Pub Date : 2024-08-14DOI: 10.1038/s44286-024-00109-2
Alessio Lavino
{"title":"A chemical reservoir computer","authors":"Alessio Lavino","doi":"10.1038/s44286-024-00109-2","DOIUrl":"10.1038/s44286-024-00109-2","url":null,"abstract":"","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"496-496"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-07DOI: 10.1038/s44286-024-00092-8
Wilhelm T. S. Huck
The rates of all enzymatic reactions vary with temperature. Now, it is shown how this temperature sensitivity can be exploited to construct oscillating reaction networks that are able to detect temperature changes with remarkable precision.
{"title":"Oscillating chemical reaction networks stopped cold","authors":"Wilhelm T. S. Huck","doi":"10.1038/s44286-024-00092-8","DOIUrl":"10.1038/s44286-024-00092-8","url":null,"abstract":"The rates of all enzymatic reactions vary with temperature. Now, it is shown how this temperature sensitivity can be exploited to construct oscillating reaction networks that are able to detect temperature changes with remarkable precision.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"499-500"},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-07DOI: 10.1038/s44286-024-00087-5
N. Lobato-Dauzier, A. Baccouche, G. Gines, T. Levi, Y. Rondelez, T. Fujii, S. H. Kim, N. Aubert-Kato, A. J. Genot
Complex organisms perceive their surroundings with sensory neurons that encode physical stimuli into spikes of electrical activities. The past decades have seen a throve of computing approaches taking inspiration from neurons, including reports of DNA-based chemical neurons that mimic artificial neural networks with chemical reactions. Yet, they lack the physical sensing and temporal coding of sensory biological neurons. Here we report a thermosensory chemical neuron based on DNA and enzymes that spikes with chemical activity when exposed to cold. Surprisingly, this chemical neuron shares deep mathematical similarities with a toy model of a cold nociceptive neuron: they follow a similar bifurcation route between rest and oscillations and avoid artefacts associated with canonical bifurcations (such as irreversibility, damping or untimely spiking). We experimentally demonstrate this robustness by encoding—digitally and analogically—thermal messages into chemical waveforms. This chemical neuron could pave the way for implementing the third generation of neural network models (spiking networks) in DNA and opens the door for associative learning. Complex organisms perceive their surroundings with sensory neurons that encode physical stimuli into spikes of electrical activities. Here a thermosensory chemical neuron based on DNA and enzymes has been reported, which spikes with chemical activity when exposed to cold.
复杂的生物体通过感觉神经元感知周围环境，这些神经元将物理刺激编码为尖峰电活动。过去几十年来，从神经元中汲取灵感的计算方法层出不穷，其中包括利用化学反应模拟人工神经网络的 DNA 化学神经元。然而，它们缺乏生物感知神经元的物理传感和时间编码。在这里，我们报告了一种基于DNA和酶的热感化学神经元，当暴露在寒冷环境中时，它的化学活性会出现尖峰。令人惊讶的是，这种化学神经元与冷痛觉神经元的玩具模型在数学上有很深的相似之处：它们在静止和振荡之间遵循相似的分岔路线，并避免了与典型分岔相关的伪现象（如不可逆、阻尼或不适时尖峰）。我们通过将数字和模拟热信息编码成化学波形，在实验中证明了这种鲁棒性。这种化学神经元可为在 DNA 中实现第三代神经网络模型（尖峰网络）铺平道路，并为联想学习打开大门。复杂生物通过感觉神经元感知周围环境，这些神经元将物理刺激编码为尖峰电活动。这里报告的是一种基于 DNA 和酶的热感化学神经元，当暴露在寒冷环境中时，这种神经元会产生尖峰化学反应。
{"title":"Neural coding of temperature with a DNA-based spiking chemical neuron","authors":"N. Lobato-Dauzier, A. Baccouche, G. Gines, T. Levi, Y. Rondelez, T. Fujii, S. H. Kim, N. Aubert-Kato, A. J. Genot","doi":"10.1038/s44286-024-00087-5","DOIUrl":"10.1038/s44286-024-00087-5","url":null,"abstract":"Complex organisms perceive their surroundings with sensory neurons that encode physical stimuli into spikes of electrical activities. The past decades have seen a throve of computing approaches taking inspiration from neurons, including reports of DNA-based chemical neurons that mimic artificial neural networks with chemical reactions. Yet, they lack the physical sensing and temporal coding of sensory biological neurons. Here we report a thermosensory chemical neuron based on DNA and enzymes that spikes with chemical activity when exposed to cold. Surprisingly, this chemical neuron shares deep mathematical similarities with a toy model of a cold nociceptive neuron: they follow a similar bifurcation route between rest and oscillations and avoid artefacts associated with canonical bifurcations (such as irreversibility, damping or untimely spiking). We experimentally demonstrate this robustness by encoding—digitally and analogically—thermal messages into chemical waveforms. This chemical neuron could pave the way for implementing the third generation of neural network models (spiking networks) in DNA and opens the door for associative learning. Complex organisms perceive their surroundings with sensory neurons that encode physical stimuli into spikes of electrical activities. Here a thermosensory chemical neuron based on DNA and enzymes has been reported, which spikes with chemical activity when exposed to cold.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"510-521"},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44286-024-00087-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-06DOI: 10.1038/s44286-024-00102-9
