Metal-Binding Ligands Rather Than Redox Active Metabolites Are Essential to Microbially-Induced Corrosion of Cobalt
Bibliographic record
Abstract
Abstract Microbially-induced corrosion (MIC) has been well-studied in the context of damage to iron-bearing infrastructure, where microbes can solubilize solid elemental iron using redox-active metabolites such as phenazines. Whether such pathways can solubilize economically-critical cobalt remains poorly understood. We hypothesized that secondary metabolites produced by the model bacterium Pseudomonas chlororaphis subspecies aureofaciens associated with MIC of iron could corrode cobalt by oxidation ( i.e., phenazines) or by acting as a ligand ( i.e., cyanide). One-week incubations using live cells supplied with Co(0) wires led to 20-30% cobalt mass losses and ∼2200 µM Co(2+) recovered in solution, which was at least 3-fold higher than filtered cultures and sterile medium controls. Removing the capacity for cells to produce phenazines and cyanide showed similar corrosion compared to wild type cells and phenazine standards did not corrode cobalt, ruling these metabolites out as contributors to MIC. Further experiments testing whether metabolite mixtures devoid of phenazines could corrode Co(0) in the presence and absence of oxygen confirmed that atmospheric oxygen initiates Co(0) oxidation, and that unidentified cell-derived metabolites drive MIC forward by keeping Co(2+) from precipitating as oxides that form a passivation layer on the wire surface. This revised mechanistic explanation underscores the importance of considering how abiotic redox cycling and cellular metabolites that solubilize metals interact in the MIC of non-ferrous metals. Identifying the biosynthetic pathways involved in solubilizing cobalt will be key to reframing MIC as more than an environmental concern and optimizing sustainable cobalt recovery strategies for solid waste that rely on naturally-occurring microbial metabolites. Importance Characterizing microbially-induced corrosion mechanisms for cobalt is important for predicting the fate of critical metals in the environment and optimizing sustainable strategies for cobalt reclamation from electronic waste. This study uses the model bacterium Pseudomonas chlororaphis subspecies aureofaciens to test if redox-active phenazines and the metal-binding metabolite cyanide can corrode elemental cobalt. We repeatedly observed that live cells corroded cobalt, but that corrosion did not rely on phenazines or cyanide. Instead, our results show that oxygen oxidized elemental cobalt and unidentified metabolites keep cobalt ions in solution to limit cobalt oxide precipitation and drive the corrosion reaction forward. Our study reinforces the need to consider connections between abiotic and biotic reactions in the corrosion of metals other than iron. Understanding these mechanisms is critical to reframing microbial corrosion as a sustainable approach for metal recovery and our study makes the case that it is possible for the critical metal cobalt.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".