There are limited naturally derived protein biomaterials for the available medical implants. High cost, low yield, and batch-to-batch inconsistency, as well as intrinsically differing bioactivity in some of the proteins, make them less beneficial as common implant materials compared to their synthetic counterparts. Here, we present a milk-derived whey protein isolate (WPI) as a new kind of natural protein-based biomaterial for medical implants. The WPI was methacrylated at 100 g bench scale, >95% conversion, and 90% yield to generate a photo-cross-linkable material. WPI-MA was further processed into injectable hydrogels, monodispersed microspheres, and patterned scaffolds with photo-cross-linking-based advanced processing methods including microfluidics and 3D printing. In vivo evaluation of the WPI-MA hydrogels showed promising biocompatibility and degradability. Intramyocardial implantation of injectable WPI-MA hydrogels in a model of myocardial infarction attenuated the pathological changes in the left ventricle. Our results indicate a possible therapeutic value of WPI-based biomaterials and give rise to a potential collaboration between the dairy industry and the production of medical therapeutics.

Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety. However, a lack of reliable cathode materials has largely pledged their applications. Herein, a machine learning (ML)-based approach to predict cathodes with high capacity (>100 mAh g −1 ) and high voltage (>0.5 V) is developed. Over ≈130 000 inorganic materials from the materials project database are screened and the crystal graph convolutional neural network based ML approach is applied with data from the AFLOW database, the combination of these two gives rise to ≈80 predicted cathode materials. Among them, ≈10 cathode materials have been experimentally discovered previously, which agrees remarkably well with experimental measurements, while ≈70 new promising candidates have been predicted for further experimental validations. The authors hope this study could spur further interests in ML-based advanced theoretical tools for battery materials discovery.

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Zn-ion batteries with low cost and high safety have been regarded as a promising energy storage technology for grid storage. It is well-known that the metal anode surface orientation is vital to its reversibility. Herein, we demonstrate a facile route to control the Zn metal anode surface orientation through electrodeposition with electrolyte additives. An ultrathin (101)-inclined Zn metal anode (down to 2 μm) is obtained by adding a small amount of dimethyl sulfoxide (DMSO) in the ZnSO 4 aqueous electrolyte. Scanning electron microscopy indicates the formation of flat terrace-like surfaces, while in situ optical observations demonstrate the reversible plating and stripping. DFT calculations reveal that the large reconstruction of the Zn-( 101) surface with DMSO and H 2 O adsorption to lower the interface energy is the main driving force for surface preference. Raman, XPS, and ToF-SIMS characterizations are performed to unveil the surface SEI components. Exceptional electrochemical performance is demonstrated for the (101)-inclined Zn metal anode in a half cell, which could cycle for 200 h with a low overpotential (<50 mV). The Zn||V 2 O full cells are assembled, showing much better cycle performance for the 5 μm (101)-inclined Zn metal anode as compared to the commercialized 10 μm Zn metal foil, with a maximum specific capacity of 359 mAh/g and >170 mAh/g after over 300 cycles. We hope this study will spur further interest in the control of surface crystallographic orientation for a stable ultrathin Zn metal anode.