Liquid crystals harness dynamic fluid lattice motion to activate topochemical polymerization beyond conventional geometric constraints, yielding recyclable polymers with dissipative fluorescence readout.

Superconducting diodes, which enable dissipationless one-way current flow, have long been limited by low operating temperatures and scalability. Now, writing in National Science Review, a team from Nanjing University reports scalable high-temperature superconducting diodes based on intrinsic Josephson junctions. The devices operate up to 86 K, show tunable nonreciprocity, and exhibit zero-field memory effects, providing a practical route toward energy-efficient superconducting electronics.

Researchers have developed a tandem depolymerization and hydrogenolysis strategy that selectively converts polystyrene waste into toluene with 99 % selectivity and 83.5 wt% yield. The two‑stage process first subjects polystyrene to hydropholysis at 475 °C to generate aromatic intermediates, followed by vapor‑phase hydrogenolysis at 275 °C and 0.4 MPa over a ruthenium single‑atom modified Co₃O₄ catalyst (RuSA/Co₃O₄). The system is compatible with various disposable polystyrene‑based raw materials, delivering approximately 81 wt% yield and >99 % selectivity for toluene. This work represents a transformative paradigm for polystyrene valorization, moving from non‑selective deconstruction toward selective synthesis.

Researchers have developed a tandem depolymerization and hydrogenolysis strategy that selectively converts polystyrene waste into toluene with 99 % selectivity and 83.5 wt% yield. The two‑stage process first subjects polystyrene to hydropholysis at 475 °C to generate aromatic intermediates, followed by vapor‑phase hydrogenolysis at 275 °C and 0.4 MPa over a ruthenium single‑atom modified Co₃O₄ catalyst (RuSA/Co₃O₄). The system is compatible with various disposable polystyrene‑based raw materials, delivering approximately 81 wt% yield and >99 % selectivity for toluene. This work represents a transformative paradigm for polystyrene valorization, moving from non‑selective deconstruction toward selective synthesis.

Artificial intelligence is fundamentally transforming chemical engineering across all sub‑disciplines, from reaction engineering and catalyst design to process control, materials discovery, and plant operations. This review highlights how machine learning and digital twins are enabling real‑time process optimization, predictive safety monitoring, and sustainable manufacturing. Foundation models and autonomous laboratories are accelerating molecular and materials discovery, while large language models extract structured knowledge from scientific literature. The convergence of AI with chemical engineering is driving a paradigm shift toward smarter, greener, and more efficient production systems.