Pub Date : 2025-12-19DOI: 10.1038/s44286-025-00332-5
Kathryn A. Whitehead
Effective delivery has long constrained RNA therapeutics. A 2008 combinatorial chemistry approach transformed lipid discovery and testing, establishing a paradigm that is still contributing to the clinical translation of RNA medicines today.
{"title":"The great lipid hunt","authors":"Kathryn A. Whitehead","doi":"10.1038/s44286-025-00332-5","DOIUrl":"10.1038/s44286-025-00332-5","url":null,"abstract":"Effective delivery has long constrained RNA therapeutics. A 2008 combinatorial chemistry approach transformed lipid discovery and testing, establishing a paradigm that is still contributing to the clinical translation of RNA medicines today.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"3 1","pages":"10-11"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049450","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 : 2025-12-19DOI: 10.1038/s44286-025-00327-2
Radu Custelcean, Benjamin Doughty
Atmospheric CO2 removal through direct air capture (DAC) may help restore CO2 concentrations to optimal levels, but slow CO2 uptake rates remain a major impediment. Now, an intensified DAC process has been demonstrated, involving enhanced CO2 capture rates and reactive K2CO3 crystallization at the interface of ultraconcentrated KOH solutions with air.
{"title":"Harnessing interfaces for direct air capture","authors":"Radu Custelcean, Benjamin Doughty","doi":"10.1038/s44286-025-00327-2","DOIUrl":"10.1038/s44286-025-00327-2","url":null,"abstract":"Atmospheric CO2 removal through direct air capture (DAC) may help restore CO2 concentrations to optimal levels, but slow CO2 uptake rates remain a major impediment. Now, an intensified DAC process has been demonstrated, involving enhanced CO2 capture rates and reactive K2CO3 crystallization at the interface of ultraconcentrated KOH solutions with air.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"3 1","pages":"24-25"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049446","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 : 2025-12-19DOI: 10.1038/s44286-025-00337-0
Ryan T. Berry, Phillip Christopher
From vacuum tubes to heterogeneous photocatalysts, the Menzel–Gomer–Redhead mechanism describes how electronically excited molecules behave on surfaces. Advances in LED light sources and photocatalyst design have turned this mechanistic picture into a promising strategy for driving and steering catalytic reactions.
{"title":"Exciting metal surfaces for more than a century","authors":"Ryan T. Berry, Phillip Christopher","doi":"10.1038/s44286-025-00337-0","DOIUrl":"10.1038/s44286-025-00337-0","url":null,"abstract":"From vacuum tubes to heterogeneous photocatalysts, the Menzel–Gomer–Redhead mechanism describes how electronically excited molecules behave on surfaces. Advances in LED light sources and photocatalyst design have turned this mechanistic picture into a promising strategy for driving and steering catalytic reactions.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"3 1","pages":"15-16"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049458","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 : 2025-12-19DOI: 10.1038/s44286-025-00342-3
Andrew S. Rosen
Andrew Rosen discusses the big data demands of artificial intelligence models in atomistic modeling, and the future when benchmarks and dataset sizes begin to plateau.
Andrew Rosen讨论了原子建模中人工智能模型的大数据需求，以及基准测试和数据集规模开始趋于平稳的未来。
{"title":"Beyond big data in quantum chemistry","authors":"Andrew S. Rosen","doi":"10.1038/s44286-025-00342-3","DOIUrl":"10.1038/s44286-025-00342-3","url":null,"abstract":"Andrew Rosen discusses the big data demands of artificial intelligence models in atomistic modeling, and the future when benchmarks and dataset sizes begin to plateau.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"3 1","pages":"79-79"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049445","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 : 2025-12-18DOI: 10.1038/s44286-025-00318-3
Yahao Dai, Henry Chan, Aikaterini Vriza, Jingyuan Fan, Fredrick Kim, Yunfei Wang, Wei Liu, Naisong Shan, Jing Xu, Max Weires, Yukun Wu, Zhiqiang Cao, C. Suzanne Miller, Ralu Divan, Xiaodan Gu, Chenhui Zhu, Sihong Wang, Jie Xu
Artificial intelligence (AI)-powered autonomous experimentation (AE) accelerates materials discovery, but its use for electronic materials is limited by data scarcity from lengthy and complex design–fabricate–test–analyze cycles. Unlike human scientists, even current advanced AI–AE systems lack the adaptability for informative, real-time decisions with limited datasets. Here we developed an AI decision interface featuring an AI advisor for real-time monitoring, analysis and interactive human–AI collaboration, enabling active adaptation to different experimental stages and types. We applied this platform to an important class of electronic materials, mixed ion–electron conducting polymers, to study multiscale morphology and properties. Using organic electrochemical transistors to evaluate the mixed-conducting figure of merit, defined as the product of charge-carrier mobility and volumetric capacitance (μC*), our platform achieved a broad μC* range from 166 to 1,275 F cm−1 V−1 s−1 in just 64 autonomous trials. The analysis identified two key structural factors for higher volumetric capacitance: larger crystalline lamellar spacing and higher specific surface area, and uncovered a previously unknown polymer polymorph. This study reports on an AI-powered autonomous experimentation platform that overcomes data scarcity in electronic materials discovery by using an AI advisor for real-time progress monitoring, data analysis and interactive human–AI collaboration. Applied to mixed ion–electron conducting polymers, it rapidly optimized performance in 64 experimental trials, revealing morphology–property relationships and an unreported polymer polymorph.
