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Synthesis and Catalytic Application of ZSM-48 Zeolite ZSM-48 沸石的合成与催化应用
IF 3.6 3区 工程技术 Q2 CHEMISTRY, PHYSICAL Pub Date : 2024-07-11 DOI: 10.1039/d4me00076e
Wen Liu, Yanan Wang, Xinbao Zhang, Fucun Chen, Sujuan Xie, Longya Xu, Xiujie Li, Xiangxue Zhu
ZSM-48 is a kind of high-silica zeolite with one dimensional (1D) 10-member ring (10-MR) channel structure. It is well known for its unique pore structure and acid properties, as well as exceptional catalytic performance in various reactions. However, the diffusion limitation and insufficient acid density pose significant challenges to its widespread application and promotion. This review aims to summarize the advancements in enhancing diffusivity and regulating acid properties of ZSM-48 zeolite, as well as its catalytic applications. To alleviate diffusion limitations, the construction of hierarchical ZSM-48 zeolites through post-treatment and in-situ strategies are extensively summarized. Ongoing endeavors focus on determining the optimal balance between maintaining structural integrity and improving mass transfer capacity through post-treatment techniques. Concerning acid regulation, various strategies such as the use of special organic structure directing agent (OSDA), seed-assisted synthesis, zeolites hybridization, and heteroatom doping strategies have been developed. The emphasis on acid regulation in ZSM-48 zeolite involves efforts to design or discover more cost-effective OSDAs. Additionally, researchers are exploring simpler and more economical seed-assisted synthesis routes to produce Al-rich candidates. In terms of catalytic application, extensive research has been conducted on various reactions including hydroisomerization of paraffin, isomerization of xylenes, cracking of hydrocarbons, and methanol to hydrocarbons have been extensively investigated. Its distinctive catalytic performance is primarily related to the shape-selective advantage conferred by its one dimensional channel structure. In particular, ZSM-48 zeolite is widely regarded as the leading candidate in paraffin hydroisomerization reactions, attributed to its high proportion of multi-branched isomers in the catalytic products. The present review aims to provide a comprehensive reference for researchers dedicated to the synthesis, modification, and application of ZSM-48 zeolite.
ZSM-48 是一种具有一维(1D)10 元环(10-MR)通道结构的高硅沸石。它以其独特的孔隙结构和酸特性以及在各种反应中的优异催化性能而著称。然而,扩散限制和酸密度不足对其广泛应用和推广构成了重大挑战。本综述旨在总结在增强 ZSM-48 沸石的扩散性和调节其酸特性方面的进展及其催化应用。为了缓解扩散限制,本文广泛总结了通过后处理和原位策略构建分层 ZSM-48 沸石的方法。目前的工作重点是通过后处理技术确定保持结构完整性和提高传质能力之间的最佳平衡。在酸调节方面,已开发出多种策略,如使用特殊的有机结构引导剂(OSDA)、种子辅助合成、沸石杂化和杂原子掺杂策略。在 ZSM-48 沸石中进行酸调节的重点是努力设计或发现更具成本效益的 OSDA。此外,研究人员还在探索更简单、更经济的种子辅助合成路线,以生产富铝候选物质。在催化应用方面,对石蜡的加氢异构化、二甲苯的异构化、碳氢化合物的裂解以及甲醇制碳氢化合物等各种反应进行了广泛的研究。其独特的催化性能主要与其一维通道结构所带来的形状选择优势有关。其中,ZSM-48 沸石因其催化产物中多支链异构体比例高而被广泛认为是石蜡加氢异构化反应的主要候选材料。本综述旨在为致力于 ZSM-48 沸石合成、改性和应用的研究人员提供全面的参考。
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引用次数: 0
Designing novel inhibitor derivatives targeting SARS-CoV-2 Mpro enzyme: a deep learning and structure biology approach 设计针对 SARS-CoV-2 Mpro 酶的新型抑制剂衍生物:一种深度学习和结构生物学方法
IF 3.6 3区 工程技术 Q2 CHEMISTRY, PHYSICAL Pub Date : 2024-07-10 DOI: 10.1039/d4me00062e
Tushar Joshi, Shalini Mathpal, Priyanka Sharma, Akshay Abraham, Rajadurai Vijay Solomon, Subhash Chandra
