An aqueous electrolytic MnO 2 −Zn battery with eyecatching Mn 2+ /MnO 2 cathode chemistry has been attracting immense interest for next-generation energy storage devices due to its irreplaceable advantages. However, the limited MnO 2 conductivity restricts its long service life at high areal capacities. Here, we report a high-performance electrolytic MnO 2 −Zn battery via a bromine redox mediator, to enhance its electrochemical performance. The MnO 2 /Br 2 −Zn battery displays a high discharge voltage of 1.98 V with a capacity of ∼5.8 mAh cm −2 . It also shows an excellent rate performance of 20 C with a long-term stability of over 600 cycles. Furthermore, the scaled-up MnO 2 /Br 2 −Zn battery with a capacity of ∼950 mAh exhibits a stable 100 cycles with a practical cell energy density of ∼32.4 Wh kg −1 and an attractively low energy cost of below 15 US$ kWh −1 . The design approach can be generalized to other electrodes and battery systems, thus opening up new possibilities for large-scale energy storage.

The development of Zn-free anodes to inhibit Zn dendrite formation and modulate high-capacity Zn batteries is highly applauded yet very challenging. Here, we design a robust two-dimensional antimony/antimony-zinc alloy heterostructured interface to regulate Zn plating. Benefiting from the stronger adsorption and homogeneous electric field distribution of the Sb/Sb2Zn3-heterostructured interface in Zn plating, the Zn anode enables an ultrahigh areal capacity of 200 mAh cm−2 with an overpotential of 112 mV and a Coulombic efficiency of 98.5%. An anode-free Zn-Br2 battery using the Sb/Sb2Zn3-heterostructured interface@Cu anode shows an attractive energy density of 274 Wh kg−1 with a practical pouch cell energy density of 62 Wh kg−1. The scaled-up Zn-Br2 battery in a capacity of 500 mAh exhibits over 400 stable cycles. Further, the Zn-Br2 battery module in an energy of 9 Wh (6 V, 1.5 Ah) is integrated with a photovoltaic panel to demonstrate the practical renewable energy storage capabilities. Our superior anode-free Zn batteries enabled by the heterostructured interface enlighten an arena towards large-scale energy storage applications.

Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman signal is weak and due to the label-free character of RS, the Raman data is untargeted. Therefore, the analysis of Raman spectra is challenging and machine learning based chemometric models are needed. As a subset of representation learning algorithms, deep learning (DL) has had great success in data science for the analysis of Raman spectra and photonic data in general. In this review, recent developments of DL algorithms for Raman spectroscopy and the current challenges in the application of these algorithms will be discussed.