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Invasive plants pose a significant threat to natural ecosystems because of their high adaptability, rapid propagation and spreading ability in the environment. In this study, a typical aquatic invasive plant, Pistia stratiotes, was chosen as a novel feedstock for the preparation of nitrogen-doped biochars (NBs) for the first time, and the NBs were used as efficient sorbents to remove phthalate esters (PAEs) from aqueous solution. Characterization results showed that NBs possess great pore structure (up to 126.72 m g), high nitrogen (2.02%-2.66%) and ash (24.7%-34.1%) content, abundant surface functional groups, hydrophobicity and a graphene structure. Batch sorption experiments were performed to investigate the sorption performance, processes and mechanisms. The capacities for PAEs sorption onto NBs were high, especially with NBs pyrolyzed at 700 °C, ranging up to 161.7 mg g for diethyl phthalate and 85.4 mg g for dibutyl phthalate; these levels were better than many reported for other sorbents. With kinetic and isotherm results, Pseudo-second order and Freundlich models fit the sorption data well, and chemical interactions involving hydrogen bonding, Lewis acid-base interaction, functional group interaction, cation-π interaction and π-π stacking interaction were identified as possible rate-limited steps. Moreover, Intra-particle diffusion and Dubinin-Radushkevich models indicated that multiple pore filling and partitioning dominated the process of PAEs sorption onto NBs. This study opens the door for new methods of pollution control with waste treatment, since invasive plant biomass resources were converted into advanced biochars for efficient environmental remediation.
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http://dx.doi.org/10.1016/j.chemosphere.2021.130712 | DOI Listing |
J Econ Entomol
September 2025
European Biological Control Laboratory (EBCL USDA ARS), Montferrier-sur-lez, France.
Evaluating the olfactory preferences of emerging insect pests is critical to develop monitoring tools and improve early detection and management strategies. Here the chemical ecology and olfactory preferences of the allium leafminer Phytomyza gymnostoma Loew (Diptera: Agromyzidae), an invasive pest in North America affecting allium crops such as leeks and onions, were investigated. Three bioassay methods were assessed under laboratory conditions: wind tunnel, Y-tube olfactometer, and arena bioassay.
View Article and Find Full Text PDFPlant J
September 2025
State Key Laboratory of Plant Diversity and Specialty Crops, Wuhan Botanical Garden, Chinese Academy of Science, Wuhan, Hubei, 430074, China.
Trapa L. is a non-cereal aquatic crop with significant economic and ecological value. However, debates over its classification have caused uncertainties in species differentiation and the mechanisms of polyploid speciation.
View Article and Find Full Text PDFPLoS Negl Trop Dis
September 2025
Department of Clinical Science, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Background: Salmonella enterica encompasses over 2,600 serovars, including several commonly associated with severe infection in humans. Salmonella is a major cause of sepsis in Africa; however, diagnosis requires clinical microbiology facilities. Environmental surveillance has the potential to play a role in Salmonella surveillance.
View Article and Find Full Text PDFOecologia
September 2025
Marine Biological Laboratory, Woods Hole, MA, 02543, USA.
Beech leaf disease (BLD) poses a serious threat to the health of beech forests throughout the northeastern USA and Canada. Caused by invasive nematodes, BLD first appeared in 2012 in Ohio and has rapidly spread eastward. We investigated the effects of BLD on leaf and litter chemistry and leaf litter decomposition rate from four infected beech stands in Falmouth, Massachusetts.
View Article and Find Full Text PDFPest Manag Sci
September 2025
AgResearch Ltd, Tuhiraki, Lincoln, New Zealand.
Background: Conventional weed risk assessments (WRAs) are time-consuming and often constrained by species-specific data gaps. We present a validated, algorithmic alternative, the model, that integrates climatic suitability ( ), weed-related publication frequency (P) and global occurrence data ( ), using publicly available databases and artificial intelligence (AI)-assisted text screening with a large language model (LLM).
Results: The model was tested against independent weed hazard classifications for New Zealand and California.