The emerging variants of SARS-CoV-2 have raised serious concerns worldwide due to their infectivity, lethality, and unpredictability. Moreover, the ability of these variants to bypass vaccine protection and immunity has compelled the research community to design novel compounds against SARS-CoV-2. This study focuses on designing novel molecules using artificial intelligence methods for the development of new therapeutics against SARS-CoV-2. Furthermore, these molecules were validated against main protease (Mpro) using in-silico methods. In this study, we used the DeepScreening RNN-based web server to design novel molecules using potential inhibitors of Mpro from CHEMBL4495582. Screened compounds were further validated by molecular docking and molecular dynamics (MD) simulation studies. One hundred molecules were obtained and studied through molecular docking and MD simulations. Additionally, eight molecules, based on their docking scores, were also evaluated for electronic structure properties by conducting Density Functional Theory (DFT) calculations using the B3LYP method and a 6-31G basis set. A total of three compounds, namely L18, L36, and L26, showed very good binding and stability with the active site of the Mpro protein. The results of this study demonstrate that potential molecules can be designed using artificial intelligence methods for the rapid development of drug candidates against SARS-CoV-2, addressing the alarming worldwide situation of emerging deadly SARS-CoV-2 variants. We hope that our study will attract the attention of the scientific community to increase the application of artificial intelligence techniques in the drug discovery process.
新出现的 SARS-CoV-2 变种因其传染性、致命性和不可预测性而引起了全世界的严重关切。此外,这些变种能够绕过疫苗保护和免疫,这迫使研究界设计新型化合物来对抗 SARS-CoV-2。本研究的重点是利用人工智能方法设计新型分子,以开发针对 SARS-CoV-2 的新疗法。此外,这些分子还通过内嵌方法针对主要蛋白酶(Mpro)进行了验证。在这项研究中,我们使用基于 DeepScreening RNN 的网络服务器,利用 CHEMBL4495582 中 Mpro 的潜在抑制剂来设计新型分子。通过分子对接和分子动力学(MD)模拟研究进一步验证了筛选出的化合物。通过分子对接和 MD 模拟研究,共获得 100 个分子。此外,还根据其对接得分,使用 B3LYP 方法和 6-31G 基集进行密度泛函理论(DFT)计算,评估了 8 个分子的电子结构特性。共有三种化合物(即 L18、L36 和 L26)与 Mpro 蛋白的活性位点表现出了很好的结合性和稳定性。这项研究结果表明,利用人工智能方法可以设计出潜在的分子,从而快速开发出抗击 SARS-CoV-2 的候选药物,以应对全球范围内新出现的致命 SARS-CoV-2 变体的严峻形势。我们希望我们的研究能引起科学界的关注,增加人工智能技术在药物发现过程中的应用。
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引用次数: 0
Unravelling the impact of sulfur atom oxidation and donor–acceptor effects on the performance of blue TADF emitters: a detailed computational study 揭示硫原子氧化和供体-受体效应对蓝色 TADF 发射器性能的影响:一项详细的计算研究
IF 3.6 3区 工程技术 Q2 CHEMISTRY, PHYSICAL Pub Date : 2024-07-04 DOI: 10.1039/d4me00035h
Singaravel Nathiya
Blue TADF materials demonstrate significant potential for OLED and photovoltaic applications. Nevertheless, systematic studies are essential to explore the relationship between molecular structures and luminescence properties to develop blue-TADF emitters. In this study, a series of new 24 donor–acceptor–donor (D–A–D) type molecules with different electron donors and acceptors are designed theoretically, and their photophysical properties are analyzed by using DFT and TD-DFT methods. We examined the combined impact of sulfur oxidation and the symmetric incorporation of a nitrogen heteroatom, with positional modifications (2-dipyridyl and 3-dipyridyl), within the phenyl ring of the acceptor group. The findings suggest that enhancing both the donating and accepting strength of the molecules results in an orthogonal geometry and a small ΔEST, accompanied by an enhanced charge-transfer (CT) character. Upon sulfur oxidation, the magnitude of SOC decreases, resulting in a reduction of ΔEST attributed to screening and lone pair effects. Through quantum chemical calculations, we have theoretically identified 12 promising blue TADF molecules, featuring small ΔEST, increased SOC magnitude, and higher RISC (∼10+07 s−1) rates. Overall, our current study provides a robust molecular design approach and reliable computational method for designing a blue TADF emitter.
