Data-guided rational design of additives for halogenation of highly fluorinated naphthalenes: integrating fluorine chemistry and machine learning

Naoya Ohtsuka, Muhammad Zhafran Mohd Aris, Toshiyasu Suzuki and Norie Momiyama

Highly fluorinated aromatic compounds exhibit unique electronic structures, however their selective transformation remains a longstanding challenge. Halogenation of F7 naphthalene previously required low temperatures (40 to 0 1C) for high yields, while room-temperature reactions suffered from side reactions and decomposition. Here we present a data-guided framework for rational additive design enabling efficient halogenation under ambient conditions. Screening 25 functional additives revealed distinct groups, with effective ones affording halogenated products in yields above 50% and recovering in high rates. Machine learning models built from DFT-derived descriptors achieved...
続きを読む

Data-Integrated Elucidation of Structure–Activity Relationships toward the Rational Design of Perfluoroiodoarene-Based Halogen-Bond Donor Catalysts

Masayuki Kato, Fumio Nakashima, Naoya Ohtsuka, Yukina Nishioka, Atsuto Izumiseki, Takeshi Fujinami. Shunya Oishi, Toshiyasu Suzuki, and Norie Momiyama

Understanding and predicting catalyst performance from structural and electronic information remains a central challenge in organocatalysis. Here, we present a data-integrated framework that quantitatively combines experimental, computational, and machine learning (ML) approaches to elucidate the structure–activity relationships of halogen-bond (XB) donor catalysts based on perfluoroiodoarene cores. Single-crystal X-ray diffraction and chloride-binding analyses revealed that linker-containing two-point donors exhibit significantly stronger binding and higher catalytic activity than one-point donors. Through density functional theory calculations and an ML regression analysis integrating crystallographic and electronic descriptors, ...
続きを読む

Machine Learning Prediction of Au(III) Extractability of Various Organic Solvents Based on Ion Solvation in Hydrochloric Acid Media

​Tatsuya Oshima, Yuhi Iwakiri, Hiroki Yokota, and Asuka Inada

Selective recovery of gold from waste electrical and electronic equipment has attracted increasing attention. Au(III), which is present as tetrachloroauric acid (HAuCl4) in hydrochloric acid media, can be extracted by using various organic solvents, such as ketones and ethers. However, the decisive factors of the solvent for predicting Au(III) extractability have not been confirmed. In the present study, the relationship between the extraction percentage of Au(III) and the properties of the solvent was investigated for 79 types of solvents. Based on the relationships between the Hansen solubility parameters of the solvent and the extraction percentage of Au(III) in 5.0 M HCl with a threshold of 80%, the extractability was classified with 93.7% accuracy...
続きを読む

Synthesis of Halogen-Bond-Donor-Site-Introduced Functional Monomers through Wittig Reaction of Perfluorohalogenated Benzaldehydes: Toward Digitalization as Reliable Strategy in Small-Molecule Synthesis

Tatsuaki Hori, Shuya Kakinuma, Naoya Ohtsuka, Takeshi Fujinami, Toshiyasu Suzuki, and Norie Momiyama

The Wittig reaction of perfluoromonohalobenzaldehydes was systematically studied to synthesize 2,3,5,6-tetrafluoro-4-halostyrene (TFXSs) as functional monomers bearing halogen-bond donor sites. The reaction proceeded efficiently in tetrahydrofuran using 1,1,3,3-tetramethylguanidine as an organic base. Correlation analysis quantitatively identified three key factors required to obtain TFXSs in reasonable yields. The present approach not only contributes to the study of halogen-bond-based functional molecules, but also presents digitalization as a potential strategy in small-molecule synthesis.
続きを読む