The global accumulation of plastic waste poses a serious threat to wildlife and ecosystems. Catalytic processes that convert plastic waste into valuable chemicals and fuels offer a promising solution. However, the recycling of real-life plastic waste mixtures remains a challenge due to their highly diverse composition and structural complexity. Accurate identification of the components within plastic waste mixtures is a prerequisite for their effective separation and recycling.Solid-state nuclear magnetic resonance (NMR) spectroscopy, which has the advantage of directly analyzing insoluble samples, provides detailed information into local atomic structure, molecular motion, interactions, and chemical environment—making it a powerful tool for studying complex polymer systems.In a study published in Nature, Prof. XU Shutao from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with the team of Prof. WANG Meng and Prof. MA Ding from Peking University, reported a advance in the indentification, separation, and catalytic transformation of real-life plastic waste mixtures.The researchers utilized an innovative solid-state NMR method—1H-13C Frequency Switched Lee Goldburg Heteronuclear Correlation (FSLG-HETCOR) NMR. By optimizing key parameters such as spinning rate, contact time, and homonuclear decoupling field strength, and using 13C-labeled tyrosine hydrochloride as a reference, the researchers obtained high-resolution “fingerprint” spectra of individual plastic components from an eight-plastic mixture containing PS (polystyrene), PLA (polylactic acid), PU (polyurethane), PC (polycarbonate), PVC (polyvinyl chloride), PET (polyethylene terephthalate), PE (polyethylene), and PP (polypropylene).The method obtained spectra with high signal intensity and good resolution in the indirect dimension, allowing for precise identification of various functional groups in the plastic mixture and enabling real-time tracking of their chemical evolution.Furthermore, the researchers demonstrated the feasibility, effectiveness, and universality of this method by monitoring the full catalytic separation and transformation of real-life plastic waste mixtures. Their NMR-based analysis enabled the mapping of each step in the conversion process—from complex mixtures to multiple high-value chemicals products.By identifying characteristic functional group signals in plastic waste mixtures, the researchers laid a solid foundation for their effective separation and transformation. This work paves the way for integrating existing transformation processes into a unified framework, providing technical support for scalable industrial solutions to global plastic pollution."Solid-state NMR provides a powerful way to identify individual components in plastic waste mixtures. It acts as a 'guiding eye' for the separation and catalytic transformation processes, laying the technological foundation for real-life solutions to plastic pollution," said Prof. XU.

Polymeric membranes are widely used in separation technologies due to their low cost and easily scalable fabrication. Unlike inorganic nanoporous materials such as metal-organic frameworks and covalent organic frameworks which feature periodic and ordered channels, polymeric membranes produced through traditional methods—such as phase separation—typically have irregular and disordered pore structures. This structural limitation makes it difficult to accurately separate ions or molecules of similar sizes, leading to a trade-off between selectivity and permeability.In a recent study published in Nature Chemical Engineering, a research team led by Prof. LI Xianfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS) developed a novel interfacial polymer cross-linking strategy to fabricate ultra-thin polymeric membranes with nanoscale separation layers.The newly developed strategy first confines polymer cross-linking reactions within a limited interfacial space, and then induces polymer film solidification through solvent-nonsolvent exchange.Fabrication route and structure of 3-μm-thick polymeric membranes(Image by LIU Xiaonan and LU Wenjing)Using this strategy, researchers constructed a nanoscale cross-linked separation layer on top of a polymeric supporting layer. The stable, covalently cross-linked structure enabled the overall membrane thickness to be reduced to just 3 μm. By varying the cross-linking time and types of agents, researchers could tune the thickness and morphology of the separation layer. The cavities between the polymer chains ranged from 1.8 to 5.4 Å in size—forming a quasi-ordered reticular structure that allows for precise, angstrom-scale ion sieving.This structure simultaneously achieves high ion selectivity and low resistance, effectively overcoming the traditional permeability and selectivity trade-off. The membrane's high size-sieving capability and low-transport resistance resulted in a vanadium flow battery with an energy efficiency of 82.38% at 300 mA/cm2."We have developed a simple strategy to reduce membrane thickness, which significantly lowers ion-transport resistance. Our study addresses long-standing challenges in polymeric membrane design and provides significant insight for both membrane-based separation and energy storage technologies," said Prof. LI.

