Meichen Liu

Changchun University of Chinese Medicine

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Jiyan Li

Lanzhou University of Technology

Wenwen Luo

Agro-Environmental Protection Institute

Hanxue Sun

Lanzhou University of Technology

An Li

Beijing Technology and Business University

Guoqiang Li

Nankai University

Bin Lin

University of Electronic Science and Technology of China

Ruishen Li

Inner Mongolia Agricultural University

Shengwei Zhang

Peking University Shenzhen Hospital

Yunjia Wang

Shenzhen University

Qiang Zhao

University of Electronic Science and Technology of China

Cooperative Institutions

Lanzhou University of Technology

Inner Mongolia Agricultural University

Harbin Institute of Technology

Nanjing University of Posts and Telecommunications

Nankai University

University of Electronic Science and Technology of China

Dalian University of Technology

Chinese Academy of Sciences

Research Institute of Petroleum Exploration and Development

Jilin University

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Effects of phosphate, silicate, humic acid, and calcium on the release of As(V) co-precipitated with Fe(III) and Fe(II) during aging

Journal of Hazardous Materials (2022)

Erdan Hu Meichen Liu Feng Wang Bosheng Lv Jun Wu

10.1016/j.jhazmat.2022.129478

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Citations (6)

Two-photon polymerization-based 3D micro-scaffolds toward biomedical devices

Chemical Engineering Journal (2024)

Wei Wang Ziqing Chen Bin Lin Meichen Liu Yang Zhang

Two-photon excitation microscopy

10.1016/j.cej.2024.152469

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Citations (6)

Efficient antibiotic wastewater treatment via interfacial water evaporation based soot-coated conjugated microporous polymer hollow microspheres

Chemical Engineering Journal (2023)

Wenwen Luo Meichen Liu Rui Jiao Yunjia Wang Jiyan Li

10.1016/j.cej.2023.146226

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Citations (16)

Nutrient Stimulation of Indigenous Microorganisms for Oil-in-Water Emulsion in a Medium Temperature Petroleum Reservoir with Ca2+-Rich Brine

Geofluids (2021)

Guoxin Shi Yuanyuan Jia Lei Bai Guoqiang Li Xiaoli Liu

One of the challenges indigenous microbial enhanced oil recovery (MEOR) is facing is the high percentage of divalent ions, which obstruct the growth and metabolism of microorganisms and destabilize the oil-in-water (o/w) emulsion. Six formulas were selected for the stimulation of indigenous microbes and to compare their performances on the oil emulsification and oil spreading in the Luliang oilfield containing Ca2+-rich brine. Illumina MiSeq sequencing of 16S rRNA genes was applied to investigate the structural response of microbial communities to various formulas. The results showed that the addition of proper organic phosphorus and the optimal P/N ratio (0.01) can facilitate production of biosurfactant and create stable o/w emulsion with specific reservoir condition containing Ca2+-rich brine, through direct stimulation of certain functional microbes. This study provides a promising path for direct enrichment of biosurfactant-producing and oil-degrading Dietzia genus and a potential instructional approach of indigenous MEOR in Luliang oilfield.

Brine

Halomonas

10.1155/2021/6659038

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Mining soil heavy metal inversion based on Levy Flight Cauchy Gaussian perturbation sparrow search algorithm support vector regression (LSSA-SVR)

Ecotoxicology and Environmental Safety (2024)

Meng Luo Meichen Liu Shengwei Zhang Jing Gao Xiaojing Zhang

Soil heavy metal pollution in mining areas poses severe challenges to the ecological environment. In recent years, machine learning has been widely used in heavy metal inversion by hyperspectral data. However, deterministic algorithms and probabilistic algorithms may confront local optimal solutions in practical applications. The local optimal solution is not the optimal value obtained within the entire defined interval, and as a result will affect the reliability of these approaches. This paper proposes a Levy Flight Cauchy Gaussian perturbation Sparrow Search algorithm Support Vector Regression (LSSA-SVR) soil heavy metal content prediction model. It introduces Levy Flight (LF) measurement and Cauchy Gaussian perturbation based on the Sparrow search algorithm. The LSSA-SVR model was shown to increase the breadth of solutions searched, avoiding the local optimal solution problem. When applied to mining soil heavy metal experiments, we found that the LSSA-SVR model gave a good fit for the elements Cu, Zn, As, and Pb. The correlation coefficients between the predicted results and the actual results of the four elements were all above 0.94. The heavy metal predicted results of LSSA-SVR have a small error margin in both the overall distribution and in individual differences. This study provides an efficient and accurate monitoring method for mining soil heavy metal inversion. It also provides strong support for environmental management and soil remediation.

10.1016/j.ecoenv.2024.117295

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Excellent solar-driven interface evaporation by an oil repellence Janus photothermal membrane for oily wastewater treatment

Chemical Engineering Journal (2024)

Wenwen Luo Jia Zhang Meichen Liu Anli Yi Rui Jiao

10.1016/j.cej.2024.149211

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Citations (11)