Porphyrins and related families of molecules are important organic modules as has been reflected in the award of the Nobel Prizes in Chemistry in 1915, 1930, 1961, 1962, 1965, and 1988 for work on porphyrin-related biological functionalities. The porphyrin core can be synthetically modified by introduction of various functional groups and other elements, allowing creation of numerous types of porphyrin derivatives. This feature makes porphyrins extremely useful molecules especially in combination with their other interesting photonic, electronic and magnetic properties, which in turn is reflected in their diverse signal input–output functionalities based on interactions with other molecules and external stimuli. Therefore, porphyrins and related macrocycles play a preeminent role in sensing applications involving chromophores. In this review, we discuss recent developments in porphyrin-based sensing applications in conjunction with the new advanced concept of nanoarchitectonics, which creates functional nanostructures based on a profound understanding of mutual interactions between the individual nanostructures and their arbitrary arrangements. Following a brief explanation of the basics of porphyrin chemistry and physics, recent examples in the corresponding fields are discussed according to a classification based on physical modes of detection including optical detection (absorption/photoluminescence spectroscopy and energy and electron transfer processes), other spectral modes (circular dichroism, plasmon and nuclear magnetic resonance), electronic and electrochemical modes, and other sensing modes.

This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using a molecule generator and a DFT simulator, and attempted to generate novel photofunctional molecules whose lowest excited states lie at desired energetic levels. A 10 day run on the 12-core server discovered 86 potential photofunctional molecules around target lowest excitation levels, designated as 200, 300, 400, 500, and 600 nm. Among the molecules discovered, six were synthesized, and five were confirmed to reproduce DFT predictions in ultraviolet visible absorption measurements. This result shows the potential of AI-assisted chemistry to discover ready-to-synthesize novel molecules with modest computational resources.

A copper(II)-oxide-based exhaust catalyst exhibits better activity than Pt- and Rh-nanoparticle catalysts in NO remediation at 175 °C. Following theoretical design, the CuO catalyst is rationally prepared; CuO nanoplates bearing a maximized amount of the active {001} facet are arranged in interleaved layers. A field test using a commercial gasoline engine demonstrates the ability of the catalyst to remove NO from the exhaust of small vehicles.