The effect of adding protein on the decomposition behavior of lignin in Japanese cedar under supercritical methanol conditions (270 °C/27 MPa) was studied. The Klason method was used to detect the lignin content in the insoluble residue following to a 30 min treatment. Adding either an animal (bovine serum albumin) or plant (soy) protein enhanced delignification from 50 to 65% of the lignin-based wt %. This result was attributed to enhanced lignin depolymerization owing to inhibited lignin recondensation and/or the suppressed formation of polysaccharide-derived char via reactions between the protein and polysaccharides. Although the solubilization of lignin was promoted and the yield of lignin-derived low-molecular-weight compounds increased, the selectivity of major monomers such as coniferyl alcohol (CA) and γ-methylated CA decreased. The addition of proteins has a substantial impact on the decomposition behavior of cell wall components under supercritical methanol conditions. This information provides insights into the use of protein-rich lignocelluloses.
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http://dx.doi.org/10.1021/acsomega.2c03716 | DOI Listing |
ACS Nano
January 2025
Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou 215123, China.
The widespread application of anode-free lithium metal batteries (AFLMBs) is hindered by the severe dendrite growth and side reactions due to the poor reversibility of Li plating/stripping. Herein, our study introduces an ultrathin interphase layer of covalent cage 3 (CC3) for highly reversible AFLMBs. The subnano triangular windows in CC3 serve as a Li sieve to accelerate Li desolvation and transport kinetics, inhibit electrolyte decomposition, and form LiF- and LiN-rich solid-electrolyte interphases.
View Article and Find Full Text PDFWaste Manag
January 2025
School of Metallurgy and Environment, Central South University, Changsha 410083 China; State Key Laboratory of Advanced Metallurgy for Non-ferrous Metals, Changsha 410083 China; Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha 410083 China. Electronic address:
At present, lead-containing wastes have increasingly become the raw materials together with primary lead concentrate for lead production to meet the ever-increasing lead demand market. PbSO is the dominant component in the lead-containing wastes, nevertheless, its reaction behavior during lead smelting is not sufficiently investigated. This study investigated PbSO decomposition behaviors and phase transformation mechanisms at oxidizing and reductive atmospheres and various gas flow rates.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
Nanoporous metals, a class of free-standing, high specific-area materials, evolve from interface-controlled self-organization in a selective dissolution (e.g., dealloying).
View Article and Find Full Text PDFFront Oncol
December 2024
Center of Burns and Plastic Surgery and Dermatology, The 924th Hospital of Joint Logistics Support Force of the People's Liberation Army (PLA), Guilin, China.
Background: Cutaneous Malignant Melanoma (CMM) is a significant global health challenge. Understanding regional differences in CMM prevalence and trends is crucial for developing targeted strategies. To address this, we analyzed epidemiological patterns and investigated risk factors for CMM-related mortality.
View Article and Find Full Text PDFSci Rep
January 2025
State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 South Beijing Road, Urumqi, 830011, China.
Litterfall load is crucial in maintaining ecosystem health, controlling wildfires, and estimating carbon stock in arid regions. However, there is a lack of spatiotemporal analysis of litterfall in arid riparian forests. This study aims to estimate Litterfall load using a BP neural network based on vegetation indices from Landsat 5 and 8 satellite images, litterfall inventory data, slope, and distance to major river tributaries.
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