Enhancing Lessons on the Internet of Things in Science, Technology, Engineering, and Medical Education with a Remote Lab DOI Creative Commons
Sofia Amador Nelke, Dan Kohen-Vacs,

Michael Khomyakov

и другие.

Sensors, Год журнала: 2024, Номер 24(19), С. 6424 - 6424

Опубликована: Окт. 4, 2024

Integrating remote Internet of Things (IoT) laboratories into project-based learning (PBL) in higher education institutions (HEIs) while exploiting the approach technology-enhanced (TEL) is a challenging yet pivotal endeavor. Our proposed enables students to interact with an IoT-equipped lab locally and remotely, thereby bridging theoretical knowledge practical application, creating more immersive, adaptable, effective experience. This study underscores significance combining hardware, software, coding skills PBL, emphasizing how IoTRemoteLab (the we developed) supports customized educational experience that promotes innovation safety. Moreover, explore potential as TEL, facilitating supporting understanding definition requirements learning. Furthermore, demonstrate incorporate generative artificial intelligence IoTRemoteLab’s settings, enabling personalized recommendations for leveraging or remotely. serves model educators researchers aiming equip essential digital age addressing broader issues related access, engagement, sustainability HEIs. The findings following in-class experiment reinforce value its features preparing future technological demands fostering inclusive, safe, environment.

Язык: Английский

Self-Driving Laboratories for Chemistry and Materials Science DOI Creative Commons
Gary Tom, Stefan P. Schmid, Sterling G. Baird

и другие.

Chemical Reviews, Год журнала: 2024, Номер 124(16), С. 9633 - 9732

Опубликована: Авг. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Язык: Английский

Процитировано

39
Neural Ordinary Differential Equations for Forecasting and Accelerating Photon Correlation Spectroscopy DOI
Andrew H. Proppe, Kin Long Kelvin Lee, Weiwei Sun

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 518 - 524

Опубликована: Янв. 6, 2025

Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments. Photon Fourier spectroscopy (PCFS) one such technique measures time-resolved single-emitter line shapes and offers additional spectral information over Hong–Ou–Mandel two-photon interference but long experimental acquisition times. Here, we demonstrate neural ordinary differential equation model, g2NODE, can forecast complete noise-free interferometry experiment from small subset noisy functions. We this for simulated data, where g2NODE utilizes 10–20 measured functions to create entire denoised interferograms up 200 stage positions, enabling 20-fold speedup in time hours minutes. Our work presents new deep learning approach greatly accelerate use as an characterization tool novel emitter materials.

Язык: Английский

Процитировано

2
Closed-loop transfer enables artificial intelligence to yield chemical knowledge DOI
Nicholas H. Angello, David Friday, Changhyun Hwang

и другие.

Nature, Год журнала: 2024, Номер 633(8029), С. 351 - 358

Опубликована: Авг. 28, 2024

Язык: Английский

Процитировано

13
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

8
Active oversight and quality control in standard Bayesian optimization for autonomous experiments DOI Creative Commons
Sumner B. Harris, Rama K. Vasudevan, Yongtao Liu

и другие.

npj Computational Materials, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 27, 2025

Язык: Английский

Процитировано

1
Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review DOI
Yiming Zhao,

Yongjia Zhao,

Jian Wang

и другие.

Industrial & Engineering Chemistry Research, Год журнала: 2025, Номер 64(9), С. 4637 - 4668

Опубликована: Фев. 24, 2025

This review discusses the transformative impact of convergence artificial intelligence (AI) and laboratory automation on discovery synthesis metal–organic frameworks (MOFs). MOFs, known for their tunable structures extensive applications in fields such as energy storage, drug delivery, environmental remediation, pose significant challenges due to complex processes high structural diversity. Laboratory has streamlined repetitive tasks, enabled high-throughput screening reaction conditions, accelerated optimization protocols. The integration AI, particularly Transformers large language models (LLMs), further revolutionized MOF research by analyzing massive data sets, predicting material properties, guiding experimental design. emergence self-driving laboratories (SDLs), where AI-driven decision-making is coupled with automated experimentation, represents next frontier research. While remain fully realizing potential this synergistic approach, AI heralds a new era efficiency innovation engineering materials.

Язык: Английский

Процитировано

1
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

1
Impact of Host–Emitter Interactions on Light Amplification in Laser Dyes DOI Creative Commons
Masashi Mamada,

Ayano Abe,

Takashi Fujihara

и другие.

Aggregate, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

ABSTRACT Organic lasers hold great promise for enabling a new class of future optoelectronics. Consequently, the development organic semiconductors as gain media has recently been subject significant interest. The molecular design principle based on Einstein coefficients validated achieving high gain, with para ‐phenylene‐vinylene scaffolds recognized one most crucial frameworks. In this study, we develop stilbene tetramer derivative, QSBCz, which significantly increased conjugation compared to highly efficient laser material, BSBCz, resulting in remarkably radiative decay rate and large cross‐section. However, find that optical losses play role light amplification QSBCz. Indeed, comprehensive understanding suppression detrimental loss pathways throughout lasing process are essential, whereas intrinsically associated molecules have not well considered. Although host–guest systems helpful preventing concentration quenching aggregated states, study reveals notable when using common host such 4,4′‐bis(9 H ‐carbazol‐9‐yl)biphenyl (CBP) mCBP. contrast, BSBCz derivative is successfully employed host, leading improved stimulated emission amplification. These findings indicate importance host–emitter interactions properties highlight necessity optimize materials developing dyes.

Язык: Английский

Процитировано

1
Materials Acceleration Platforms (MAPs) Accelerating Materials Research and Development to Meet Urgent Societal Challenges DOI Creative Commons
Simon Stier,

Christoph Kreisbeck,

H. Ihssen

и другие.

Advanced Materials, Год журнала: 2024, Номер 36(45)

Опубликована: Сен. 6, 2024

Abstract Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These drive policy to achieve sustainable, resilient, clean solutions with Advanced (AdMats) for industrial supply chains economic prosperity. The research landscape comprising industry, academe, government identified a critical path accelerate the Green Transition far beyond slow conventional through Digital Technologies that harness Artificial Intelligence, Smart Automation High Performance Computing Acceleration Platforms, MAPs. In this perspective, following short paper, broad overview about addressed, existing projects building blocks of MAPs will be provided while concluding review remaining gaps measures overcome them.

Язык: Английский

Процитировано

8
Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories DOI
Sina Sadeghi, Richard B. Canty,

Nikolai Mukhin

и другие.

ACS Sustainable Chemistry & Engineering, Год журнала: 2024, Номер 12(34), С. 12695 - 12707

Опубликована: Авг. 6, 2024

The accelerating depletion of natural resources undoubtedly demands a radical reevaluation research practices addressing the escalating climate crisis. From traditional approaches to modern-day advancements, integration automation and artificial intelligence (AI)-guided decision-making has emerged as transformative route in shaping new methodologies. Harnessing robotics high-throughput alongside intelligent experimental design, self-driving laboratories (SDLs) offer an innovative solution expedite chemical/materials timelines while significantly reducing carbon footprint scientific endeavors, which could be utilized not only generate green materials but also make process itself more sustainable. In this Perspective, we examine potential SDLs driving sustainability forward through case studies discovery optimization, thereby paving way for greener efficient future. While hold immense promise, discuss challenges that persist their development deployment, necessitating holistic approach both design implementation.

Язык: Английский

Процитировано

5