工业工程最新文献

Hydrophobicity enhancement of metal-free leather, which is crucial for improving its comprehensive performance, can be achieved by using amphiphilic copolymer retanning agents. However, the relationship between the sequential structure and the hydrophobic modification effect of amphiphilic copolymers remains unclear. Herein, an amphiphilic block copolymer was synthesized using stearyl methacrylate and 2-(dimethylamino)ethyl methacrylate via atom transfer radical polymerization, and the corresponding random copolymer with similar monomer compositions and molecular weights was prepared for comparison. The aggregation behavior of block and random copolymers was investigated. DLS and TEM results indicate that the block copolymer exhibits a larger aggregate size than the corresponding random copolymer. Molecular dynamics simulations suggest that the block copolymer aggregate exhibit a thicker hydrophilic shell and more concentrated distribution of cationic DMA block than the random copolymer aggregate. Subsequently, the block and random copolymers were used for the hydrophobic modification of metal-free tanned collagen fibers (CFs). The block copolymer shows superior binding capacity to CFs than the random one because of its larger size and more concentrated charge distribution. Hence, the block copolymer can form a dense and uniform hydrophobic film on the surface of collagen fibrils and endow CFs with higher hydrophobicity than the random one. This work provides theoretical guidance for modulating the hydrophobicity of CFs by tailoring the sequential structure of amphiphilic copolymers, which is expected to inspire the manufacturing of high-performance metal-free leather.

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Silicate bioceramics have demonstrated great potential in hydrogel dressings for wound healing due to their special origins of promoting endothelial cell angiogenesis and inhibiting apoptosis of cardiomyocyte. However, there are still some deficiencies, such as insufficient biological activity, instability of silicate ion release, and lower wet adhesion on wounds with tissue exudate, limiting their further clinical applications. Herein, inspired by mussels, a multifunctional double-network hydrogel (FS/PAM-Gel-PDA) wound dressing composited gelatin with silicate ceramic powder with satisfactory wet adhesion, stable release of bioactive ions, hemostasis, and the ability of promoting vascular regeneration was engineered through specifically grafting dopamine to gelatin and introducing ferrous silicate ceramic powder into the hydrogel. The comprehensive experimental results substantiate that the FS/PAM-Gel-PDA has wet-adhesion strength of up to 21.78 kPa, and remains stably adherent to porcine myocardial tissues intuitively after bending, twisting, soaking in water, and stretching. The test results of ion release behavior in vitro show that the oxidation and agglomeration of ferrous silicate ceramic powder can be effectively inhibited by using dopamine to form an antioxidant layer on the surface of ceramic powder, and thus, the stable release of Fe²⁺ and SiO₄⁴⁻ effective ions can be realized. The animal experiment exhibits that FS/PAM-Gel-PDA can achieve rapid hemostasis in the lethal liver defect model. Meanwhile, the FS/PAM-Gel-PDA reveals the remarkable ability to promote wound healing in a full-thickness skin injury model, which can obviously accelerate skin re-epithelialization. To sum up, the FS/PAM-Gel-PDA has excellent wet adhesion and stable release of active ions to accelerate angiogenesis, which shows great potential in promoting wound healing.

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It is essential to investigate the influence of alkaline earth metals on the pyrolysis mechanism and resulting products of lignin to enhance the efficient thermochemical conversion and utilization of lignin or biomass. In this study, the density functional theory method was used to simulate the pyrolytic reaction pathways of a β-O-4 type lignin dimer model compound (1-methoxy-2-(4-methoxyphenethoxy)benzene, mc) affected by alkaline earth metal ions Ca²⁺ and Mg²⁺. The computational findings suggest that Ca²⁺ and Mg²⁺ tend to combine with the oxygen atom at the C_β position and the oxygen atom on the methoxy group of the lignin dimer model compound, forming stable complexes that modify the bond lengths of the C_α–C_β and C_β–O bonds and affect their pyrolysis energy barriers. During the catalytic pyrolysis process, the presence of Ca²⁺ and Mg²⁺ can promote the concerted decomposition reaction, leading to increased production of products like 1-methoxy-4-vinylbenzene, 2-methoxyphenol and catechol. Meanwhile, they can suppress homolytic cleavage reactions of the C_β–O and C_α–C_β bonds, thereby hindering the formation of other products such as 1-ethyl-4-methoxybenzene and 2-hydroxybenzaldehyde.

