SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4279-0
Inkjet printing has emerged as a viable additive manufacturing route for organic light-emitting diodes (OLEDs), offering drop-on-demand patterning, high material utilization, and compatibility with large-area flexible substrates. This review critically examines the formulation science, printhead physics, and drying kinetics that govern the quality of inkjet-printed organic layers. We analyze the rheological window required for stable jetting, typically 1–20 mPa·s viscosity and 25–45 mN/m surface tension, and the dimensionless Ohnesorge number (0.1 < Z < 1) that defines satellite-free droplet formation. The coffee-ring effect, driven by capillary flow and solvent evaporation gradients, remains the dominant failure mode for pixel non-uniformity; binary solvent systems and substrate temperature control (40–60 °C) mitigate this. We survey recent progress in printed hole-transport, emissive, and electron-transport layers, with particular attention to cross-linkable hole-transport materials that resist interlayer dissolution. Device performance metrics from printed OLEDs now reach external quantum efficiencies of 15–20% for fluorescent emitters and >25% for phosphorescent systems, with operating lifetimes (T95) exceeding 1,000 hours at 1,000 cd/m². We identify remaining bottlenecks: nozzle clogging from aggregated nanoparticles, film thickness variation across large panels, and the absence of standardized ink formulations. The review concludes with a roadmap for industrial adoption, emphasizing in-line metrology and closed-loop process control as prerequisites for yield parity with vacuum-deposited OLEDs.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3534-0
Lithium metal anodes (LMAs) offer a theoretical capacity of 3860 mAh g−1 and a redox potential of −3.04 V vs. SHE, yet uncontrolled dendrite growth and infinite volume expansion during plating/stripping degrade cycling stability, particularly at high current densities. This study introduces a three-dimensional lithiophilic host fabricated by incorporating ZnO/ZnSe heterostructures onto brass fibers (ZnO/ZnSe@Brass). The hierarchical architecture mitigates volume expansion and reduces local current density during lithiation. The uniformly distributed ZnO/ZnSe acts as a lithiophilic skin, promoting smooth and dense Li deposition. In situ formed solid electrolyte interphase (SEI), enriched with Li2Se and Li2O, provides high ionic conductivity and mechanical robustness, accelerating ion transport and charge transfer kinetics. Symmetric cells with the ZnO/ZnSe@Brass host exhibit cycling stability exceeding 10,000 cycles at 20 mA cm−2 and 1 mAh cm−2, and sustain fast charging at an ultra-high current density of 80 mA cm−2. When paired with LiFePO4, full cells deliver >500 cycles at 2 C and superior rate capability. The ZnO/ZnSe@Brass host design offers a viable pathway for advanced LMAs in fast-charging lithium metal batteries.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3524-y
The 12-lead electrocardiogram (ECG) is indispensable for the initial diagnosis of cardiac conditions, yet existing neuromorphic hardware for multi-lead ECG monitoring requires multiple array circuits and two operational processes, imposing severe constraints on device consistency and diagnostic accuracy. This study introduces a neuromorphic parallel computing hardware architecture based on quantum dot synaptic transistors that leverages trap and surface electric field effects to enable 12-lead ECG monitoring within a single array circuit, eliminating the need for twelve separate circuits. The system concurrently processes multiple ECG signals and produces final outputs without external computing or control circuits. A 12-transistor array, termed STAC, directly processes one-dimensional ECG data without additional conversion circuits, integrating a feature extraction layer at the pixel level and a feature fusion layer at the circuit level. Classification of ECG signals from the MIT-BIH Arrhythmia Database and the Chinese Twelve-Lead ECG Challenge Database yields a training accuracy exceeding 98%. A five-class ECG signal classification task achieves 96.2% recognition accuracy, with a 5×5 confusion matrix confirming high classification precision across normal (N) and four abnormal categories (A, V, L, R). The architecture accurately detects myocardial infarction by fine-tuning internal weights, demonstrating proficiency in monitoring abnormal ECG signals. This advancement offers a compact, low-cost solution for wearable and portable 12-lead ECG monitoring devices, enabling real-time cardiac assessment with reduced hardware complexity and enhanced diagnostic reliability.