The borylation of unreactive carbon-hydrogen bonds is a valuable method for transforming feedstock chemicals into versatile building blocks. Here, we describe a transition metal-free method for the photoredox-catalyzed borylation of unactivated C(sp3)−H bond, initiated by 1,5-hydrogen atom transfer (HAT). The remote borylation was directed by 1,5-HAT of the amidyl radical, which was generated by photocatalytic reduction of hydroxamic acid derivatives. The method accommodates substrates with primary, secondary and tertiary C(sp3)−H bonds, yielding moderate to good product yields (up to 92%) with tolerance for various functional groups. Mechanistic studies, including radical clock experiments and DFT calculations, provided detailed insight into the 1,5-HAT borylation process.

We describe a transition-metal free, photoredox-catalyzed borylation method of unactivated C(sp3)-H bonds via initiated 1,5-hydrogen atom transfer (HAT). The remote borylation was directed by 1,5-HAT of the amidyl radical, which was generated by photocatalytic reduction of hydroxamic acid derivatives. Substrates bearing primary, secondary and tertiary C(sp3)-H bonds could all be accommodated in this remote borylation protocol, giving moderate to good yields of the desired products (up to 92%), and a series of functional groups were tolerated. Mechanistic studies, including radical clock experiments and DFT calculations, gave detailed insight into the 1,5-HAT borylation process.

Particle size inversion of dynamic light scattering (DLS) is a typically ill-posed problem. Regularization is an effective method to solve the problem. The regularization involves imposing constraints on the fitted autocorrelation function data by adding a norm. The classical regularization inversion for DLS data is constrained by the L2 norm. In the optimization equation, the norm determines the smoothness and stability of the inversion result, affecting the inversion accuracy. In this paper, the Lp norm regularization model is constructed. When p is 1, 2, 10, 50, 100, 1000, and ∞, respectively, the influence of their norm models on the inversion results of data with different noise levels is studied. The results prove that overall, the inversion distribution errors show a downward trend with the increase of p. When p is larger than 10, there is no significant difference in distribution error. Compared with L2, L∞ can provide better performance for unimodal particles with strong noise, although this does not occur in weak noise cases. Meanwhile, L∞ has lower sensitivity to noise and better peak resolution, and its inverse particle size distribution is closer to the true distribution for bimodal particles. Thus, L∞ is more suitable for the inversion of DLS data.

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