Reversible optical data storage and encryption enabled by phase-change and hydrogel integration
Peer-Reviewed Publication
Updates every hour. Last Updated: 27-Oct-2026 01:16 ET (27-Oct-2026 05:16 GMT/UTC)
Phase-change materials and hydrogels emerge as versatile, low-cost, high-speed platforms for next-generation optical information storage and multi-level encryption. NDAO Lab (Nano Devices and Applied Optics) at UC San Diego developed an optical storage system combining laser-written phase-change antimony trisulfide (Sb₂S₃) with a humidity-responsive CMC-N₃ hydrogel to enable full-color image multiplexing. This scalable approach overcomes limitations of metasurface-based storage, offering rewritable, environmentally responsive optical memory for data security, anti-counterfeiting, and adaptive display applications across large areas with low fabrication complexity and cost.
Hydrogel electrolytes are pivotal for flexible zinc-ion batteries (ZIBs) yet suffer from an intrinsic trade-off between mechanical robustness and ionic conductivity. Herein, drawing inspiration from the “adhesion-conduction” architecture of spider webs, we developed a hierarchical hydrogel electrolyte (MTP) by incorporating tannic acid (TA)-modified MXene nanosheets (MT) into a polyacrylamide (PAM) skeleton to construct uniform 3D ion-conductive pathways. This bioinspired hierarchy serves a dual function: The PAM framework ensures mechanical integrity, while the MT network creates directed low-resistance channels for Zn2+ transport. Specifically, the dense array of polar groups on MXene and phenolic hydroxyls on TA act as “sticky sites”, which accelerate desolvation kinetics and homogenize Zn2+ flux. Consequently, the MTP electrolyte achieves an impressive ionic conductivity of 27.69 mS cm−1 and a high Zn2+ transference number of 0.833. Enabled by this design, Zn//Zn symmetric cells demonstrate an ultralong lifespan of 4600 h (> 6 months) at 0.5 mA cm−2/0.5 mAh cm−2. Furthermore, Zn//Z-VO full cells exhibit outstanding cyclability, retaining 74.5% capacity after 2000 cycles at 2 A g−1 and maintaining durable operation for over 10,000 cycles at 5 A g−1. This work successfully translates a biological blueprint into a practical strategy for resolving the kinetic and stability challenges in high-performance flexible ZIBs.
The Fe–N–C single-atom catalyst represents a promising candidate for promoting the oxygen reduction reaction (ORR), which is crucial for fuel cell applications; yet, identifying optimal modification strategies to enhance its activity and stability remains challenging. Herein, the modulation of Fe–N–C catalysts via in-plane heteroatom doping and axial coordination is systematically investigated using integrated density functional theory (DFT) and machine learning (ML) approaches. The analysis reveals that axial ligands have a more profound influence on ORR performance than in-plane dopants, primarily by modulating the Fe dz2 orbital and weakening *OH adsorption. Through interpretable descriptors extracted from ML models, the key electronic and geometric properties governing catalyst behavior are identified, and several novel dual-modified candidates with enhanced activity relative to pristine Fe–N–C are subsequently predicted and validated by DFT calculations. This work provides a unified mechanistic and data-driven framework for accelerating the design of high-performance Fe–N–C ORR electrocatalysts.
Using a simple physical model, researchers at the University of Osaka, The National Institute of Advanced Industrial Science and Technology (AIST), RIKEN, and the Institute of Science Tokyo revealed how superionic conduction emerges when mobile ions undergo sublattice melting while the surrounding crystal framework remains intact. Near this transition, ions move cooperatively in spatially heterogeneous, string-like patterns, aided by anharmonic lattice motion. The findings identify general physical principles connecting lattice softness and collective transport, potentially guiding more efficient development of solid electrolytes for batteries and energy-conversion technologies.
Detecting the overall characteristics of culture medium components as fluorescence patterns and evaluating quality using machine learning
Increasing atmospheric carbon dioxide (CO₂) levels pose a significant global challenge, making the assessment of natural carbon sinks, such as forests, ever more critical. New research conducted in the Garhwal region of the Indian Himalayas investigates how different forest types contribute to carbon storage across varying altitudes. The investigation by Arvind Singh, Vinod Prasad Khanduri, and colleagues identifies distinct carbon partitioning strategies, where high-altitude conifer forests excel at storing carbon in biomass, while lower-elevation mixed broadleaf forests are superior in stabilizing soil carbon. This understanding is vital for developing effective climate mitigation plans.
In an innovative gas fermentation process, reducing the concentration of carbon dioxide was found to significantly improve microbial production of the biodegradable plastic, poly[(R)-3-hydroxybutyrate]. Researchers found that hydrogen-oxidizing bacteria grown under safe, nonflammable gas conditions enable more efficient production of biodegradable plastic at lower CO2 levels. The study provides a promising strategy for sustainable carbon recycling and efficient CO2 utilization.