A modular autonomous flow reactor combining monitoring technologies with a feedback algorithm is presented for the synthesis of the natural product carpanone. The autonomous self-optimizing system, controlled via MATLAB, was designed as a flexible platform enabling an adaptation of the experimental setup to the specificity of the chemical transformation to be optimized. The reaction monitoring uses either online high pressure liquid chromatography (HPLC) or in-line benchtop nuclear magnetic resonance (NMR) spectroscopy. The custom-made optimization algorithm derived from the Nelder-Mead and golden section search methods performs constrained optimizations of black-box functions in a multidimensional search domain, thereby assuming no a priori knowledge of the chemical reactions. This autonomous self-optimizing system allowed fast and efficient optimizations of the chemical steps leading to carpanone. This contribution is the first example of a multistep synthesis where all discrete steps were optimized with an autonomous flow reactor.
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http://dx.doi.org/10.1021/acs.joc.8b01821 | DOI Listing |
Sensors (Basel)
March 2024
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA.
In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era.
View Article and Find Full Text PDFEntropy (Basel)
June 2023
Nokia Bell Labs, 12 Rue Jean Bart, 91300 Paris, France.
Maintaining and managing ever more complex telecommunication networks is an increasingly difficult task, which often challenges the capabilities of human experts. There is a consensus both in academia and in the industry on the need to enhance human capabilities with sophisticated algorithmic tools for decision-making, with the aim of transitioning towards more autonomous, self-optimizing networks. We aimed to contribute to this larger project.
View Article and Find Full Text PDFMicromachines (Basel)
February 2023
Department of Industrial & Production Engineering, Bangladesh University of Engineering & Technology (BUET), Dhaka 1000, Bangladesh.
Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature.
View Article and Find Full Text PDFACS Nano
June 2022
Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
Animals execute intelligent and efficient interactions with their surroundings through neural pathways, exhibiting learning, memory, and cognition. Artificial autonomous devices that generate self-optimizing feedback mimicking biological systems are essential in pursuing future intelligent robots. Here, we report an artificial neural pathway (ANP) based on a memristor synapse to emulate neuromorphic learning behaviors.
View Article and Find Full Text PDFAnal Bioanal Chem
April 2020
Arbeitskreis Prozessanalytik, Gesellschaft Deutscher Chemiker, 60486, Frankfurt am Main, Germany.
Complex processes meet and need Industry 4.0 capabilities. Shorter product cycles, flexible production needs, and direct assessment of product quality attributes and raw material attributes call for an increased need of new process analytical technologies (PAT) concepts.
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