Data-Driven Insight into the Reductive Stability of Ion-Solvent Complexes in Lithium Battery Electrolytes.

Lithium (Li) metal batteries (LMBs) are regarded as one of the most promising energy storage systems due to their ultrahigh theoretical energy density. However, the high reactivity of the Li anodes leads to the decomposition of the electrolytes, presenting a huge impediment to the practical application of LMBs. The routine trial-and-error methods are inefficient in designing highly stable solvent molecules for the Li metal anode. Herein, a data-driven approach is proposed to probe the origin of the reductive stability of solvents and accelerate the molecular design for advanced electrolytes. A large database of potential solvent molecules is first constructed using a graph theory-based algorithm and then comprehensively investigated by both first-principles calculations and machine learning (ML) methods. The reductive stability of 99% of the electrolytes decreases under the dominance of ion-solvent complexes, according to the analysis of the lowest unoccupied molecular orbital (LUMO). The LUMO energy level is related to the binding energy, bond length, and orbital ratio factors. An interpretable ML method based on Shapley additive explanations identifies the dipole moment and molecular radius as the most critical descriptors affecting the reductive stability of coordinated solvents. This work not only affords fruitful data-driven insight into the ion-solvent chemistry but also unveils the critical molecular descriptors in regulating the solvent's reductive stability, which accelerates the rational design of advanced electrolyte molecules for next-generation Li batteries.

Probing the Origin of Viscosity of Liquid Electrolytes for Lithium Batteries.

Viscosity is an extremely important property for ion transport and wettability of electrolytes. Easy access to viscosity values and a deep understanding of this property remain challenging yet critical to evaluating the electrolyte performance and tailoring electrolyte recipes with targeted properties. We proposed a screened overlapping method to efficiently compute the viscosity of lithium battery electrolytes by molecular dynamics simulations. The origin of electrolyte viscosity was further comprehensively probed. The viscosity of solvents exhibits a positive correlation with the binding energy between molecules, indicating viscosity is directly correlated to intermolecular interactions. Salts in electrolytes enlarge the viscosity significantly with increasing concentrations while diluents serve as the viscosity reducer, which is attributed to the varied binding strength from cation-anion and cation-solvent associations. This work develops an accurate and efficient method for computing the electrolyte viscosity and affords deep insight into viscosity at the molecular level, which exhibits the huge potential to accelerate advanced electrolyte design for next-generation rechargeable batteries.

An Electrocatalytic Model of the Sulfur Reduction Reaction in Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) battery is strongly considered as one of the most promising energy storage systems due to its high theoretical energy density and low cost. However, the sluggish reduction kinetics from Li2 S4 to Li2 S during discharge hinders the practical application of Li-S batteries. Although various electrocatalysts have been proposed to improve the reaction kinetics, the electrocatalytic mechanism is unclear due to the complexity of sulfur reduction reactions (SRR). It is crucial to understand the electrocatalytic mechanism thoroughly for designing advanced electrocatalysts. Herein an electrocatalytic model is constructed to reveal the chemical mechanism of the SRR in Li-S batteries based on systematical density functional theory calculations, taking heteroatoms-doped carbon materials as an example. The adsorption energy of LiSy ⋅ (y=1, 2, or 3) radicals is used as a key descriptor to predict the reaction pathway, rate-determining step, and overpotential. A diagram for designing advanced electrocatalysts is accordingly constructed. This work establishes a theoretical model, which is an intelligent integration for probing the complicated SRR mechanisms and designing advanced electrocatalysts for high-performance Li-S batteries.

Unveiling the unprecedented catalytic capability of micro-sized Co-ZIF-L for the thermal decomposition of RDX by 2D-structure-induced mechanism reversal.

Developing MOF-based catalysts with superior catalytic properties for the thermal decomposition of cyclotrimethylenetrinitramine (RDX) is significant for the application of novel and efficient combustion catalysts oriented to RDX-based propellants with excellent combustion performance. Herein, micro-sized Co-ZIF-L with a star-like morphology (SL-Co-ZIF-L) was found to exhibit unprecedented catalytic capability for the decomposition of RDX, which can lower the decomposition temperature of RDX by 42.9 °C and boost the heat release by 50.8%, superior to that of all the ever-reported MOFs and even ZIF-67, which has similar chemical composition but a much smaller size. In-depth mechanism study from both experimental and theoretical views reveals that the weekly interacted 2D layered structure of SL-Co-ZIF-L could activate the exothermic C-N fission pathway for the decomposition of RDX in the condensed phase, thus reversing the commonly advantageous N-N fission pathway and promoting the decomposition process in the low-temperature stage. Our study reveals the unusually superior catalytic capability of micro-sized MOF catalysts and sheds light on the rational structure design of catalysts used in micromolecule transformation reactions, typically the thermal decomposition of energetic materials.

[Symptomatic Profiles of 173 Chinese Patients with Essential Thrombocythemia].

OBJECTIVE: To explore the symptomatic burden of patients with essential thrombocythemia (ET) and its relation with clinical characteristics including the mutation status, therapeutic protocols and sex. METHODS: Total of 173 Chinese ET patients were selected and grouped on the basis of disease characteristics (mutation status, therapeutic pro to- cols, and sex). RESULTS: All the groups showed low-to-high symptom burden, with the highest in the Hu (hydroxyurea)-group (total symptom score [TSS], 14.7; range, 7.6-14.7). In the JAK2V617F-positive, Hu-treated, and female groups TSS and independent symptom scores were higher than those in the control group. The CALR-positive and IFN-α-treated groups had lower overall and individual scores as compared with groups lacking the corresponding characteristics. As the number of characteristics (JAK2V617F-positive, Hu-treated, and female) increases, the severity of symptoms gradually increased. CONCLUSION: The different characteristics have various effects on symptom burden in ET patients. The accumulation of certain characteristics will lead to more severe symptom burden, thus the patient＇s symptom burden should be considered comprehensively when making up the treatment schemes and prognosis.