Proton exchange membrane (PEM) water electrolysis is a significant technology for producing large-scale green hydrogen. While Platinum on Carbon (Pt/C) is a state-of-the-art cathode catalyst due to its moderate hydrogen binding energy and high resistance to acid corrosion, its high Pt loadings significantly increase costs.Schematic illustration for confinement of single-site Pt by the enriched asymmetric  electrons with boosted activity and stability for acidic HER (Image by XU Mingxia)In a study published in Joule, a research team led by Prof. DENG Dehui and Prof. YU Liang from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Prof. LU Junling from the University of Science and Technology of China (USTC) and Prof. YU Hongmei from the DICP, developed a highly efficient and stable catalyst for acidic hydrogen evolution. This catalyst employs enriched asymmetric π electrons on the surface of a chainmail structure to create a unique confinement effect, enhancing both the activity and stability of surface-confined platinum (Pt) atoms.The researchers designed a chainmail catalyst consisting of a cobalt-nickel (CoNi) nano-alloy encapsulated in a monolayer of graphene. They discovered that electron transfer from CoNi to the carbon layer, along with 3d-2p electronic interaction, led to an enrichment of asymmetric π electronic states on the graphene surface. After depositing Pt single atoms using atomic layer deposition, these enriched asymmetric π electrons exhibited a unique confinement effect on the Pt atoms.This confinement operates through two synergistic mechanisms: First, electron transfer from CoNi to Pt via the graphene layer results in an electron-rich Pt site, optimizing hydrogen adsorption energy and promoting hydrogen desorption, thereby improving catalytic activity. Second, strong interactions between the asymmetric π electrons and the Pt 5d orbital enhance the structural stability of Pt sites, boosting the durability of the catalyst.Furthermore, the researchers assembled a proton exchange membrane (PEM) water electrolyzer with this catalyst. It achieved an ultra-high current density of 4.0 A cm−2 at 2.02 V and maintained excellent durability over 1,000 hours at 2 A cm−2, using only 1.2 μgPt cm−2 Pt loading. Moreover, they assembled a 2.85 kW PEM water electrolyzer using this catalyst operated stably for over 300 hours at an industrial current density of 1.5 A cm−2, highlighting its outstanding industrial application potential."This work provides a new idea for developing high-performance, long-life and low-cost catalysts for hydrogen production via acid water electrolysis," said Prof. DENG.

Zeolites such as ZSM-5 are essential components in industrial catalysis, with widespread applications in petroleum refining, coal conversion, and environmental remediation.In practice, industrial catalysts typically consist of both "zeolitic components" and "non-zeolitic components" (e.g., silica, alumina, amorphous aluminosilicate, clays), which together play important roles in processes like polyolefin catalytic cracking and residue fluid catalytic cracking. However, most research to date has focused mainly on the diffusion behaviors of a single zeolitic component or non-zeolitic component, neglecting the interfacial interactions between them.In a study published in the Journal of the American Chemical Society, a research team led by Prof. XU Shutao, Prof. WEI Yingxu, and Prof. LIU Zhongmin from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Prof. YAN Zifeng's team from China University of Petroleum (East China), investigated the diffusion and cracking kinetics of zeolite-based multicomponent model catalysts.The researchers designed a series of model cracking catalysts composed of ZSM-5 and mesoporous SiO2 with precisely controlled interfacial channel connectivity. These catalysts enabled systematic exploration of structure–diffusion–reaction relationships at zeolite and non-zeolite interfaces. By employing advanced characterization techniques—including hyperpolarized (HP) 129Xe nuclear magnetic resonance (NMR), intelligent gravimetric analysis (IGA), and time-resolved in situ Fourier-transform infrared (FTIR) spectroscopy—the researchers quantitatively analyzed both global and zeolitic diffusion behaviors in these multicomponent systems.Their findings revealed a pronounced "funnel effect" from the mesopores in non-zeolitic component, showing that a well-connected micro-/mesopore network can effectively accelerate interfacial diffusion and fully enhance the catalytic efficiency of the zeolitic component. This highlights the foundational functions of pore interconnectivity in facilitating the free migration of reactant species between zeolitic and non-zeolitic components."Our study showed that for the elaborate design of industrial multicomponent catalysts, more attention should be placed on constructing highly interconnected hierarchical pore structures—both within and between zeolitic and non-zeolitic components—as well as tuning their surface properties," said Prof. XU.Catalytic efficiency assessment of multicomponent model catalysts in catalytic cracking of model intermediates（Image by XU Yipu）