Yaxu Zhong, Timothy C. Moore, Tobias Dwyer, Alex Butrum-Griffith, Vincent R. Allen, Jun Chen, Yi Wang, Fanrui Cheng, Sharon C. Glotzer, Xingchen Ye
Nanoparticle self-assembly offers a scalable and versatile means to fabricate next-generation materials. The prevalence of metastable and nonequilibrium states during the assembly process makes the final structure and function directly dependent upon formation pathways. However, it remains challenging to steer the assembly pathway of a nanoparticle system toward multiple superstructures while visualizing in situ. Here we use liquid-cell transmission electron microscopy to image complete self-assembly processes of gold nanocubes, a model shape-anisotropic nanocolloidal system, into distinct superlattices. Theoretical analysis and molecular dynamics simulations indicate that the electrostatic screening of the medium dictates self-assembly pathways by its effects on the interactions between nanocubes. We leverage this understanding to demonstrate on-the-fly control of assembly behavior through rapid solvent exchange. Our joint experiment–simulation–theory investigation paves the way for elucidating the relationships among building block attributes, assembly pathways and superstructures in nanoscale assembly and opens new avenues for the bottom-up design of reconfigurable and adaptive metamaterials. Guiding the assembly pathway of a nanoparticle system toward multiple superstructures while visualizing in situ remains challenging. Here the authors combine liquid-cell transmission electron microscopy, scaling theory and molecular dynamics simulations to image and quantify self-assembly processes of gold nanocubes into distinct superlattices.
{"title":"Engineering and direct imaging of nanocube self-assembly pathways","authors":"Yaxu Zhong, Timothy C. Moore, Tobias Dwyer, Alex Butrum-Griffith, Vincent R. Allen, Jun Chen, Yi Wang, Fanrui Cheng, Sharon C. Glotzer, Xingchen Ye","doi":"10.1038/s44286-024-00102-9","DOIUrl":"10.1038/s44286-024-00102-9","url":null,"abstract":"Nanoparticle self-assembly offers a scalable and versatile means to fabricate next-generation materials. The prevalence of metastable and nonequilibrium states during the assembly process makes the final structure and function directly dependent upon formation pathways. However, it remains challenging to steer the assembly pathway of a nanoparticle system toward multiple superstructures while visualizing in situ. Here we use liquid-cell transmission electron microscopy to image complete self-assembly processes of gold nanocubes, a model shape-anisotropic nanocolloidal system, into distinct superlattices. Theoretical analysis and molecular dynamics simulations indicate that the electrostatic screening of the medium dictates self-assembly pathways by its effects on the interactions between nanocubes. We leverage this understanding to demonstrate on-the-fly control of assembly behavior through rapid solvent exchange. Our joint experiment–simulation–theory investigation paves the way for elucidating the relationships among building block attributes, assembly pathways and superstructures in nanoscale assembly and opens new avenues for the bottom-up design of reconfigurable and adaptive metamaterials. Guiding the assembly pathway of a nanoparticle system toward multiple superstructures while visualizing in situ remains challenging. Here the authors combine liquid-cell transmission electron microscopy, scaling theory and molecular dynamics simulations to image and quantify self-assembly processes of gold nanocubes into distinct superlattices.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"532-541"},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s44286-024-00107-4
Metal–organic frameworks are promising materials for use as sustainable membrane technology. However, their use for liquid-phase separation is limited. We developed a metal–organic framework with topological defects to build membranes with high performance for molecular separation in methanol. The efficient and durable sieving of molecules through membrane modules indicates their potential for refining chemical products.
