Aqueous Zn‐ion batteries (ZIBs) have emerged as a promising energy supply for next‐generation wearable electronics, yet they are still impeded by the notorious growth of zinc dendrite and uncontrollable side reaction. While the rational design of electrolyte composition or separator decoration can effectively restrain zinc dendrite growth, synchronously regulating the interfacial electrochemical performance by tackling the physical delamination venture between electrode and electrolyte remains a major obstacle for high‐performance wearable aqueous ZIB. Herein, a category of hybrid biogel electrolyte containing carrageenan and wool keratin (CWK) is put forward to regulate the interfacial electrochemistry in aqueous ZIB. Systematic electrochemical kinetics analyses and ex situ scanning electrochemical microscopy (SECM) characterizations achieve comprehensive understanding of the keratin enhanced interfacial Zn2+ redox reaction. Thanks to the keratin triggered selective ion permeability, the as‐designed CWK hybrid biogel electrolyte manifests a promoted Zn2+ transference number and excellent reversibility of Zn plating/stripping and outstanding Zn utilization (average Coulombic efficiency ≈98%). More impressively, the CWK hybrid biogel electrolyte also demonstrates cathode side‐reaction depression and strengthened interfacial adhesion while assembled into a quasi‐solid‐state flexible ZIB. This work offers a strategy to synchronously solve concurrent challenges for both of Zn anode and cathode toward realistic wearable aqueous ZIB.
The
interactions between ions and the low-dimensional halide perovskites
are critical to realizing the next-generation energy storage devices
such as photorechargeable ion batteries and ion capacitors. In this
study, we performed high-throughput calculations and machine-learning
analysis for ion adsorption on two-dimensional A2BX4 halide perovskites. The first-principles calculations obtained
an initial data set containing adsorption energies of 640 compositionally
engineered ion/perovskite systems with diverse ions including Li+, Zn2+, K+, Na+, Al3+, Ca2+, Mg2+, and F–. The
machine learning algorithms including k-nearest neighbors (KNN), Kriging,
Random Forest, Rpart, SVM, and Xgboost algorithms were compared, and
the Xgboost algorithm achieved the best accuracy (r = 0.97, R
2 = 0.93) and was selected
to predict the virtual design space consisting of 11 976 ion/perovskite
systems. The features were then analyzed and ranked according to their
Pearson correlations to the output values. In particular, to better
understand the features, diverse feature selection methods were employed
to comprehensively evaluate the features. The machine-learning-predicted
virtual design space was subsequently screened to select stable lead-free
ion/perovskite systems with suitable band gaps and halogen mixing
features. The present study provides a theoretical foundation to design
halide perovskite materials for ion-based energy storage applications
such as secondary ion batteries, ion capacitors, and solar-rechargeable
batteries.
Halide perovskite materials exhibit poor optoelectronic properties under hostile conditions, such as water, therefore interpretable machine learning models of halide perovskite systems under hostile conditions are necessary. In this article, we employ machine learning methods to explore cosensitizer-based halide perovskite films in aqueous solution, with the introduction of multiple types of light-absorbing molecules to modify the perovskite/TiO 2 interface. Chemical insights are provided based on the analysis of the molecular descriptors, suggesting the importance of the electrostatic and electrotopological features as well as the chemical compositions and functional groups of the interlayer molecules. Experiments are carried out to validate the machine learning model, and fair agreements between the machine learning model and the experimental results are achieved; this leads to the identification of alternative cosensitizers that offer promising optoelectronic properties in aqueous solution. This article highlights the model interpretability to predict and understand the halide perovskitebased films, and the approach outlined here can be generalized to design other surface systems.
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