人工智能（AI）驱动的自主实验（AE）加速了材料的发现，但其在电子材料中的应用受到冗长而复杂的设计-制造-测试-分析周期的数据稀缺的限制。与人类科学家不同，即使是目前先进的人工智能- ae系统也缺乏对有限数据集的信息实时决策的适应性。在这里，我们开发了一个人工智能决策界面，其中包含一个人工智能顾问，用于实时监控、分析和人机交互协作，能够主动适应不同的实验阶段和类型。我们将该平台应用于一类重要的电子材料，混合离子-电子导电聚合物，以研究多尺度形态和性质。使用有机电化学晶体管来评估混合导电优值，定义为电荷载流子迁移率和体积电容（μC*）的乘积，我们的平台在64次自主试验中实现了从166到1275 F cm−1 V−1 s−1的宽μC*范围。分析确定了高容量电容的两个关键结构因素：更大的晶体片层间距和更高的比表面积，并揭示了以前未知的聚合物多晶型。本研究报告了一个人工智能驱动的自主实验平台，该平台通过使用人工智能顾问进行实时进度监控、数据分析和人机交互协作，克服了电子材料发现中的数据稀缺问题。应用于混合离子-电子导电聚合物，它在64个实验试验中快速优化了性能，揭示了形态-性能关系和未报道的聚合物多晶态。
{"title":"Adaptive AI decision interface for autonomous electronic material discovery","authors":"Yahao Dai, Henry Chan, Aikaterini Vriza, Jingyuan Fan, Fredrick Kim, Yunfei Wang, Wei Liu, Naisong Shan, Jing Xu, Max Weires, Yukun Wu, Zhiqiang Cao, C. Suzanne Miller, Ralu Divan, Xiaodan Gu, Chenhui Zhu, Sihong Wang, Jie Xu","doi":"10.1038/s44286-025-00318-3","DOIUrl":"10.1038/s44286-025-00318-3","url":null,"abstract":"Artificial intelligence (AI)-powered autonomous experimentation (AE) accelerates materials discovery, but its use for electronic materials is limited by data scarcity from lengthy and complex design–fabricate–test–analyze cycles. Unlike human scientists, even current advanced AI–AE systems lack the adaptability for informative, real-time decisions with limited datasets. Here we developed an AI decision interface featuring an AI advisor for real-time monitoring, analysis and interactive human–AI collaboration, enabling active adaptation to different experimental stages and types. We applied this platform to an important class of electronic materials, mixed ion–electron conducting polymers, to study multiscale morphology and properties. Using organic electrochemical transistors to evaluate the mixed-conducting figure of merit, defined as the product of charge-carrier mobility and volumetric capacitance (μC*), our platform achieved a broad μC* range from 166 to 1,275 F cm−1 V−1 s−1 in just 64 autonomous trials. The analysis identified two key structural factors for higher volumetric capacitance: larger crystalline lamellar spacing and higher specific surface area, and uncovered a previously unknown polymer polymorph. This study reports on an AI-powered autonomous experimentation platform that overcomes data scarcity in electronic materials discovery by using an AI advisor for real-time progress monitoring, data analysis and interactive human–AI collaboration. Applied to mixed ion–electron conducting polymers, it rapidly optimized performance in 64 experimental trials, revealing morphology–property relationships and an unreported polymer polymorph.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"2 12","pages":"760-770"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772815","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 : 2025-12-18DOI: 10.1038/s44286-025-00330-7
Yanfei Zhu
{"title":"New shifts in olefin production","authors":"Yanfei Zhu","doi":"10.1038/s44286-025-00330-7","DOIUrl":"10.1038/s44286-025-00330-7","url":null,"abstract":"","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"2 12","pages":"725-725"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772821","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 : 2025-12-18DOI: 10.1038/s44286-025-00333-4
Mo Qiao
{"title":"Casting pathways for net zero","authors":"Mo Qiao","doi":"10.1038/s44286-025-00333-4","DOIUrl":"10.1038/s44286-025-00333-4","url":null,"abstract":"","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"2 12","pages":"727-727"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772823","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 : 2025-12-18DOI: 10.1038/s44286-025-00347-y
Mo Qiao
{"title":"Take it past the limit","authors":"Mo Qiao","doi":"10.1038/s44286-025-00347-y","DOIUrl":"10.1038/s44286-025-00347-y","url":null,"abstract":"","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"2 12","pages":"732-732"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772751","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 : 2025-12-18DOI: 10.1038/s44286-025-00311-w
Alessio Lavino
{"title":"Enzymes can multitask","authors":"Alessio Lavino","doi":"10.1038/s44286-025-00311-w","DOIUrl":"10.1038/s44286-025-00311-w","url":null,"abstract":"","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"2 12","pages":"726-726"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772822","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}
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