While many approaches to predict aqueous pK a values exist, the fast and accurate prediction of non-aqueous pK a values is still challenging. Based on the iBonD experimental pK a database (39 solvents), ah olistic pK a prediction model was established using machine learning.S tructural and physical-organic-parameter-based descriptors (SPOC) were introduced to represent the electronic and structural features of the molecules.The models trained with aneural network or the XGBoost algorithm showed the best prediction performance with alow MAE value of 0.87 pK a units.The approachallows ac omprehensive mapping of all possible pK a correlations between different solvents and it was validated by predicting the aqueous pK a and micro-pK a of pharmaceutical molecules and pK a values of organocatalysts in DMSO and MeCN with high accuracy.Anonline prediction platform was constructed based on the current model, which can providepK a prediction for different types of XÀHacidity in the most commonly used solvents.

3D thick electrode design is a promising strategy to increase the energy density of lithium-ion batteries but faces challenges such as poor rate and limited cycle life. Herein, a coassembly method is employed to construct low-tortuosity, mechanically robust 3D thick electrodes. LiFe 0.7 Mn 0.3 PO 4 nanoplates (LFMP NPs) and graphene are aligned along the growth direction of ice crystals during freezing and assembled into sandwich frameworks with vertical channels, which prompts fast ion transfer within the entire electrode and reveals a 2.5-fold increase in ion transfer performance as opposed to that of random structured electrodes. In the sandwich framework, LFMP NPs are entrapped in the graphene wall in a "plate-on-sheet" contact mode, which avoids the detachment of NPs during cycling and also constitutes electron transfer highways for the thick electrode. Such vertical-channel sandwich electrodes with mass loading of 21.2 mg cm −2 exhibit a superior rate capability (0.2C-20C) and ultralong cycle life (1000 cycles). Even under an ultrahigh mass loading of 72 mg cm −2 , the electrode still delivers an areal capacity up to 9.4 mAh cm −2 , ≈2.4 times higher than that of conventional electrodes. This study provides a novel strategy for designing thick electrodes toward high performance batteries.

The results indicated that the proposed framework could be used for this PET scanner with improved image quality. This method could also be applied to other state-of-the-art whole-body PET scanners and preclinical PET scanners with a similar shape.

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