Transforming science labs into automated factories of discovery DOI
Angelos Angelopoulos, James F. Cahoon, Ron Alterovitz

и другие.

Science Robotics, Год журнала: 2024, Номер 9(95)

Опубликована: Окт. 23, 2024

Laboratories in chemistry, biochemistry, and materials science are at the leading edge of technology, discovering molecules to unlock capabilities energy, catalysis, biotechnology, sustainability, electronics, more. Yet, most modern laboratories resemble factories from generations past, with a large reliance on humans manually performing synthesis characterization tasks. Robotics automation can enable scientific experiments be conducted faster, more safely, accurately, greater reproducibility, allowing scientists tackle societal problems domains such as health energy shorter timescale. We define five levels laboratory automation, assistance full automation. also introduce robotics research challenges that arise when increasing generality tasks within laboratory. Robots poised transform labs into automated discovery accelerate progress.

Язык: Английский

Chemical Synthesis of Human Proteoforms and Application in Biomedicine DOI Creative Commons
Huasong Ai, Man Pan, Lei Liu

и другие.

ACS Central Science, Год журнала: 2024, Номер 10(8), С. 1442 - 1459

Опубликована: Июль 22, 2024

Limited understanding of human proteoforms with complex posttranslational modifications and the underlying mechanisms poses a major obstacle to research on health disease. This Outlook discusses opportunities challenges

Язык: Английский

Процитировано

13
The relevance of sustainable laboratory practices DOI Creative Commons
Thomas Freese, Nils Elzinga, Matthias Heinemann

и другие.

RSC Sustainability, Год журнала: 2024, Номер 2(5), С. 1300 - 1336

Опубликована: Янв. 1, 2024

Scientists are of key importance to the society advocate awareness climate crisis and its underlying scientific evidence provide solutions for a sustainable future. As much as research has led great achievements benefits, traditional laboratory practices come with unintended environmental consequences. Scientists, while providing problems educating young innovators future, also part problem: excessive energy consumption, (hazardous) waste generation, resource depletion. Through their own operations, science, laboratories have significant carbon footprint contribute crisis. Climate change requires rapid response across all sectors society, modeled by inspiring leaders. A broader community that takes concrete actions would serve an important step in convincing general public similar actions. Over past years, grassroots movements sciences recognized overlooked impact enterprise, so-called Green Lab initiatives emerged seeking address research. Driven voluntary efforts researchers staff, they educate peers, develop sustainability guidelines, write publications maintain accreditation frameworks. With this perspective we want spark leadership promote systemic approach Comprehensive root-causes is presented, expanded data from current case study University Groningen showcasing annual savings 398 763 € well 477.1 tons CO

Язык: Английский

Процитировано

12
Autonomous chemistry: Navigating self-driving labs in chemical and material sciences DOI

Oliver Bayley,

Elia Savino,

Aidan Slattery

и другие.

Matter, Год журнала: 2024, Номер 7(7), С. 2382 - 2398

Опубликована: Июль 1, 2024

Язык: Английский

Процитировано

11
Temperature excavation to boost machine learning battery thermochemical predictions DOI
Yu Wang,

Xuning Feng,

Dongxu Guo

и другие.

Joule, Год журнала: 2024, Номер 8(9), С. 2639 - 2651

Опубликована: Июль 26, 2024

Язык: Английский

Процитировано

11
Impact of modeling and simulation on pharmaceutical process development DOI Creative Commons
Junu Kim, Kozue Okamura, Mohamed Rami Gaddem

и другие.

Current Opinion in Chemical Engineering, Год журнала: 2025, Номер 47, С. 101093 - 101093

Опубликована: Янв. 27, 2025

Язык: Английский

Процитировано

2
Automated Research Platform for Development of Triplet–Triplet Annihilation Photon Upconversion Systems DOI Creative Commons
Paulius Baronas, Justas Lekavičius, Maciej Majdecki

и другие.

ACS Central Science, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Triplet-triplet annihilation photon upconversion (TTA-UC) systems hold great promise for applications in energy, 3D printing, and photopharmacology. However, their optimization remains challenging due to the need precise tuning of sensitizer annihilator concentrations under oxygen-free conditions. This study presents an automated, high-throughput platform discovery TTA-UC systems. Capable performing 100 concentration scans just two hours, generates comprehensive maps critical parameters, including quantum yield, triplet energy transfer efficiency, threshold intensity. Using this approach, we identify key loss mechanisms both established novel At high porphyrin-based concentrations, yield losses are attributed self-quenching via aggregation triplet-triplet (sensitizer-TTA). Additionally, reverse (RTET) at elevated levels increases excitation thresholds. Testing sensitizer-annihilator pairs confirms these mechanisms, highlighting opportunities molecular design improvements. automated offers a powerful tool advancing research other photochemical studies requiring low oxygen levels, intense laser excitation, minimal material use.

Язык: Английский

Процитировано

2
A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand DOI
Tao Song, Man Luo, Xiaolong Zhang

и другие.

