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To study the antifungal effect and mechanism of linalool against Fusarium oxysporum FOX-1, the causative agent of ginger Fusarium wilt, the damaging effects of different concentrations of linalool on the mycelial growth, spore germination and cell membrane of F. oxysporum FOX-1, and its control effect on ginger Fusarium wilt were systemically investigated. The results showed that linalool could significantly inhibit the mycelial growth of F. oxysporum FOX-1 in a concentration-dependent manner, and the half maximal effective concentration (EC50) and the minimum inhibitory concentration (MICs) were 1.183 and 2 g/L, respectively. The spore concentration of F. oxysporum FOX-1 treated with linalool at MIC for 6 h was (1.43 ± 0.12) × 104 spores/mL, which was significantly lower than that of the control group ((5.79 ± 0.19) × 104 spores/mL) (P < 0.05), and the spore germination rate of F. oxysporum FOX-1 treated with linalool at MIC for 12 h was (5.83 ± 2.16)%, which was significantly lower than that of the control group ((95.12 ± 3.78)%) (P < 0.05). Treatment with linalool at 1/2 MIC and MIC damaged the integrity of the cell membrane, resulting in a significant increase in relative electrical conductivity and nucleic acid leakage in the mycelial suspension and malondialdehyde (MDA) content in the mycelium, and a significant decrease in ergosterol content, finally weakening the pathogenicity of the fungus. No lesions appeared on ginger pieces after treatment with linalool at MIC for 3 days. In conclusion, linalool affects the normal physiological function of F. oxysporum FOX-1 by inhibiting its spore germination and mycelial growth and destroying its cellular structure. This study provides a new direction for the effective control of Fusarium wilt.

FER zeolites have a unique framework structure and acid distribution, and are widely studied as a catalyst for reactions such as n-butene skeletal isomerization and dimethyl ether carbonylation. The Brönsted acid site (BAS) located in the 10-member ring (10-MR) of FER zeolites serves as the active site for the isomerization reaction of skeletal n-butene to produce isobutene. This study prepared five types of FER zeolites using different methods: using pyrrolidine (PY) alone as a template; using Na-form FER as seeds (SN) or H-form FER as seeds without organic structure directing agents (OSDAs); and combining the seeds of SN or SH with PY as OSDAs. The differences in the structure and acid distribution of the five zeolites were investigated, as well as their catalytic performance for the skeletal isomerization of n-butene. Experiments and characterization results showed that under hydrothermal synthesis conditions, the FER-PY+SH zeolites synthesized by using both H-form zeolites seeds and pyridine exhibited the highest aluminum concentrations at T1 and T3 sites, along with the greatest BAS located in the 10-MR. This unique composition contributed to the highest selectivity of isobutene. The FER-PY+SH catalyst was continuously used for 720 h at 350 °C, 0.1 MPa, and an n-butene mass space velocity of 2.0 h−1 for three cycles of 2160 h. During this period, the conversion of n-butene was over 39%, while the selectivity of isobutene exceeded 95%. The FER-PY+SH catalyst exhibited excellent stability and activity. [ABSTRACT FROM AUTHOR]

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm's performance by optimizing the relevant parameter intervals. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD), which poses a significant risk to pine plantations across the globe, is caused by the pathogenic agent Bursaphelenchus xylophilus, also referred to as the pine wood nematode (PWN). A droplet digital PCR (ddPCR) assay was developed for the quick identification of the PWN in order to improve detection sensitivity. The research findings indicate that the ddPCR assay demonstrated significantly higher analysis sensitivity and detection sensitivity in comparison to traditional quantitative PCR (qPCR). However, it had a more limited dynamic range. High specificity was shown by both the ddPCR and qPCR techniques in the diagnosis of the PWN. Assessments of reproducibility revealed that ddPCR had lower coefficients of variation at every template concentration. Inhibition tests showed that ddPCR was less susceptible to inhibitors. There was a strong linear association between standard template measurements obtained using ddPCR and qPCR (Pearson correlation = 0.9317; p < 0.001). Likewise, there was strong agreement (Pearson correlation = 0.9348; p < 0.001) between ddPCR and qPCR measurements in the evaluation of pine wood samples. Additionally, wood samples from symptomatic (100% versus 86.67%) and asymptomatic (31.43% versus 2.9%) pine trees were diagnosed with greater detection rates using ddPCR. This study's conclusions highlight the advantages of the ddPCR assay over qPCR for the quantitative detection of the PWN. This method has a lot of potential for ecological research on PWD and use in quarantines. [ABSTRACT FROM AUTHOR]

Transformation of maleic anhydride (MA) into succinic anhydride (SA) through the development of high‐performance catalysts is an appealing approach from a sustainable development perspective. Herein, a core‐shell catalyst with silicate‐1 supported and MCM‐41 encapsulated nickel was prepared, which exhibited extraordinarily high MA conversion (99.8 %) and SA selectivity (99.9 %). Detailed structural characterizations and catalytic evaluation demonstrated that the core‐shell catalyst derived from the liquid deposition method is a hierarchically porous material with tunable shell thicknesses, possessing strong interaction between Ni particles and the supports. Key to this success is the MCM‐41 shell that covered the defects such as silanol nests on the surface of the silicate‐1 zeolite, diminishing both the surface and intra‐particle diffusion barriers, thus the specially‐designed catalyst achieved high activity under rather mild conditions (100 °C, 3.0 MPa), which is 59 % higher than the conventional silicate‐1 supported Ni catalyst. Furthermore, the mesoporous MCM‐41 shell plays a crucial role in minimizing the leaching and sintering of Ni particles, thereby bolstering catalytic stability. Overall, the research provides a new approach for the rational design to optimize the performance of hydrogenation catalysts and introduces a novel idea to enhance the activity and stability of Ni‐supported catalysts. [ABSTRACT FROM AUTHOR]