蓝色 TADF 材料在有机发光二极管和光伏应用方面具有巨大潜力。然而,要开发蓝色 TADF 发射器,必须进行系统研究,探索分子结构与发光特性之间的关系。本研究从理论上设计了一系列具有不同电子供体和受体的新型 24 供体-受体-供体(D-A-D)型分子,并采用 DFT 和 TD-DFT 方法分析了它们的光物理性质。我们研究了硫氧化和在受体基团的苯基环上对称加入一个氮杂原子的综合影响,以及位置修饰(2-二吡啶基和 3-二吡啶基)。研究结果表明,增强分子的供体和受体强度会导致正交几何形状和较小的ΔEST,同时增强电荷转移(CT)特性。硫氧化时,SOC 的大小会减小,从而导致ΔEST 的减小,这归因于屏蔽效应和孤对效应。通过量子化学计算,我们从理论上确定了 12 种有前景的蓝色 TADF 分子,它们具有较小的 ΔEST 值、较高的 SOC 值和较高的 RISC(10∼10+07 s-1)速率。总之,我们目前的研究为设计蓝色 TADF 发射器提供了一种稳健的分子设计方法和可靠的计算方法。
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引用次数: 0
Empowering Tomorrow's Medicine: Energy-Driven Micro/Nano-Robots Redefining Biomedical Applications 赋能未来医学:能源驱动的微型/纳米机器人重新定义生物医学应用
IF 3.6 3区 工程技术 Q2 CHEMISTRY, PHYSICAL Pub Date : 2024-07-02 DOI: 10.1039/d4me00090k
Subham Preetam, Pingal Pritam, Richa Mishra, Smita Lata, Sarvesh Rustagi, Sumira Malik
Micro/nano-robots (MNR) have gained attention as a rapidly developing field with significant potential in advanced therapies and futuristic solutions. These self-propelled robots offer a promising strategy to enhance monitoring, overcome diffusion limitations, and interact effectively with target factors. Research in MNR has become highly influential, especially in addressing critical issues like cancer. The progression from passive micro- and nanomaterials to active MNR and ultimately to intelligent MNR has led to advancements in motion abilities, multifunctionality, adaptive responses, swarming behaviour, and communication among robots. Nanorobotics, featuring sophisticated submicron devices made from nanocomponents, holds great promise for revolutionizing the healthcare industry. This review aims to highlight recent progress in propulsion mechanisms, including chemically controlled micromotors, field control, and biohybrid approaches, which serve as power sources for various biomedical and environmental applications. These applications utilize different energy sources such as magnetic, light, auditory, electric, and chemical reactions, particularly in drug delivery systems for cancer treatment. The review also discusses the challenges and future directions in the practical implementation of smart micro- and nanorobots, paving the way for their real-world applications.