{"title":"Turning defects in metal–organic frameworks into benefits for membrane separation","authors":"","doi":"10.1038/s44286-024-00107-4","DOIUrl":"10.1038/s44286-024-00107-4","url":null,"abstract":"Metal–organic frameworks are promising materials for use as sustainable membrane technology. However, their use for liquid-phase separation is limited. We developed a metal–organic framework with topological defects to build membranes with high performance for molecular separation in methanol. The efficient and durable sieving of molecules through membrane modules indicates their potential for refining chemical products.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"508-509"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s44286-024-00103-8
Lun Li, Jinlong Yang, Rui Tan, Wei Shu, CheeTong John Low, Zixin Zhang, Yu Zhao, Cheng Li, Yajun Zhang, Xingchuan Li, Huazhang Zhang, Xin Zhao, Zongkui Kou, Yong Xiao, Francis Verpoort, Hewu Wang, Liqiang Mai, Daping He
Thermal runaway, a major battery safety issue, is triggered when the local temperature exceeds a threshold value resulting from slower heat dissipation relative to heat generation inside the cell. However, improving internal heat transfer is challenged by the low thermal conductivity of metal current collectors (CCs) and challenges in manufacturing nonmetal CC foils at large scales. Here we report a rapid temperature-responsive nonmetallic CC that can substitute benchmark Al and Cu foils to enhance battery safety. The nonmetallic CC was fabricated through a continuous thermal pressing process to afford a highly oriented Gr foil on a hundred-meter scale. This Gr foil demonstrates a high thermal conductivity of 1,400.8 W m−1 K−1, about one order of magnitude higher than those of Al and Cu foils. Importantly, LiNi0.8Co0.1Mn0.1O2||graphite cells integrated with these temperature-responsive foils show faster heat dissipation, eliminating the local heat concentration and circumventing the fast exothermic aluminothermic and hydrogen-evolution reactions, which are critical factors causing the thermal failure propagation of lithium-ion battery packs. Understanding and preventing thermal runaway is critical to ensuring the safe and reliable operation of batteries. Here the authors demonstrate the large-scale production of a highly conductive graphene-based foil current collector to mitigate thermal runaway in high-capacity batteries.
热失控是电池安全的一个主要问题，当局部温度超过阈值时，电池内部的散热速度相对于发热速度较慢，从而引发热失控。然而，由于金属集流体（CC）的热传导率较低，以及大规模制造非金属集流体箔的挑战，改善内部热传导面临挑战。在此，我们报告了一种快速温度响应非金属 CC，它可以替代基准铝箔和铜箔，从而提高电池的安全性。这种非金属 CC 是通过连续热压工艺制造的，从而获得了百米级的高取向性 Gr 箔。这种 Gr 箔的热导率高达 1,400.8 W m-1 K-1，比铝箔和铜箔高出约一个数量级。重要的是，集成了这种温度响应箔的 LiNi0.8Co0.1Mn0.1O2|| 石墨电池的散热速度更快，消除了局部热量集中，避免了快速放热的铝热反应和氢化反应，而这些反应是导致锂离子电池组热失效传播的关键因素。了解和防止热失控对于确保电池的安全可靠运行至关重要。作者在本文中展示了大规模生产基于石墨烯的高导电箔电流收集器，以缓解大容量电池中的热失控现象。
{"title":"Large-scale current collectors for regulating heat transfer and enhancing battery safety","authors":"Lun Li, Jinlong Yang, Rui Tan, Wei Shu, CheeTong John Low, Zixin Zhang, Yu Zhao, Cheng Li, Yajun Zhang, Xingchuan Li, Huazhang Zhang, Xin Zhao, Zongkui Kou, Yong Xiao, Francis Verpoort, Hewu Wang, Liqiang Mai, Daping He","doi":"10.1038/s44286-024-00103-8","DOIUrl":"10.1038/s44286-024-00103-8","url":null,"abstract":"Thermal runaway, a major battery safety issue, is triggered when the local temperature exceeds a threshold value resulting from slower heat dissipation relative to heat generation inside the cell. However, improving internal heat transfer is challenged by the low thermal conductivity of metal current collectors (CCs) and challenges in manufacturing nonmetal CC foils at large scales. Here we report a rapid temperature-responsive nonmetallic CC that can substitute benchmark Al and Cu foils to enhance battery safety. The nonmetallic CC was fabricated through a continuous thermal pressing process to afford a highly oriented Gr foil on a hundred-meter scale. This Gr foil demonstrates a high thermal conductivity of 1,400.8 W m−1 K−1, about one order of magnitude higher than those of Al and Cu foils. Importantly, LiNi0.8Co0.1Mn0.1O2||graphite cells integrated with these temperature-responsive foils show faster heat dissipation, eliminating the local heat concentration and circumventing the fast exothermic aluminothermic and hydrogen-evolution reactions, which are critical factors causing the thermal failure propagation of lithium-ion battery packs. Understanding and preventing thermal runaway is critical to ensuring the safe and reliable operation of batteries. Here the authors demonstrate the large-scale production of a highly conductive graphene-based foil current collector to mitigate thermal runaway in high-capacity batteries.