Bioproduction of cerium-bearing magnetite and application to improve carbon-black supported platinum catalysts
Bibliographic record
Abstract
Abstract Background Biogeochemical processing of metals including the fabrication of novel nanomaterials from metal contaminated waste streams by microbial cells is an area of intense interest in the environmental sciences. Results Here we focus on the fate of Ce during the microbial reduction of a suite of Ce-bearing ferrihydrites with between 0.2 and 4.2 mol% Ce. Cerium K-edge X-ray absorption near edge structure (XANES) analyses showed that trivalent and tetravalent cerium co-existed, with a higher proportion of tetravalent cerium observed with increasing Ce-bearing of the ferrihydrite. The subsurface metal-reducing bacterium Geobacter sulfurreducens was used to bioreduce Ce-bearing ferrihydrite, and with 0.2 mol% and 0.5 mol% Ce, an Fe(II)-bearing mineral, magnetite (Fe(II)(III) 2 O 4 ), formed alongside a small amount of goethite (FeOOH). At higher Ce-doping (1.4 mol% and 4.2 mol%) Fe(III) bioreduction was inhibited and goethite dominated the final products. During microbial Fe(III) reduction Ce was not released to solution, suggesting Ce remained associated with the Fe minerals during redox cycling, even at high Ce loadings. In addition, Fe L 2,3 X-ray magnetic circular dichroism (XMCD) analyses suggested that Ce partially incorporated into the Fe(III) crystallographic sites in the magnetite. The use of Ce-bearing biomagnetite prepared in this study was tested for hydrogen fuel cell catalyst applications. Platinum/carbon black electrodes were fabricated, containing 10% biomagnetite with 0.2 mol% Ce in the catalyst. The addition of bioreduced Ce-magnetite improved the electrode durability when compared to a normal Pt/CB catalyst. Conclusion Different concentrations of Ce can inhibit the bioreduction of Fe(III) minerals, resulting in the formation of different bioreduction products. Bioprocessing of Fe-minerals to form Ce-containing magnetite (potentially from waste sources) offers a sustainable route to the production of fuel cell catalysts with improved performance. Graphical Abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".