微型/纳米机器人(MNR)作为一个快速发展的领域备受关注,在先进疗法和未来解决方案方面具有巨大潜力。这些自走式机器人为加强监测、克服扩散限制以及与目标因子有效互动提供了一种前景广阔的策略。MNR 研究已具有很大的影响力,尤其是在解决癌症等关键问题方面。从被动的微型和纳米材料到主动的多功能纳米机器人,再到最终的智能多功能纳米机器人,机器人在运动能力、多功能性、自适应反应、蜂群行为和相互通信等方面都取得了进步。纳米机器人的特点是由纳米元件制成的精密亚微米设备,它为医疗保健行业带来了巨大的变革前景。本综述旨在重点介绍推进机制方面的最新进展,包括化学控制微电机、场控制和生物混合方法,这些方法可作为各种生物医学和环境应用的动力源。这些应用利用了不同的能源,如磁、光、听觉、电和化学反应,特别是在治疗癌症的药物输送系统中。综述还讨论了智能微型机器人和纳米机器人在实际应用中面临的挑战和未来发展方向,为它们在现实世界中的应用铺平了道路。
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引用次数: 0
Outstanding Reviewers for Molecular Systems Design & Engineering in 2023 2023 年《分子系统设计与工程》杰出审稿人
IF 3.6 3区 工程技术 Q2 CHEMISTRY, PHYSICAL Pub Date : 2024-06-28 DOI: 10.1039/d4me90024c
We would like to take this opportunity to thank all of Molecular Systems Design & Engineering (MSDE)'s reviewers for helping to preserve quality and integrity in chemical science literature. We would also like to highlight the Outstanding Reviewers for MSDE in 2023.
我们想借此机会感谢《分子系统设计与工程》(MSDE)的所有审稿人,感谢他们帮助维护化学科学文献的质量和完整性。我们还想特别介绍一下 2023 年 MSDE 的杰出审稿人。
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引用次数: 0
Machine learning-based epoxy resin property prediction 基于机器学习的环氧树脂性能预测
IF 3.6 3区 工程技术 Q2 CHEMISTRY, PHYSICAL Pub Date : 2024-06-25 DOI: 10.1039/d4me00060a
Huiwon Jang, Dayoung Ryu, Wonseok Lee, Geunyeong Park, Jihan Kim
Epoxy resins have been utilized across various industries due to their superior mechanical and chemical properties. However, discovering the optimal design of epoxy resins is challenging because of the large chemical space of polymer systems. In this study, we adopted a data-driven approach to develop an effective prediction system for epoxy resin. In particular, we constructed a database of 789 epoxy resins, encompassing four key properties: density, coefficient of thermal expansion, glass transition temperature, and Young's modulus, obtained through molecular dynamics simulations. We devised descriptors that effectively represent epoxy resins. Ultimately, a machine learning model was trained, successfully predicting properties with reasonable accuracy. Our predictive model is a generalized model that was verified across various types of epoxy resins, making it applicable to all kinds of epoxy and hardener combinations. This achievement enables large-scale screening over numerous polymers, accelerating the discovery process. Further, we conducted an in-depth analysis of the important features that have a high impact on the epoxy resin. This provides valuable insights into the structure–property relationship which can guide researchers in designing new epoxy resins.
环氧树脂具有优异的机械和化学特性,已被广泛应用于各个行业。然而,由于聚合物体系的化学空间很大,发现环氧树脂的最佳设计具有挑战性。在本研究中,我们采用了数据驱动法来开发环氧树脂的有效预测系统。特别是,我们构建了一个包含 789 种环氧树脂的数据库,其中包括通过分子动力学模拟获得的四种关键特性:密度、热膨胀系数、玻璃化温度和杨氏模量。我们设计了能有效代表环氧树脂的描述符。最终,我们训练了一个机器学习模型,成功地预测出了具有合理准确度的特性。我们的预测模型是一个通用模型,已在各种类型的环氧树脂中得到验证,因此适用于所有类型的环氧树脂和固化剂组合。这一成果实现了对众多聚合物的大规模筛选,加快了发现过程。此外,我们还深入分析了对环氧树脂影响较大的重要特征。这为研究人员设计新型环氧树脂提供了结构-性能关系方面的宝贵见解。
{"title":"Machine learning-based epoxy resin property prediction","authors":"Huiwon Jang, Dayoung Ryu, Wonseok Lee, Geunyeong Park, Jihan Kim","doi":"10.1039/d4me00060a","DOIUrl":"https://doi.org/10.1039/d4me00060a","url":null,"abstract":"Epoxy resins have been utilized across various industries due to their superior mechanical and chemical properties. However, discovering the optimal design of epoxy resins is challenging because of the large chemical space of polymer systems. In this study, we adopted a data-driven approach to develop an effective prediction system for epoxy resin. In particular, we constructed a database of 789 epoxy resins, encompassing four key properties: density, coefficient of thermal expansion, glass transition temperature, and Young's modulus, obtained through molecular dynamics simulations. We devised descriptors that effectively represent epoxy resins. Ultimately, a machine learning model was trained, successfully predicting properties with reasonable accuracy. Our predictive model is a generalized model that was verified across various types of epoxy resins, making it applicable to all kinds of epoxy and hardener combinations. This achievement enables large-scale screening over numerous polymers, accelerating the discovery process. Further, we conducted an in-depth analysis of the important features that have a high impact on the epoxy resin. This provides valuable insights into the structure–property relationship which can guide researchers in designing new epoxy resins.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design of ionic liquid crystals enabled by [2]rotaxane structure formation 通过形成 [2]rotaxane 结构设计离子液晶