Pyrolysis, an economically viable method, thermochemically converts solid fuel into transportation fuels and value-added chemicals, such as clean gas, liquid fuels, and chemicals, alongside undesirable by-products. Photoionization mass spectrometry (PIMS) is a versatile technique for real-time process analysis, offering ‘soft’ ionization for complex analytes, detecting and analyzing ions during in-situ pyrolysis. This review focuses on recent applications of PIMS during pyrolysis of solid fuels (i.e. coal, biomass and energetic materials). It summarizes studies on mass spectrometric analysis combined with different reactors and highlights the benefits through online PIMS as a diagnostic tool for in-situ analysis. It provides an overview of interplay between experimental advancements and models and discusses future perspectives, potential applications in support of mechanistic studies.

Chemical looping oxidative dehydrogenation (CL-ODH) provides a multifunctional conversion platform that can take advantage of the selective oxidation of lattice oxygen in oxygen carrier to achieve high-valued ethane to ethylene conversion. In this study, we explored the effect of B-site element in MgX₂O₄ (X=Cr, Fe, or Mn) spinel-type oxygen carriers on the performance of ethane CL-ODH. The properties test and characterization of MgX₂O₄ spinel were tested by fixed bed and H₂-TPR, O₂-TPD, TG, in-situ Raman, SEM, and TEM. The results showed that because MgCr₂O₄ only released a small amount of adsorbed surface oxygen, it tended to catalyze the conversion of ethane to coke and hydrogen. MgFe₂O₄ facilitated the deep oxidation of ethane into CO₂ by providing more surface lattice oxygen. Meanwhile, since a significant amount of bulk lattice oxygen was released by the MgMn₂O₄ oxygen carrier, it could burn hydrogen in a targeted manner to advance the reaction and increased ethylene’s selectivity. Thereby, MgMn₂O₄ achieved an ethane conversion of 73.72% with an ethylene selectivity of 81.46%. Furthermore, the MgMn₂O₄ catalyst demonstrated stable reactivity and an ethylene yield of about 62.00% in ethane CL-ODH over the 30 redox cycles. The screening tests indicated that the B-site elements in MgX₂O₄ spinel oxides could significantly influence their ability to supply lattice oxygen, thereby affecting their performance in ethane CL-ODH reaction.

Acellular dermal matrix (ADM) is one of the most promising scaffold materials due to its ability to retain natural extracellular matrix structure. Micronized acellular dermal matrix (mADM) was prepared with no intact cell nuclei and preserved growth factors by High Hydrostatic Pressure (HHP) approach. And mADM-collagen wound dressings were developed with different proportion of type I collagen and recombinant humanized type III collagen. The porous structure of the mADM-collagen wound dressings made them a good candidate for preventing excessive fluid accumulation, while the collagens with gel-like texture combined with mADM powder to form pasty texture wound dressing, which preserving the moisture at the wound site. Moreover, the paste texture of the mADM-collagen wound dressing was easy to reshape to conform any wound shapes and body contours. Furthermore, the resulted mADM-collagen wound dressings showed good biocompatibility by supporting fibroblasts adhesion and proliferation in vitro. Subsequently, a murine model of full-thickness skin wounds was employed to assess its effects on wound healing. Notably, mADM-75% Col-I exhibited superior effects throughout the wound healing process, specifically it promoted neovascularization, skin appendage growth and new skin regeneration. This formulation closely mimicked the collagen ratio found in healthy skin, facilitating the favorable wound repair. These results indicated the superior performance of this mADM-collagen wound dressing providing an optimal environment for wound healing.

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Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos. This technology plays a crucial role in facilitating the transition from conventional to precision agriculture, particularly in the context of weed control. Precision agriculture, which previously relied on manual efforts, has now embraced the use of smart devices for more efficient weed detection. However, several challenges are associated with weed detection, including the visual similarity between weed and crop, occlusion and lighting effects, as well as the need for early-stage weed control. Therefore, this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning, as well as the combination of the two methods, for weed detection across different crop fields. The results of this review show the advantages and disadvantages of using machine learning and deep learning. Generally, deep learning produced superior accuracy compared to machine learning under various conditions. Machine learning required the selection of the right combination of features to achieve high accuracy in classifying weed and crop, particularly under conditions consisting of lighting and early growth effects. Moreover, a precise segmentation stage would be required in cases of occlusion. Machine learning had the advantage of achieving real-time processing by producing smaller models than deep learning, thereby eliminating the need for additional GPUs. However, the development of GPU technology is currently rapid, so researchers are more often using deep learning for more accurate weed identification.