The traditional development of catalysts has long depended on trial-and-error methods—an approach that is time—consuming and often yields inconsistent data. To advance the precision and efficiency of catalyst design, transitioning to a data-driven, automated paradigm has become imperative.In a perspective article published in Matter, a research group led by Prof. DENG Dehui from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Dr. LI Haobo's group from Nanyang Technological University, systematically reviewed the transformative role of artificial intelligence (AI) in the design and synthesis of heterogeneous catalysts. The perspective also outlines future directions for AI-driven innovations in this field.Schematic illustration of the AI-driven catalyst design and synthesis paradigm, and the development of automated chemical synthesis techniques alongside the advancements in machine learning methods (Image by ZHANG Longhai and BING Qiming)The perspective highlights machine learning (ML) as a powerful tool for predicting catalyst structure-property relationships, optimizing synthesis conditions, and enabling high-throughput automated calculations and experiments. By identifying key performance descriptors, ML reduces reliance on resource-intensive theoretical calculations such as density functional theory (DFT), thereby accelerating the catalyst discovery process. Advanced techniques such as active learning and generative models further enhance design efficiency by prioritizing critical experiments and proposing novel catalyst candidates.A central focus of this perspective is the development of AI-powered closed-loop systems that integrate automated synthesis, characterization, and optimization. These systems improve data quality, minimize human error, and ensure reproducibility across the entire catalyst development cycle.The authors also pointed out current challenges, including the limited generalizability of AI models across diverse catalytic systems, the difficulty of integrating multidisciplinary datasets, and the need for better anomaly detection in automated workflows. To address these, the authors proposed technological roadmaps emphasizing cross-institutional data sharing and adaptive AI frameworks."This Perspective provides a blueprint for transitioning catalysis research toward fully automated and intelligent paradigms, unlocking unprecedented efficiency in catalyst development," said Prof. DENG.

Aqueous zinc-ion batteries (AZIBs) are regarded as promising next-generation solution for large-scale energy storage, offering advantages such as high safety, low cost, and environmental friendliness. However, under practical high-rate and long-cycling conditions, Zn anodes suffer from interfacial challenges–including hydrogen evolution, self-corrosion, and dendrite growth–that hinder performance and life of the batteries.In a recent study published in Angewandte Chemie International Edition, Prof. CHEN Zhongwei's team from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences proposed a novel π-electron delocalization-based strategy for additive molecule screening. The team established a clear structure–function relationship between additive molecular design and interfacial behavior, providing a new avenue for optimizing AZIB performance.By leveraging the self-assembly behavior of trace functional organic molecules at the electrode interface, the researchers constructed a flexible hydrophilic–hydrophobic interfacial layer (HHIL) on the Zn anode surface. This HHIL enhances interfacial stability and electrochemical reversibility, opening a new pathway toward high-performance AZIBs.Molecular structure screening of additives and the schematic diagram of severe HER, chemical corrosion, non-uniform Zn2+ deposition, and dendrite growth on the Zn anode in BE (Image by SANG Yudong)The researchers identified N-hydroxyphthalimide (NHPI) as a functional additive, featuring a rigid quasi-planar structure, strong π-electron delocalization, and a high positive electrostatic potential. Through π–π stacking and ion–dipole interactions, NHPI spontaneously self-assemble on the Zn surface to form a robust HHIL. During cycling, this HHIL further promotes the formation of an inorganic sublayer rich in ZnF2 and ZnS, resulting in a dual-layer structure that combines flexible organic and rigid inorganic interfaces.This cooperative interface effectively regulatess Zn2+ deposition, suppresses parasitic reactions and dendrite growth, and enhances interfacial stability.As a result, Zn//Zn symmetric cells achieved stable cycling over 900 hours at a high current density of 20 mA cm-2 with a capacity of 10 mAh cm-2. Moreover, full Zn//NaV3O8·1.5H2O (NVO) cells delivered more than 25,000 cycles at 10 A g-1 with 85% capacity retention, outperforming baseline systems. The strategy also demonstrated high rate performance and practical applicability in soft-pack pouch cells."Our study establishes a full-link mechanism from molecular design to interfacial construction to performance enhancement," said Prof. CHEN. "It offers both theoretical insights and experimental evidence for additive molecule design and interface regulation in AZIBs, accelerating their development for long-life, high-energy-density energy storage applications," added Prof. CHEN.