IF 3.6 3区 工程技术 Q2 CHEMISTRY, PHYSICAL Pub Date : 2024-06-20 DOI: 10.1039/d4me00034j
Gosuke Washino, Takashi Kajitani, Suzushi Nishimura, Atsushi Shishido
We report a new synthetic concept for converting isotropic ionic molecules into thermotropic ionic liquid crystals by forming [2]rotaxane structures. Our results demonstrate the synthesis of liquid-crystalline (LC) rotaxane from an ionic axle molecule as a mesogen core, and a molecular ring as flexible tails, neither of which possess LC properties. The [2]rotaxane obtained exhibited an interdigitated smectic A phase at around 140 °C. A simple mixture of the axle and the ring, which cannot form a rotaxane structure, did not show an LC phase. A [2]rotaxane compound having a ring with shorter flexible tails did not show an LC phase, either. These comparisons revealed that the integration of the mesogen core and flexible tails of a sufficient length in one molecule via the rotaxane structure enables the emergence of LC nature. Our results prove that the rotaxane structure serves as a connection to spatially introduce flexible tails into the mesogen core, pioneering a new approach to LC molecular design.
我们报告了一种通过形成 [2]rotaxane 结构将各向同性离子分子转化为热致性离子液晶的新合成概念。我们的研究结果表明,以离子轴分子为介质核心,以分子环为柔性尾部(两者均不具备液晶特性),合成了液晶(LC)罗他烷。获得的 [2]rotaxane 在 140 °C 左右呈现出相互交错的 Smectic A 相。轴和环的简单混合物不能形成罗他烷结构,也没有显示出低密度相。一种具有较短柔性尾部的环的 [2]rotaxane 化合物也没有出现 LC 相。这些比较结果表明,通过轮烷结构将介源核心和足够长的柔性尾端整合在一个分子中,可以产生低浓相。我们的研究结果证明,轮烷结构是将柔性尾巴在空间上引入介源核心的连接纽带,开创了低浓分子设计的新方法。
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引用次数: 0
Polymer configuration conversion mechanism in dynamically stable interface of silicon anodes 硅阳极动态稳定界面中的聚合物构型转换机制
IF 3.6 3区 工程技术 Q2 CHEMISTRY, PHYSICAL Pub Date : 2024-06-07 DOI: 10.1039/d4me00049h
Qiaoqiao Ye, Miaomiao Jiang, Yingbing Zhang, Lei Chen, Yuanyuan Ma, Jianping Yang
Silicon oxide nanospheres (SiOC) have been considered one of the key candidates for the next generation of high-energy-density anode materials. Nevertheless, the intrinsic limitations of their design impede their large-scale commercial deployment, including large volume expansion, poor electrical conductivity, and low initial coulombic efficiency (ICE). The application of a polymer coating represents a beneficial modification. Herein, a composite SiOC anode is synthesized by constructing poly(hexaazatrinaphthalene) (PHATN) on the surface of boron doping-induced self-assembled SiOC nanospheres. The SiOC nanospheres change from a monodisperse structure to a regular and ordered arrangement by self-assembly, which improves the structural stability. A special polymer, PHATN, is selected for its unique structure, which introduces a dynamic conversion mechanism to the material. During the lithium intercalation process, –C[double bond, length as m-dash]N– groups in the PHATN coordinate with Li+ to form –C–N–Li– bonds on the PHATN molecule layer. The dynamic volume change of the PHATN molecule allows room for the volume expansion of SiOC, thus providing excellent protection against structural collapse. After 1000 deep cycles, the capacity of the composite anode can be maintained at 623.7 mA h g−1, showing considerable stability and superior specific capacity. PHATN simultaneously repairs the surface defects of the SiOC assemblies and enhances the performance of the SEI membrane, increasing the ICE from 40% to 50%, which exhibits better electrochemical performance.
