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  • 1.
    Peets, Pilleriin
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för material- och miljökemi (MMK).
    Wang, Wei
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för material- och miljökemi (MMK).
    MacLeod, Matthew
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för miljövetenskap.
    Breitholtz, Magnus
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för miljövetenskap.
    Martin, Jonathan W.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för miljövetenskap. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Kruve, Anneli
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för material- och miljökemi (MMK).
    MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS2022Inngår i: Environmental Science and Technology, ISSN 0013-936X, E-ISSN 1520-5851, Vol. 56, nr 22, s. 15508-15517Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS2). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC50) values from the CompTox database and validated for 219 chemicals and 420 MS2 spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC50 values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC50 values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or logKOW. 

  • 2.
    Wang, Wei
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för material- och miljökemi (MMK). Lanzhou Jiaotong University, China.
    Chen, Keyu
    Sun, Yan
    Zhou, Shiqi
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för material- och miljökemi (MMK).
    Zhang, Miao
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för material- och miljökemi (MMK).
    Yuan, Jiayin
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för material- och miljökemi (MMK).
    Mesoporous Ni-N-C as an efficient electrocatalyst for reduction of CO2 into CO in a flow cell2022Inngår i: Applied Materials Today, ISSN 2352-9407, Vol. 29, artikkel-id 101619Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Recently, nitrogen-doped porous carbon materials containing non-precious metals (termed “M-N-C”) have formed a group of functional materials to replace precious metal-based catalysts for electrochemical CO2 reduction reaction. Here, a series of mesoporous Ni-N-C electrocatalysts (termed “mp-Ni-N-Cs”) were prepared via a gel-template method, and could effectively reduce CO2 into CO in a flow cell. The result in gas sorption tests exhibited a typical mesoporous structure, which would bring both sufficient exposed active sites and convenient mass transfer channels. Electrochemical tests showed excellent performance at an applied potential of -1.3 V (vs. RHE), e.g., a CO Faradaic efficiency (FECO) of 95.85 %, and a CO reduction current (jCO) of -21.29 mA cm−2. Significantly, its FECO exceeded 93 % in a wide range of potentials from -1.0 to -1.5 V, showing great tolerance to fluctuation in potential. The mp-Ni-N-C electrocatalysts have satisfactory features in terms of catalytic activity, facile preparation, and economic feasibility, and will offer a valuable reference for next exploration of cost-effective electrocatalysts for CO2 conversion.

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