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Abstract Photothermal catalysis is a promising strategy to combine the advantages of both thermal-catalysis and photocatalysis. Herein we achieve the protolignin conversion to aromatics via the photothermal catalytic transfer hydrogenolysis process intensified by the in-situ protection strategy. The Pd/TiO2 at 140 °C with UV irradiation can catalyze the reforming of primary alcohols to aldehydes and active H* species, which further participate in the acetalation protection of the 1,3-diol group of β-O-4 linkage and mediate the hydrogenolysis of Cβ–OAr bonds, respectively. The conversion of birch sawdust with ethanol as the hydrogen donor provides a 40 wt% yield of phenolic monomers, compared with an 11 wt% monomer yield obtained from the conversion of extracted 1,3-diol-protected lignin under the same conditions. The synergistic effect of photocatalysis and thermal-catalysis contributes to the prior cleavage of the Cβ–OAr bond before other C–O bonds. The feasibility of solar-light-driven photothermal catalytic system is demonstrated.

Abstract Intrusion detection is a critical obstacle in the realm of security and data mining methodologies. Consequently, researchers have extensively investigated the quest for the swiftest and most precise means of identifying intrusions. Essentially, intrusion detection systems are tasked with recognizing any unauthorized activities, misuse, or harm inflicted upon a system, be it by internal or external users. Recently, in order to design intrusion detection systems, artificial intelligence and machine learning methods have been used, each of which has its own characteristics and advantages. Accordingly, this article focuses on using machine learning to improve the accuracy of the intrusion detection process. In fact, by using machine learning, trends, and patterns can be easily identified and thus used in a network environment to detect intrusion. It can be very useful. For this purpose, we utilize Radial Basis Function (RBF) neural networks and support vector machine (SVM) algorithm to improve decision-making and intrusion detection. By employing RBF neural networks, important features of the data are extracted, which in turnPlease check if the author details and affiliations are presented correctly. Kindly amend if necessary. enhance the overall performance of the solution and the efficiency of the SVM algorithm. This is because feature reduction ultimately leads to improved effectiveness of the SVM algorithm. In methods lacking this capability, the learning algorithm is compelled to utilize features that have no specific correlation with intrusion and essentially do not contribute to identifying attacks. Such learning approaches essentially learn from noisy data, which negatively impacts the intrusion detection solution. Finally, the proposed solution was evaluated using Python programming language and KDD99 data set. The results of the evaluation indicate that the proposed solution has a higher accuracy and precision than other evaluated solutions. So, the accuracy is 97, and the precision is over 99%.

Human hair fibers are structurally organized as a cortex protected by an outer scaly sheath (cuticle). Developing methods to isolate these two compartments will help to elucidate their roles in hair biology and develop potential applications. Herein, we developed enzymatic methods for human hair cuticle cell harvesting or removal. After a single treatment with esperase for three days, cuticle cells were released from hair shafts and effectively harvested by a series of filtration, centrifugation, and resuspension steps. Separately, combining L-cysteine and either esperase or savinase could remove the entire cuticle layer to obtain descaled hair fibers. The physical and chemical structures, and performance of the isolated cuticle cells and descaled hair samples were characterized using electron microscopy, FTIR, solid state 13C NMR, TGA, and DSC. Results show that the harvested cuticle cells contain mainly β-sheets and random coil structures, while descaled hair samples contain more α-helical structures. Harvested cuticle cells have greater thermal stability than the descaled hair samples below 260°C. The enzymatic methods developed in this study are convenient and easily controlled. They are effective not just for cuticle cell harvesting but also for surface modification and structure disintegration of hair fibers.

To reduce greenhouse gas (GHG) emissions, biomass has been increasingly developed as a renewable and clean alternative to fossil fuels because of its carbon-neutral characteristics. China has been investigating the rational development and use of bioenergy for developing its clean energy and achieving carbon neutrality. Substituting fossil fuels with multi-source and multi-approach utilized bioenergy and corresponding carbon reduction in China remain largely unexplored. Here, a comprehensive bioenergy accounting model with a multi-dimensional analysis was developed by combining spatial, life cycle, and multi-path analyses. Accordingly, the bioenergy production potential and GHG emission reduction for each distinct type of biomass feedstock through different conversion pathways were estimated. The sum of all available organic waste (21.55 EJ yr−1) and energy plants on marginal land (11.77 EJ yr−1) in China produced 23.30 EJ of bioenergy and reduced 2,535.32 Mt CO2-eq emissions, accounting for 19.48% and 25.61% of China’s total energy production and carbon emissions in 2020, respectively. When focusing on the carbon emission mitigation potential of substituting bioenergy for conventional counterparts, bioelectricity was the most effective, and its potential was 4.45 and 8.58 times higher than that of gaseous and liquid fuel alternatives, respectively. In this study, life cycle emission reductions were maximized by a mix of bioenergy end uses based on biomass properties, with an optimal 78.56% bioenergy allocation from biodiesel, densified solid biofuel, biohydrogen, and biochar. The main regional bioenergy GHG mitigation focused on the Jiangsu, Sichuan, Guangxi, Henan, and Guangdong provinces, contributing to 31.32% of the total GHG mitigation potential. This study provides valuable guidance on exploiting untapped biomass resources in China to secure carbon neutrality by 2060.

Genomic analysis has demonstrated that many fungi possess essential gene clusters for the production of previously unobserved secondary metabolites; however, these genes are normally reduced or silenced under most conditions. These cryptic biosynthetic gene clusters have become treasures of new bioactive secondary metabolites. The induction of these biosynthetic gene clusters under stress or special conditions can improve the titers of known compounds or the production of novel compounds. Among the inducing strategies, chemical-epigenetic regulation is considered a powerful approach, and it uses small-molecule epigenetic modifiers, which mainly act as the inhibitors of DNA methyltransferase, histone deacetylase, and histone acetyltransferase, to promote changes in the structure of DNA, histones, and proteasomes and to further activate cryptic biosynthetic gene clusters for the production of a wide variety of bioactive secondary metabolites. These epigenetic modifiers mainly include 5-azacytidine, suberoylanilide hydroxamic acid, suberoyl bishydroxamic acid, sodium butyrate, and nicotinamide. This review gives an overview on the method of chemical epigenetic modifiers to trigger silent or low-expressed biosynthetic pathways to yield bioactive natural products through external cues of fungi, mainly based on the research progress in the period from 2007 to 2022. The production of about 540 fungal secondary metabolites was found to be induced or enhanced by chemical epigenetic modifiers. Some of them exhibited significant biological activities such as cytotoxic, antimicrobial, anti-inflammatory, and antioxidant activity.