Revealing the Structure and Reactivity of the Active Species in the FeCl₂–TBHP System: Case Study on Alkene Oxidation

A mechanism involving a quintet ferryl [Fe^IVO] species has been disclosed for the oxidation of alkene under the FeCl₂–TBHP system. Both theoretical and experimental results suggested that the quintet ferryl species [Fe^IVO] was produced by the reaction of FeCl₂ with TBHP. A Mulliken atomic spin density distribution on [Fe^IVO] showed that the O site has strong radical character and could easily react with alkene to form a carbon radical intermediate. This radical could be further oxidized by TBHP to lead to the CC bond cleavage of alkene

Direct Counting and Imaging Chain Lengths of Lipids by Stimulated Raman Scattering Microscopy

Direct counting and mapping the chain lengths of fatty acids on a microscopic scale are of particular importance but remain an unsolvable challenge. Although the current hyperspectral stimulated Raman scattering (SRS) microscopy has gained exceptional capability in chemical imaging of the degree of desaturation, the complete lipid characterization, including the carbon chain length quantification, is awaiting a major breakthrough. Here, we pushed the spectral resolution limit of hyperspectral SRS microscopy to 5.4 cm–1 by employing a highly efficient spectral compressor, which achieved spectral narrowing of the fs laser without much energy loss. The SRS imaging with such high spectral resolution enabled us to differ eight types of saturated lipids with carbon chain lengths from C8:0 to C22:0 by interrogating their subtly red-shifting Raman bands of alkyl C–C gauche stretches between 1070 and 1110 cm–1. The SRS microscopy with superior spectral resolution will pave the way for comprehensive lipid characterization and contribute to uncovering the abnormal pathways of lipid metabolism in cancer

The optimal solutions under different RPDIs.

During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.</div

The value range and interval radius of moxibustion parameters.

The value range and interval radius of moxibustion parameters.</p

Latin sampling.

This is the simulation data and the fitting data obtained according to the Latin sampling results. We also process the data generated by simulation and fitting. (XLSX)</p

The Latin hypercube sampling.

During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.</div

COMSOL simulation model.

During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.</div

Single factor analysis.

This is the data from our single-factor analysis of the four operational parameters. (XLSX)</p

The optimal solutions under different RPDIs.

During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.</div

Fig 7 -

(a) Temperature curve at moxibustion point with different moxa radius. (b) Temperature curve at 5mm under moxibustion point with different moxa radius.</p