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Record W2897193710 · doi:10.1149/ma2018-02/52/1780

Non-Precious Group Metal Electrocatalysis of Sulfite Electro-Oxidation Reaction for Gold Electrowinning Processes

2018· article· en· W2897193710 on OpenAlexaffabout
Pooya Hosseini-Benhangi, Davood Nakhaie, Yeonuk Choi, Janet Y. Baron, Edouard Asselin

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsBarrick Gold (Canada)University of British Columbia
Fundersnot available
KeywordsElectrowinningThiosulfateInorganic chemistryAnodeMaterials scienceSulfiteElectrochemistryElectrocatalystMetallurgyOxygen evolutionChemistryElectrodeSulfur

Abstract

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The electro-oxidation of sulfite and its radicals has a wide range of applications from gold electrowinning to wastewater treatment, hydrogen production and in the food industry (1-4). While efforts have been made to study the electro-oxidation of sulfite ions on noble metals (e.g. Au) as well as carbon surfaces, metal oxides in general have not been thoroughly investigated for this electrochemical reaction (1, 5). During gold recovery from thiosulfate solutions, Au + ions are reduced to Au from the thiosulphate gold complex on the cathode while oxygen evolution occurs on the anode via the following reactions (6): Au(S 2 O 3 ) 2 3- + e - = Au + 2S 2 O 3 -2 , E 0 = 0.15 V (1) 4OH - = 2H 2 O + O 2 + 4e - , E 0 = 0.4 V (2) To drive the cathodic reaction, high current densities are used, which result in high anodic potentials on the stainless-steel anodes. Such high anodic potentials may cause transpassive corrosion of the anode as well as significant, and undesirable, changes in solution chemistry (7). This study systematically investigated the effect of operating factors on the corrosion behavior of stainless steel anodes in thiosulfate solutions during gold electrowinning. Further, novel non-precious group materials such as manganese oxides and perovskites were used as electrocatalysts for sulfite electro-oxidation to ultimately enhance the activity and long-term durability of the anodes in the gold electrowinning process (8). A 2 n half-fraction factorial design study was performed to find the effect of five important parameters involved in the gold electrowinning process (i.e. thiosulfate concentration, sulfite concentration, sulfate concentration, temperature and applied anodic current density), on the anodic potential of the stainless steel anode. As shown in the Fig. 1A, increasing the sulfite concentration and temperature of the gold electrowinning solution lead to a low anodic potential of 550 mV which is about 250 mV lower than the transpassive corrosion potential for the 316 stainless steel anodes. To further reduce the anodic potential and employ the high sulfite concentration (i.e. 3000-32000 ppm) in the gold electrowinning solution, non-precious metal oxides were tested to facilitate the electrowinning process by catalyzing the following sulfite electro-oxidation reaction instead of the oxygen evolution reaction (eq. 2): SO 3 2- + 2OH - = SO 4 2- + H 2 O + 2e - , E 0 = -0.93 V (3) Fig. 1B shows that a combination of manganese oxide and perovskite electrocatalysts provides promising electrocatalytic activity and durability for sulfite electro-oxidation (i.e. up to 500 mV lower onset-potential over 7 hours of testing at 3.5 mAcm -2 ) compared to the Ni, commercial Ag/AgO, Pt, carbon, commercial Pb-2 wt% Ag and stainless steel electrodes tested here. Fig. 1 A) Surface plot produced from the 2 n half-fraction factorial design study on the effect of five electrowinning parameters on the performance of 316 stainless steel gold electrowinning anode. B) A comparison between the electrocatalytic activity of various electrocatalyst materials for sulfite electro-oxidation in a solution of 100 ppm NaCl + 3000 ppm Na 2 SO 4 + 32000 ppm Na 2 SO 3 at 3.5 mA cm -2 and 45 °C. All MnO 2 -based catalysts are under the license with Catalyst Square Materials Ltd (8). References: P. Westbroek, J. De Strycker, K. Van Uytfanghe and E. Temmerman, Journal of Electroanalytical Chemistry , 516 , 83 (2001). J. A. Allen, G. Rowe, J. T. Hinkley and S. W. Donne, International Journal of Hydrogen Energy , 39 , 11376 (2014). R. Ojani, J. B. Raoof and A. Alinezhad, Electroanalysis , 14 , 1197 (2002). E. M. Contreras, Industrial & Engineering Chemistry Research , 47 , 9709 (2008). A. G. Zelinsky and B. Y. Pirogov, Electrochimica Acta , 231 , 371 (2017). C. Abbruzzese, P. Fornari, R. Massidda, F. Vegliò and S. Ubaldini, Hydrometallurgy , 39 , 265 (1995). N. J. Laycock, R. C. Newman and J. Stewart, Corrosion Science , 37 , 1637 (1995). E. Gyenge and P. Hosseini-Benhangi, An oxygen electrode and a method of manufacturing the same, in, U.S. (15/251,267) and Canadian (2,940,921) patent applications (Filed on August 30, 2016). Figure 1

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.237
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2018
Admission routes2
Has abstractyes

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