As an important biomass-derived chemical, D-sorbitol can be converted to a variety of useful chemicals and fuels. Because of the strong nucleophilicity of I − and reducibility of hydrogen iodide (HI), hydroiodic acid has been used for polyol hydrogenolysis with high selectivity. Here, D-sorbitol was converted by HI, rhodium, and hydrogen to give a high yield of iodohexanes (94.2%) at 373 K in a water/cyclohexane biphasic system. Rhodium and hydrogen were used to regenerate HI in situ. The reaction mechanism was studied in detail by using model molecules. The substitution−elimination−addition (SEA) mechanism was proven to be the most possible pathway. The high concentration of both proton and iodide was found to be necessary for the efficient conversion of D-sorbitol and the selective formation of iodohexanes. Besides, water could inhibit the conversion of D-sorbitol dramatically. These findings were explained by kinetic study and demonstrated in the reaction kinetic equation. This catalytic system, including HI and RhCl 3 , was proven to be reusable. In addition, iodohexanes can be quantitatively converted to hexenes over ZrO 2 at 473 K.

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Battery capacity is an important metric for evaluating and predicting the health status of lithium-ion batteries. In order to determine the answer, the battery’s capacity must be, with some difficulty, directly measured online with existing methods. This paper proposes a multi-dimensional health indicator (HI) battery state of health (SOH) prediction method involving the analysis of the battery equivalent circuit model and constant current discharge characteristic curve. The values of polarization resistance, polarization capacitance, and initial discharge resistance are identified as the health indicators reflective of the battery’s state of health. Moreover, the retention strategy genetic algorithm (e-GA) selects the optimal voltage drop segment, and the corresponding equal voltage drop discharge time is also used as a health indicator. Based on the above health indicator selection strategy, a battery SOH prediction model based on particle swarm optimization (PSO) and LSTM neural network is constructed, and its accuracy is validated. The experimental results demonstrate that the suggested strategy is accurate and generalizable. Compared with the prediction model with single health indicator input, the accuracy is increased by 0.79%.