This paper proposed a two-dimensional steady-state field prediction approach that combines B-spline functions and a fully connected neural network. In this approach, field data, which are determined by corresponding control vectors, are fitted by a selected B-spline function set, yielding the corresponding best-fitting weight vectors, and then a fully connected neural network is trained using those weight vectors and control vectors. The trained neural network first predicts a weight vector using a given control vector, and then the corresponding field can be restored via the selected B-spline set. This method was applied to learn and predict two-dimensional steady advection–diffusion physical fields with absorption and source terms, and its accuracy and performance were tested and verified by a series of numerical experiments with different B-spline sets, boundary conditions, field gradients, and field states. The proposed method was finally compared with a generative adversarial network (GAN) and a physics-informed neural network (PINN). The results indicated that the B-spline neural network could predict the tested physical fields well; the overall error can be reduced by expanding the selected B-spline set. Compared with GAN and PINN, the proposed method also presented the advantages of a high prediction accuracy, less demand for training data, and high training efficiency. [ABSTRACT FROM AUTHOR]

CO2‐in‐water (C/W) foams have attracted much attention given their eco‐friendliness in recent years. However, limited surfactants were able to effectively adsorb at the supercritical CO2–water (C–W) interface considering the weak solvent strength of CO2, especially at high temperatures. Herein, attempts have been made to design suitable CO2 foaming agents by evaluating the influence of proportions of anionic and cationic surfactants and the affinity between aminopropyl methyl siloxane (APSi) and CO2. Through systematical foamability and foam stability experiments, together with the measurements of C–W equilibrium and dynamic interfacial tensions, the adsorption and stabilization mechanisms of sodium alpha olefin sulfonate (AOS)‐APSi aqueous dispersions on the CO2 foam films are revealed. Excellent foam properties were observed at AOS/APSi mass ratio of 9:1, in which initial foam height (h0) and half‐life period (t1/2) first increased and then decreased with increasing pressure, whereas the interfacial tension decreased with increasing pressure. The results indicate that the interaction of anionic and cationic head‐groups contributes to accelerating the surfactant adsorption rate from bulk to the C–W interface, enhancing foamability and stabilizing foam. Besides, the AOS/APSi mass ratio of 8:2 shows a good affinity for CO2 at 15 MPa, of which h0 is 26% higher and t1/2 is 60% slower than AOS alone. Furthermore, the initial mean bubble area of both 9:1 and 8:2 AOS/APSi mixtures was around half that of AOS alone. This work broadens the design of novel surfactant methodologies including CO2 foam, providing a theoretical guidance for the application of CO2 on enhanced‐oil‐recovery technologies. [ABSTRACT FROM AUTHOR]

A series of quaternary ammonium or phosphonium salts were applied as zeolite growth modifiers in the synthesis of hierarchical ZSM-5 zeolite. The results showed that the use of methyltriphenylphosphonium bromide (MTBBP) could yield nano-sized hierarchical ZSM-5 zeolite with a "rice crust" morphology feature, which demonstrates a better catalytic performance than other disinfect candidates. It was confirmed that the addition of MTBBP did not cause discernable adverse effects on the microstructures or acidities of ZSM-5, but it led to the creation of abundant meso- to marco- pores as a result of aligned tiny particle aggregations. Moreover, the generation of the special morphology was believed to be a result of the coordination and competition between MTBBP and Na+ cations. The as-synthesized hierarchical zeolite was loaded with Zn and utilized in the propane aromatization reaction, which displayed a prolonged lifetime (1430 min vs. 290 min compared with conventional ZSM-5) and an enhanced total turnover number that is four folds of the traditional one, owing to the attenuated hydride transfer reaction and slow coking rate. This work provides a new method to alter the morphological properties of zeolites with low-cost disinfectants, which is of great potential for industrial applications. [ABSTRACT FROM AUTHOR]

Utilizing carbon dioxide as a carbon source for the synthesis of olefins and aromatics has emerged as one of the most practical methods for CO2 reduction. In this study, an improved selectivity of 85% for targeting products (ethylbenzene and propylbenzene) is achieved with a benzene conversion of 16.8% by coupling the hydrogenation of carbon dioxide to olefins over the bifunctional catalyst "Oxide-Zeolite" (OX-ZEO) and the alkylation of benzene with olefins over ZSM-5. In addition to investigating the influence of SAPO-34 and ZSM-5 zeolite acidity on product distribution, catalyst deactivation due to coke formation is addressed by modifying both molecular sieves to be hierarchical to extend the catalyst lifespan. Even after 100 h of operation at 400 °C, the catalysts maintained over 80% selectivity towards the target products, with benzene conversion over 14.2%. Furthermore, the pathway of propylbenzene formation is demonstrated through simple experimental design, revealing that the surface Brønsted acid sites of SAPO-34 serve as its primary formation sites. This provides a novel perspective for further investigation of the reaction network. [ABSTRACT FROM AUTHOR]