75 As Nuclear Magnetic Resonance Spectroscopic Investigation of the Thioarsenate Speciation in Strongly Alkaline Sulfidic Leaching Solutions.
Erica BrendlerKarsten MeinerJörg WaglerAlexandra ThiereAlexandros CharitosMichael StelterPublished in: Molecules (Basel, Switzerland) (2024)
Copper ores and concentrates thereof feature an increasingly notable content of impurities such as arsenic and other hazardous elements. As an alternative to the state-of-the-art partial roasting process, arsenic could be removed by the alkaline sulfide leaching of the copper concentrates. In order to optimize and understand the processes, knowledge of the speciation and oxidation states is essential. In addition to methods such as UV/Vis spectroscopy, chromatography and ICP/MS methods, 75 As NMR spectroscopy may be useful for the differentiation and quantification of the various species. Although arsenate(V) has been characterized by 75 As NMR some time ago, to our knowledge, there are no data on tetrathioarsenate(V) AsS 4 3- and the mixed oxygen/sulfur substituted mono-, di- and trithioarsenates(V) AsO x S 4- x 3- , x = 3, 2, 1, respectively. Therefore, we investigated several model solutions and samples from Cu-As leaching with 75 As NMR. The strongly alkaline conditions of the leaching solution proved to be very advantageous for that purpose. Both the tetrathioarsenate(V) and the mixed species AsO x S 4- x 3- ( x = 1-3) could be characterized and provide valuable data for the quantification of the material flows in the leaching process.
Keyphrases
- heavy metals
- magnetic resonance
- sewage sludge
- municipal solid waste
- anaerobic digestion
- high resolution
- risk assessment
- solid state
- mass spectrometry
- healthcare
- molecular docking
- electronic health record
- drinking water
- big data
- machine learning
- multiple sclerosis
- magnetic resonance imaging
- ms ms
- hydrogen peroxide
- computed tomography
- liquid chromatography
- deep learning
- pseudomonas aeruginosa
- staphylococcus aureus
- atomic force microscopy
- candida albicans
- neural network
- organic matter