氧化硅纳米球(SiOC)一直被认为是下一代高能量密度阳极材料的主要候选材料之一。然而,其设计的内在局限性阻碍了它们的大规模商业应用,包括体积膨胀大、导电性差和初始库仑效率(ICE)低。应用聚合物涂层是一种有益的改进。本文通过在掺硼自组装 SiOC 纳米球表面构建聚(六氮杂萘)(PHATN),合成了一种复合 SiOC 阳极。通过自组装,SiOC 纳米球从单分散结构变为规则有序的排列,从而提高了结构的稳定性。该材料选用了一种特殊的聚合物 PHATN,其独特的结构为材料引入了一种动态转换机制。在锂插层过程中,PHATN 中的 -CN- 基团与 Li+ 配位,在 PHATN 分子层上形成 -C-N-Li- 键。PHATN 分子的动态体积变化为 SiOC 的体积膨胀留出了空间,从而为防止结构崩溃提供了良好的保护。经过 1000 次深度循环后,复合阳极的容量可保持在 623.7 mA h g-1,显示出相当高的稳定性和卓越的比容量。PHATN 同时修复了 SiOC 组件的表面缺陷,并提高了 SEI 膜的性能,将 ICE 从 40% 提高到 50%,从而表现出更好的电化学性能。
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引用次数: 0
Applying local interpretable model-agnostic explanations to identify substructures that are responsible for mutagenicity of chemical compounds 应用局部可解释的模型--不可知论解释,确定导致化合物致突变性的亚结构
IF 3.6 3区 工程技术 Q1 Chemistry Pub Date : 2024-06-05 DOI: 10.1039/d4me00038b
Lucca Caiaffa Santos Rosa, Andre Silva Pimentel
The local interpretable model-agnostic explanations method was applied to identify substructures that represent the mutagenicity of chemical compounds using machine learning models. Random forest and extremely randomized trees were used to build models to be explained using the Hansen and Bursi Ames mutagenicity datasets. The models were analyzed using precision, recall, F1, and accuracy metrics. The aim of this study is to address the challenge of identifying substructures that indicate the mutagenicity of chemical compounds. The goal is to provide stable and consistent explanations for the mutagenicity of chemical compounds, which is crucial for trust and acceptance of the findings, especially in the sensitive field of computational toxicology. This approach is significant as it contributes to the interpretability and explainability of machine learning models, particularly in the context of identifying substructures associated with mutagenicity, thereby advancing the field of computational toxicology. Identifying substructures that represent the mutagenicity of chemical compounds is important because it can help predict the potential toxicity of new chemical compounds. This is particularly relevant in fields such as drug development and environmental toxicology, where the potential risks of exposure to new compounds need to be carefully evaluated. Some examples of chemical compounds that have been identified as mutagenic include epoxides, N-aryl compounds, nitro compounds, aromatic amines, N-oxides, nitro-containing compounds, and polycyclic aromatic hydrocarbons with a bay-region. These examples demonstrate the importance of identifying and studying mutagenic chemical compounds to better understand their potential risks and adverse effects on human health and the environment.
