Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
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http://dx.doi.org/10.1021/acs.chemrev.4c00055 | DOI Listing |
Nanoscale
January 2025
Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, Nanjing, 211189, China.
Directional transport of droplets is crucial for industrial applications and chemical engineering processes, with significant potential demonstrated in water harvesting, microfluidics, and heat transfer. In this work, we present a novel approach to induce self-driving behavior in nanodroplets within a two-dimensional (2D) nanochannel using a strain gradient, as demonstrated through molecular dynamics simulations. Our findings reveal that a small strain gradient imposed along a nanochannel constructed by parallel surfaces can induce water transport at ultrafast velocities (O(10 m s)), far exceeding macroscale predictions.
View Article and Find Full Text PDFChimia (Aarau)
December 2024
Department of Quantum Matter Physics, University of Geneva, CH-1211 Geneva, Switzerland.
In this perspective, we will discuss the impact of some of the most recent advancements in materials discovery, particularly focusing on the role of robotics, artificial intelligence, and self-driving laboratories, as well as their implications for the Swiss research landscape. While it seems timely to aim for broad, revolutionary breakthroughs in this field, we argue that more incremental steps - such as, for example, fully automatic grinding of solid powders or fully automated Rietveld refinements - may have a more significant impact on materials discovery, at least in the short run. In the center of these considerations is how small, interdisciplinary groups can drive significant progress by contributing targeted innovations, such as e.
View Article and Find Full Text PDFAdv Mater
November 2024
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China.
Acoustic sensor-based human-machine interaction (HMI) plays a crucial role in natural and efficient communication in intelligent robots. However, accurately identifying and tracking omnidirectional sound sources, especially in noisy environments still remains a notable challenge. Here, a self-powered triboelectric stereo acoustic sensor (SAS) with omnidirectional sound recognition and tracking capabilities by a 3D structure configuration is presented.
View Article and Find Full Text PDFDigit Discov
November 2024
Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
Bayesian optimization (BO) is an efficient method for solving complex optimization problems, including those in chemical research, where it is gaining significant popularity. Although effective in guiding experimental design, BO does not account for experimentation costs: testing readily available reagents under different conditions could be more cost and time-effective than synthesizing or buying additional ones. To address this issue, we present cost-informed BO (CIBO), an approach tailored for the rational planning of chemical experimentation that prioritizes the most cost-effective experiments.
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