Journal of the American Chemical Society, Год журнала: 2025, Номер unknown

Опубликована: Март 8, 2025

The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream chemical research on demand. Here, we report a robotic AI chemist powered by hierarchical multiagent system, ChemAgents, based on-board Llama-3.1-70B LLM, capable executing complex, multistep experiments with minimal human intervention. It operates through Task Manager agent that interacts researchers coordinates four role-specific agents─Literature Reader, Experiment Designer, Computation Performer, Robot Operator─each leveraging one foundational resources: comprehensive Literature Database, extensive Protocol Library, versatile Model state-of-the-art Automated Lab. We demonstrate its versatility efficacy six experimental tasks varying complexity, ranging from straightforward synthesis characterization more complex exploration screening parameters, culminating discovery optimization functional materials. Additionally, introduce seventh task, where ChemAgents is deployed new chemistry lab environment autonomously perform photocatalytic organic reactions, highlighting ChemAgents's scalability adaptability. Our multiagent-driven showcases potential on-demand accelerate democratize access advanced across academic disciplines industries.

Язык: Английский

Процитировано

2
Design Refinement of Catalytic System for Scale-Up Mild Nitrogen Photo-Fixation DOI Creative Commons

Xiao Hu Wang,

Bin Wu,

Yongfa Zhu

и другие.

Nano-Micro Letters, Год журнала: 2025, Номер 17(1)

Опубликована: Март 12, 2025

Abstract Ammonia and nitric acid, versatile industrial feedstocks, burgeoning clean energy vectors hold immense promise for sustainable development. However, Haber–Bosch Ostwald processes, which generates carbon dioxide as massive by-product, contribute to greenhouse effects pose environmental challenges. Thus, the pursuit of nitrogen fixation through carbon–neutral pathways under benign conditions is a frontier scientific topics, with harnessing solar emerging an enticing viable option. This review delves into refinement strategies scale-up mild photocatalytic fixation, fields ripe potential innovation. The narrative centered on enhancing intrinsic capabilities catalysts surmount current efficiency barriers. Key focus areas include in-depth exploration fundamental mechanisms underpinning procedures, rational element selection, functional planning, state-of-the-art experimental protocols understanding photo-fixation valid activity evaluation, design catalysts. Furthermore, offers suite forward-looking recommendations aimed at propelling advancement photo-fixation. It scrutinizes existing challenges prospects within this domain, aspiring equip researchers insightful perspectives that can catalyze evolution cutting-edge methodologies steer development next-generation systems.

Язык: Английский

Процитировано

1
A Universal Approach to Anchoring Chromophores onto Magnetic Scaffold for Achieving Easily Recyclable Heterogeneous Photocatalytic Systems DOI Creative Commons
Xuan Zhan, Yikun Wang, Chenyang Sun

и другие.

Advanced Science, Год журнала: 2025, Номер unknown

Опубликована: Март 26, 2025

Photocatalysis based on chromophores such as porphyrin, coumarin, anthraquinone, and pyrene is a promising technology to achieve green synthesis of various high-value chemicals, but the robust non-covalent immobilization onto light-inert scaffolds for industrialization-oriented heterogeneous photocatalysis remains challenging. In this work, simple universal strategy presented preparing highly efficient recyclable photocatalysts from chromophores, which achieved via biotinylation chromophore molecules subsequent supramolecular binding chromophore-biotin dyads streptavidin-decorated magnetic beads. As an example, commercial beads modified by 5,10,15,20-tetrakis(4-aminophenyl) porphyrin not only possessed remarkable photocatalytic activities oxidative coupling benzylamine derivatives oxidation thioanisole with highest product yields beyond 95% turnover numbers approaching 10000, driven photogenerated reactive oxygen species also demonstrated impressive chemical stability recyclability separation during 10 successive test cycles. The findings revealed in work pave way advancing valuable organic compounds pharmaceutical industry, agricultural sector, etc., rationally designed systems.

Язык: Английский

Процитировано

1
Drug discovery and development in the era of artificial intelligence: From machine learning to large language models DOI Creative Commons
Shenghui Guan, Guanyu Wang

Artificial Intelligence Chemistry, Год журнала: 2024, Номер 2(1), С. 100070 - 100070

Опубликована: Май 9, 2024

Drug Research and Development (R&D) is a complex difficult process, current drug R&D faces the challenges of long time span, high investment, failure rate. Machine learning, with its powerful learning ability to characterize big data networks, increasingly effective improve efficiency success rate R&D. Here we review some recent examples application machine methods in six areas: disease gene prediction, virtual screening, molecule generation, molecular attribute prediction combination synergism. We also discuss advantages integrative multi-attribute prediction. Integrative models based on base learners constructed from different dimensions one hand fully utilize information contained these data, other average performance. Finally, envision new paradigm for discovery development: large language model acts as central hub organize public resources into knowledge base, validating computational software smaller predictive models, well high-throughput automated screening platforms organoidal technologies, speed up development reduce differences efficacy between humans drug.

Язык: Английский

Процитировано

9