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"542-551"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s44286-024-00099-1
Tom Savage, Nausheen Basha, Jonathan McDonough, James Krassowski, Omar Matar, Ehecatl Antonio del Rio Chanona
Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterizations of reactor geometries are low dimensional with expensive optimization, limiting more complex solutions. To address this challenge, we have established a machine learning-assisted approach for the design of new chemical reactors, combining the application of high-dimensional parameterizations, computational fluid dynamics and multi-fidelity Bayesian optimization. We associate the development of mixing-enhancing vortical flow structures in coiled reactors with performance and used our approach to identify the key characteristics of optimal designs. By appealing to the principles of fluid dynamics, we rationalized the selection of design features that lead to experimental plug flow performance improvements of ~60% compared with conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with ‘augmented intelligence’ approaches can give rise to reactor designs with enhanced performance. Identifying the optimal geometry of continuous flow reactors is a major challenge due to the large available parameter design space. Here the authors combine a machine learning-assisted methodology with computational fluid dynamics and additive manufacturing for the design of more efficient, complex coiled-tube reactors.
{"title":"Machine learning-assisted discovery of flow reactor designs","authors":"Tom Savage, Nausheen Basha, Jonathan McDonough, James Krassowski, Omar Matar, Ehecatl Antonio del Rio Chanona","doi":"10.1038/s44286-024-00099-1","DOIUrl":"10.1038/s44286-024-00099-1","url":null,"abstract":"Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterizations of reactor geometries are low dimensional with expensive optimization, limiting more complex solutions. To address this challenge, we have established a machine learning-assisted approach for the design of new chemical reactors, combining the application of high-dimensional parameterizations, computational fluid dynamics and multi-fidelity Bayesian optimization. We associate the development of mixing-enhancing vortical flow structures in coiled reactors with performance and used our approach to identify the key characteristics of optimal designs. By appealing to the principles of fluid dynamics, we rationalized the selection of design features that lead to experimental plug flow performance improvements of ~60% compared with conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with ‘augmented intelligence’ approaches can give rise to reactor designs with enhanced performance. Identifying the optimal geometry of continuous flow reactors is a major challenge due to the large available parameter design space. Here the authors combine a machine learning-assisted methodology with computational fluid dynamics and additive manufacturing for the design of more efficient, complex coiled-tube reactors.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"522-531"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44286-024-00099-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s44286-024-00095-5
Jeffrey A. Bennett, Milad Abolhasani
The geometric design space of continuous flow reactors for optimal process intensification is prohibitively large for a comprehensive search, but incorporation of multi-fidelity optimization techniques using computer simulations and additive manufacturing can rapidly improve reactor performance.
