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LY
Verified CAS / Academic Author3 Decoded Studies

Prof. LIN Youjing

Anhui University of Science and Technology

Co-Affiliations:Hainan Ecological Environmental Monitoring Center, Haikou, 571126, China

Research Publications & English Decoded Briefs

Showing 3 publications
New Carbon Materials2026DOI: 10.1016/S1872-5805(26)61067-0

Loading of Nano-Bimetallic Catalysts onto Coal Tar Pitch-Based Activated Carbon Fibers for Efficient Reduction of p-Nitrophenol

The reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP) in wastewater faces challenges in conversion rate and stability. We used coal tar pitch-based activated carbon fibers (ACFs) as a support material for loading transition metal catalysts to catalyze the reaction. Fe–Ni nanoparticles were loaded onto the coal tar pitch-based ACF through a simple hydrothermal–calcination method. The results showed that the coal tar pitch-based ACFs had a high specific surface area (1847 m2/g) and a unique microporous structure, and the metals were loaded onto them. The average diameter of the nanoparticles formed was approximately 100 nm. By changing the metal loading it was shown that the performance was best when the reaction temperature was 45 °C, the 4-NP concentration was 2.5 mmol L−1, and the molar concentration ratio of Fe3+ to Ni2+ was 1∶2 (total 7.5 mmol L−1). Under these conditions the conversion efficiency reached 99.88%. Fe2.5/Ni5–ACF exhibited excellent catalytic activity and recyclability for 4-NP after five cycles. The inherent advantages of nanomaterials increase the catalytic efficiency of 4-NP, which expands the use of coal tar pitch-based ACFs as supporting materials in the field of catalysis.

Environmental Chemistry2026DOI: 10.7524/j.issn.0254-6108.2026012804

Characteristics and Source Apportionment of Volatile Organic Compounds at a Roadside Site in a Tropical City during Summer

This study conducted online monitoring of volatile organic compounds (VOCs) at a roadside site on a main arterial road in Haikou, a tropical city, during summer 2023 (June 25–September 30). A total of 56 VOCs were measured. The mean total VOC concentration (φ(TVOCs)) was (9.05 ± 6.24) nmol·mol−1, with concentrations in the order: alkanes > alkenes > aromatic hydrocarbons > alkynes, dominated by light alkanes. Alkenes and aromatic hydrocarbons contributed significantly to atmospheric chemical reactivity, while secondary organic aerosol formation potential (SOAFP) was limited, influenced by both VOC concentrations and temperature. VOC concentrations exhibited a pronounced bimodal diurnal pattern, consistent with traffic peaks. Ratio analysis indicated a Toluene/Benzene (T/B) ratio slightly higher than typical vehicle exhaust values, and an iso-Pentane/n-Pentane (i/n) ratio suggesting fuel evaporation influence. Positive Matrix Factorization (PMF) identified four sources: gasoline/LPG vehicle exhaust (49.9%), solvent use or vehicle evaporation (26.1%), diesel vehicle exhaust (14.9%), and biogenic sources (9.1%). SOAFP was mainly contributed by solvent use/evaporation (35.7%), gasoline/LPG exhaust (34.6%), diesel exhaust (22.0%), and biogenic sources (7.7%). These findings indicate that under tropical summer high-temperature conditions, roadside VOC pollution is predominantly traffic-related, with vehicle evaporation sources non-negligible, providing insights for evaluating vehicular impacts on particulate pollution.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-4057-4

Machine Learning-Assisted Rapid Development of High Performance Flexible Lead-Free Radiation Shielding Gels

The escalating use of ionizing radiation in medical and industrial applications necessitates lead-free, flexible, and sustainable shielding materials. Current development relies on empirical trial-and-error, which is inefficient. This study introduces a machine learning-assisted Monte Carlo simulation strategy for rapid optimization of metal filler compositions for X-ray attenuation across 40–120 kV. Guided by this AI-driven approach, polyvinyl alcohol (PVA)-based gels containing uniformly dispersed Bi/W/Gd2O3 nanoparticles were developed, forming within 1 minute at -20°C using a PVA-DMSO/H2O co-solvent system. The optimized gel with 50 wt% metal loading exhibits exceptional mechanical properties: tensile strength of 1.76 MPa, toughness of 6.3 MJ m−3, and elongation of 600%. It achieves >98% X-ray shielding efficiency at 5 mm thickness, outperforming lead composites at 120 kV. The physically cross-linked network provides recyclability and anti-freezing capability, retaining flexibility at -50°C. This work establishes a data-driven paradigm for designing high-performance radiation-shielding materials, demonstrating AI's potential to accelerate materials discovery and enable scalable fabrication of eco-friendly protective systems.