Emerging Contaminants is a leading platform for research addressing the potential ecological and human health risks associated with environmental contamination from emerging contaminants.

This article collection features papers published in Environmental Surfaces and Interfaces since 2025 on the theme of materials-driven environmental pollutant removal. It focuses on the development of novel materials and their practical applications in environmental pollutant remediation. All articles are freely available for reading.

The high O–H bond strength of common alcohols prevents their direct hydrogen atom transfer (HAT) activation. By leveraging the β-silicon effect in α-silyl alcohols, the research group of Professor Xiao Shen at Wuhan University weaken the O–H bond to enable redox-neutral, photoinduced homolysis using thioxanthone. The resulting alkoxy radicals undergo Brook rearrangement to carbon radicals, which are intercepted by nickel catalysis for enantioconvergent cross-couplings with acyl chlorides and aryl bromides. This protocol tolerates substrates inert under classical SET/PCET conditions and establishes direct HAT as a versatile platform.

From rhythmic activity in localized populations of cortical neurons to dengue hotspots that repeatedly reappear across Medellín, Colombia, localized oscillations shape collective dynamics in complex networked systems ranging from the brain to urban epidemics. However, existing explanations are often constrained by strict requirements on system dimensionality. This study reveals a more general mechanism for the emergence of localized oscillations, showing how collective dynamics can transcend these conventional constraints and broadening the theoretical framework for understanding localized collective behavior.

HER2-positive breast cancer accounts for about 15% of all breast cancers, and although anti-HER2 targeted therapies have dramatically improved patient survival, primary and acquired drug resistance remain major clinical challenges. In a comprehensive review, researchers from Fudan University Shanghai Cancer Center, Kunming Medical University, and collaborating institutions map ten major categories of resistance mechanisms, evaluate strategies to overcome them—including next-generation antibody-drug conjugates—and highlight predictive tools that are paving the way toward precision treatment.

Researchers at Zhejiang University have developed AtomDiT, a machine learning framework that builds a digital twin of dislocations from a single high-resolution electron micrograph. The twin simultaneously reveals dislocation type and position, local stress, and chemical fields. Demonstrated across metals and alloys, including dual-phase systems and hydrogen-segregated grain boundaries, AtomDiT uncovers how stress and chemistry govern dislocation motion, offering a new route to understanding crystal deformation and damage.