{"title":"Machine-learning optimization of 3D-printed flow-reactor geometry","authors":"Jeffrey A. Bennett, Milad Abolhasani","doi":"10.1038/s44286-024-00095-5","DOIUrl":"10.1038/s44286-024-00095-5","url":null,"abstract":"The geometric design space of continuous flow reactors for optimal process intensification is prohibitively large for a comprehensive search, but incorporation of multi-fidelity optimization techniques using computer simulations and additive manufacturing can rapidly improve reactor performance.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 8","pages":"501-503"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1038/s44286-024-00096-4
Xiansong Shi, He Li, Ting Chen, Yidan Duan, Dongchen Shi, Chengjun Kang, Zhaoqiang Zhang, Dan Zhao
Separating fine and similarly sized targets in liquids is a crucial but challenging task. Although current membranes have the potential to be sustainable and energy-efficient options, their molecular selectivity and durability remain limited. Here we report robust and accurate molecular-sieving membranes created through the topological design of a Zr-based metal–organic framework, namely UiO-66, for use in durable liquid-phase separations. Our findings reveal that crystallizing UiO-66 using a bimetallic method yields distinctive reo-topology frameworks with periodic missing-cluster defects. We crystallize reo-UiO-66 into thin polycrystalline membranes that exhibit improved and robust performance, lasting for over 1,500 h. The modified Ferry transport model provides a quantitative description of solute rejection from the polycrystalline membrane. Multiple molecular-sieving experiments recognize excellent membrane selectivity to accurately discriminate fine complex mixtures with molecular weights below 350 g mol−1. In addition, our membrane demonstrates promise in purifying and recovering high-value pharmaceuticals and catalysts. This work paves the way for developing polycrystalline membrane technology for the sustainable separation of chemical mixtures in liquids. Efficiently separating high-value targets with small structural differences in liquids is important to the chemical industry. Here the authors develop a metal–organic framework-based membrane with engineered topologic defects for accurate and prolonged sieving of species with molecular weights below 350 g mol−1.
分离液体中的细小和类似大小的目标是一项至关重要但又极具挑战性的任务。尽管目前的膜有可能成为可持续的节能选择，但其分子选择性和耐久性仍然有限。在此，我们报告了通过对一种基于 Zr 的金属有机框架（即 UiO-66）进行拓扑设计而制造出的用于持久液相分离的坚固而精确的分子筛分膜。我们的研究结果表明，使用双金属方法使 UiO-66 结晶，可产生具有周期性缺失簇缺陷的独特 reo 拓扑框架。我们将 reo-UiO-66 结晶成薄薄的多晶膜，这种多晶膜的性能得到了改善，而且非常坚固耐用，可持续使用超过 1,500 小时。多项分子筛分实验表明，膜具有出色的选择性，能准确分辨分子量低于 350 g mol-1 的精细复杂混合物。此外，我们的膜在提纯和回收高价值药物和催化剂方面也大有可为。这项工作为开发可持续分离液体中化学混合物的多晶膜技术铺平了道路。有效分离液体中结构差异较小的高价值目标对化学工业非常重要。在此，作者开发了一种基于金属有机框架的膜，该膜具有工程拓扑缺陷，可准确、长时间地筛分分子量低于 350 g mol-1 的物质。
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Pub Date : 2024-07-25DOI: 10.1038/s44286-024-00106-5
Comparative process analysis is foundational to chemical engineering. This Editorial discusses comparative language and the role that narrative choices play in communicating these analyses.
比较过程分析是化学工程的基础。这篇社论讨论了比较语言以及叙述选择在交流这些分析时所起的作用。
{"title":"How green is the grass on the other side?","authors":"","doi":"10.1038/s44286-024-00106-5","DOIUrl":"10.1038/s44286-024-00106-5","url":null,"abstract":"Comparative process analysis is foundational to chemical engineering. This Editorial discusses comparative language and the role that narrative choices play in communicating these analyses.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"1 7","pages":"441-441"},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44286-024-00106-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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