September is Blood Cancer Awareness Month, and the Alliance for Clinical Trials in Oncology is highlighting the importance of clinical research that advances treatments, explores earlier cancer detection, and improves the lives of people living with and beyond cancer.

In a major step forward in wearable technology, researchers have used machine learning to reveal how stretching flexible materials impacts their electrical performance. The study shows that pulling a material in one direction can boost conductivity by over 50%, while pulling it in another can hinder it, providing a crucial design guide for future electronic skin and sensors.

Deep fast-charging capability has become the core pursuit for practical applications of aqueous zinc metal batteries, yet it is critically impeded by unfavorable interfacial field evolution at the electrode/electrolyte interface under high-current–density conditions. The coexistence of sluggish Zn²⁺ desolvation, competitive H₂O reduction corrosion, uneven electric field, and concentration field collectively leads to poor rate performance. To tackle these issues, a bolstered interfacial field chemistry strategy was developed via sulfosuccinic acid to precisely regulate the physicochemical and electrochemical properties within the inner Helmholtz plane. The tuned interfacial field comprises a homogeneous distribution of interfacial ions and electric fields, promoting fast desolvation and interfacial electron transfer. Consequently, the modified Zn||Cu asymmetric cells deliver an outstanding average Coulombic efficiency of 99.48% over 1600 cycles at 2 mA cm⁻² and 1 mAh cm⁻². Furthermore, the modified Zn||Zn symmetric cells demonstrate exceptional stability under challenging conditions of 5 mA cm⁻², 2 mAh cm⁻² (over 1600 h), and 10 mA cm⁻², 10 mAh cm⁻² (over 675 h, depth of discharge = 17.08%). Impressively, a substantial cumulative capacity of 3500 mAh cm⁻² is attained at 10 mAh cm⁻² and a 56.93% Zn utilization rate. Besides, the enhanced Zn (10 µm) ||I₂ (10.87 mg cm⁻²) full cell sustains over 1490 cycles at 1 A g⁻¹ with 77.13% capacity retention and a harsh N/P ratio of 2.31. Remarkably, the Zn (10 µm) ||I₂ pouch cell achieves over 680 cycles with an ultralow N/P ratio of 2.04.

An InceptionTime + PBAP deep time-series regression neural network is proposed to construct a nonlinear mapping relationship between electrochemical performance sequences and discharge termination time, providing a data-driven paradigm for catalyst evaluation and future engineering applications of seawater metal air batteries.