利用机器学习模型,采用局部可解释模型-不可知论解释方法来识别代表化合物致突变性的子结构。使用随机森林和极端随机树建立模型,并利用汉森和布尔西-艾姆斯诱变数据集进行解释。使用精确度、召回率、F1 和准确度指标对模型进行了分析。本研究的目的是应对识别表明化合物致突变性的亚结构这一挑战。其目的是为化合物的致突变性提供稳定一致的解释,这对研究结果的信任度和接受度至关重要,尤其是在敏感的计算毒理学领域。这种方法意义重大,因为它有助于提高机器学习模型的可解释性和可解释性,特别是在识别与致突变性相关的子结构方面,从而推动计算毒理学领域的发展。识别代表化合物致突变性的子结构非常重要,因为这有助于预测新化合物的潜在毒性。这与药物开发和环境毒理学等领域尤其相关,因为这些领域需要仔细评估接触新化合物的潜在风险。已确定为诱变化合物的一些例子包括环氧化物、N-芳基化合物、硝基化合物、芳香胺、N-氧化物、含硝基化合物以及带有畦区的多环芳烃。这些例子说明了识别和研究诱变化合物的重要性,以便更好地了解它们对人类健康和环境的潜在风险和不利影响。
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引用次数: 0
GREEN SYNTHESIS OF THERMO/PHOTOCHROMIC DOPED CELLULOSE POLYMER: A BIOCOMPATIBLE FILM FOR POTENTIAL APPLICATION IN COLD CHAIN VISUAL TRACKING 热/光变色掺杂纤维素聚合物的绿色合成:一种可应用于冷链视觉跟踪的生物相容性薄膜
IF 3.6 3区 工程技术 Q1 Chemistry Pub Date : 2024-05-31 DOI: 10.1039/d4me00055b
Simone D'Agostino, Alessandra Azzali, Maria Di Filippo, Lucia Bertucioli, Silvia Panzavolta, Sophie Lilburn, Fabrizia Grepioni
To mitigate food losses and ensure a robust cold chain in transportation, sensors play a pivotal role in swiftly and visibly monitoring storage conditions. Molecules with thermo- and photochromic properties and N-salicylideneanilines and derivatives, have emerged as promising candidates due to synthesis simplicity and ability to respond to stimuli. In this study we have synthesized a family of anils through mechanochemistry, focusing on H/F substituents on the bromoaniline residue. A suitable photochromic compound was identified and incorporated into a carboxymethyl cellulose (CMC) biopolymer matrix, to produce a photochromic composite film. UV radiation induced a color change in the film from colorless to red: reversibility was evaluated at different temperatures by means of UV-Vis spectroscopy. The composite film maintained a deep red color at -19 °C and 4 °C for seven weeks, while rapidly reversing to white/yellowish at room temperature, making it suitable for cold chain transport and scenarios requiring rapid visual inspection of storage conditions.
为了减少食品损失并确保运输过程中的冷链稳固,传感器在迅速、明显地监测储存条件方面发挥着关键作用。具有热致变色和光致变色特性的分子以及 N-缩水甘油基苯胺及其衍生物,由于合成简单且能对刺激做出反应,已成为很有前途的候选物质。在这项研究中,我们通过机械化学方法合成了一系列苯胺,重点是溴苯胺残基上的 H/F 取代基。我们找到了一种合适的光致变色化合物,并将其加入到羧甲基纤维素(CMC)生物聚合物基质中,制成了一种光致变色复合薄膜。紫外线辐射使薄膜的颜色从无色变为红色:在不同温度下,通过紫外可见光谱法评估了可逆性。这种复合薄膜在-19 °C和4 °C温度下保持深红色长达七周,而在室温下则迅速变为白色/淡黄色,因此适用于冷链运输和需要快速目测储存条件的场合。
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引用次数: 0
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Molecular Systems Design & Engineering
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