture Carriers Based on Inaccurate Supervised Classification with FCN-CRF

Timely monitoring of marine aquaculture has considerable significance for marine ecological protection and maritime safety and security. Considering that supervised learning needs to rely on a large number of training samples and the characteristics of intensive and regular distribution of the laver aquaculture zone, in this paper, an inaccurate supervised classification model based on fully convolutional neural network and conditional random filed (FCN-CRF) is designed for the study of a laver aquaculture zone in Lianyungang, Jiangsu Province. The proposed model can extract the aquaculture zone and calculate the area and quantity of laver aquaculture net simultaneously. The FCN is used to extract the laver aquaculture zone by roughly making the training label. Then, the CRF is used to extract the isolated laver aquaculture net with high precision. The results show that the k a p p a coefficient of the proposed model is 0.984, the F 1 is 0.99, and the recognition effect is outstanding. For label production, the fault tolerance rate is high and does not affect the final classification accuracy, thereby saving more label production time. The findings provide a data basis for future aquaculture yield estimation and offshore resource planning as well as technical support for marine ecological supervision and marine traffic management

Catalytic conversion of cellulose to C-5/C-6 alkanes over Ir-VOx/SO2 combined with HZSM-5 in n-dodecane/water system

The liquid fuel (made of C-5/C-6 alkanes) was obtained directly from the hydrogenolysis of microcrystalline cellulose (MCC) with Ir-VOx/SiO2 combined with HZSM-5 as the composite catalyst in a biphasic system (n-dodecane + H2O). The performance of the catalyst was investigated by carrying out a series of experiments using various V/Ir molar ratios, catalyst dosages, reaction temperatures, reaction time, hydrogen pressure and substrates. At the optimized conditions, the cellulose was almost completely converted, and at the same time, a high C-5/C-6 yield of 85.1% was obtained at 210 degrees C for 24 h and 6 MPa with the V/Ir molar ratio being 0.13. These results not only proved that Ir-VOx/SiO2 (V/Ir = 0.13) has excellent performance for the hydrogenolysis of MCC to liquid alkanes, but also indicated that vanadium is a good metal promoter for iridium. In addition, it was proven the C-5/C-6 alkanes were obtained via sorbitol through the combined effect of Ir-VOx/SiO2 and HZSM-5

Catalytic depolymerization of Kraft lignin to produce liquid fuels via Ni-Sn metal oxide catalysts

In this study, Ni-Sn metal oxide catalysts, with strong Lewis acidity, were prepared and applied in lignin depolymerization to produce liquefied fuels. The Ni-Sn metal oxide catalysts could cleave the lignin linkages and stabilize the reaction intermediates due to the high Lewis acidity and the hydrogenation of nickel sites. When the molar ratio of nickel to tin was 1 : 3, a liquid product yield of 90% and a petroleum ether soluble product (mainly monomers and dimer degradation products) yield of 60% were obtained at 310 degrees C for 24 h. Under these reaction conditions, the petroleum ether soluble product had a higher heating value (HHV) (36.45 MJ kg(-1)) than Kraft lignin (25.83 MJ kg(-1)). A meticulous study on Ni-Sn metal oxide catalysts revealed that Lewis acidity and the synergistic effect between Ni and Sn played an important role in lignin depolymerization

An Innovative Approach for Effective Removal of Thin Clouds in Optical Images Using Convolutional Matting Model

Clouds are the major source of clutter in optical remote sensing (RS) images. Approximately 60% of the Earth’s surface is covered by clouds, with the equatorial and Tibetan Plateau regions being the most affected. Although the implementation of techniques for cloud removal can significantly improve the efficiency of remote sensing imagery, its use is severely restricted due to the poor timeliness of time-series cloud removal techniques and the distortion-prone nature of single-frame cloud removal techniques. To thoroughly remove thin clouds from remote sensing imagery, we propose the Saliency Cloud Matting Convolutional Neural Network (SCM-CNN) from an image fusion perspective. This network can automatically balance multiple loss functions, extract the cloud opacity and cloud top reflectance intensity from cloudy remote sensing images, and recover ground surface information under thin cloud cover through inverse operations. The SCM-CNN was trained on simulated samples and validated on both simulated samples and Sentinel-2 images, achieving average peak signal-to-noise ratios (PSNRs) of 30.04 and 25.32, respectively. Comparative studies demonstrate that the SCM-CNN model is more effective in performing cloud removal on individual remote sensing images, is robust, and can recover ground surface information under thin cloud cover without compromising the original image. The method proposed in this article can be widely promoted in regions with year-round cloud cover, providing data support for geological hazard, vegetation, and frozen area studies, among others

An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy:a development and validation study

Aims/hypothesis Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics. Methods Our deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy. Results The intraclass correlation coefficients for our algorithm were superior to those for ACCMetrics for total corneal nerve fibre length (0.933 vs 0.825), mean length per segment (0.656 vs 0.325), number of branch points (0.891 vs 0.570), number of tail points (0.623 vs 0.257), number of nerve segments (0.878 vs 0.504) and fractals (0.927 vs 0.758). In addition, our proposed algorithm achieved an AUC of 0.83, specificity of 0.87 and sensitivity of 0.68 for the classification of participants without (n = 90) and with (n = 132) neuropathy (defined by the Toronto criteria). Conclusions/interpretation These results demonstrated that our deep learning algorithm provides rapid and excellent localisation performance for the quantification of corneal nerve biomarkers. This model has potential for adoption into clinical screening programmes for diabetic neuropathy. Data availability The publicly shared cornea nerve dataset (dataset 1) is available at http://bioimlab.dei.unipd.it/Corneal% 20Nerve%20Tortuosity%20Data%20Set.htm and http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Data%20Set.htm