Photochemistry with Cyanines in the Near Infrared: A Step to Chemistry 4.0 Technologies
| Autor: | Bernd Strehmel, Christian Schmitz, Jost Göttert, Kevin Cremanns |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
photochemistry
Activation barrier 010405 organic chemistry business.industry Chemistry High intensity Concept Organic Chemistry Near-infrared spectroscopy near infrared Artificial Intelligence | Hot Paper Nanotechnology General Chemistry 010402 general chemistry artificial intelligence 01 natural sciences Catalysis 0104 chemical sciences chemistry 4.0 Radiative transfer absorber Photonics business Absorption (electromagnetic radiation) Concepts |
| Zdroj: | Chemistry (Weinheim an Der Bergstrasse, Germany) |
| ISSN: | 1521-3765 0947-6539 |
| Popis: | Cyanines covering the absorption in the near infrared (NIR) are attractive for distinct applications. They can interact either with lasers exhibiting line‐shaped focus emitting at both 808 and 980 nm or bright high intensity NIR‐LEDs with 805 nm emission, respectively. This is drawing attention to Industry 4.0 applications. The major deactivation occurs through a non‐radiative process resulting in the release of heat into the surrounding, although a small fraction of radiative deactivation also takes place. Most of these NIR‐sensitive systems possess an internal activation barrier to react in a photonic process with initiators resulting in the generation of reactive radicals and acidic cations. Thus, the heat released by the NIR absorber helps to bring the system, consisting of an NIR sensitizer and initiator, above such internal barriers. Molecular design strategies making these systems more compatible with distinct applications in a certain oleophilic surrounding are considered as a big challenge. This includes variations of the molecular pattern and counter ions derived from super acids exhibiting low coordinating properties. Further discussion focusses on the use of such systems in Chemistry 4.0 related applications. Intelligent software tools help to improve and optimize these systems combining chemistry, engineering based on high‐throughput formulation screening (HTFS) technologies, and machine learning algorithms to open up novel solutions in material sciences. Applied artificial intelligence: Machine learning will help chemistry to develop new materials for photochemical sciences and will direct chemistry in new directions with digitalization. |
| Databáze: | OpenAIRE |
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