Relationship Between Sulfidated Nano Zero Valent Iron and a Reductive Dechlorinating Microbial Culture - Synergistic or Antagonistic?
Bibliographic record
Abstract
Sulfidated nano zerovalent iron (S-nZVI) has garnered significant attention from researchers due to its potential for effective in-situ remediation applications. Compared to bare nZVI, sulfidation process enhances its reactivity towards chlorinated volatile organic compounds (cVOCs) and improves its longevity (Nunez Garcia et al. 2021). Stabilizing the particles with a polymer, like carboxymethyl cellulose (CMC), can further improve the performance of S-nZVI by imparting higher stability, less toxicity towards microbial cells, and a potential biostimulatory effect, making CMC-S-nZVI a promising in-situ remediation technology (Nunez Garcia et al. 2021). Recently, CMC-S-nZVI has also been applied for field-scale remediation (Nunez Garcia et al. 2020;Brumovský et al. 2021). The contaminated sites usually have multiple pollutants and not all can be degraded by CMC-S-nZVI, thus, leaving some recalcitrant cVOCs untreated (Zhang et al. 2021). Biodegradation of cVOCs by dechlorinating microbial cultures may generate highly toxic intermediates like vinyl chloride (Kocur et al. 2016). However, coupling the two treatments may be able to compensate for each other’s drawbacks, resulting in higher efficiency, longer effectiveness, non-accumulation of intermediates, and degradation of a wider range of target contaminants. However, interacting effects of CMC-S-nZVI on dechlorinating microbial cultures have not been studied yet. This research investigates the potential of combining CMC-S-nZVI and a reductive dechlorinating microbial culture (KB-1) to degrade trichloroethylene (TCE) and 1,2-dichloroethane (1,2-DCA). CMC-S-nZVI was synthesized by a two-step method: (1) CMC-nZVI was first synthesized by reducing ferrous sulfate-CMC solution with dropwise addition of sodium borohydride solution with continuous mixing and (2) then sodium dithionite solution was added as a sulfidation agent to the freshly-synthesized CMC-nZVI (Nunez Garcia et al. 2020). Effects of different sulfur-iron ratios (S/Fe), iron, and CMC concentrations on TCE degradation were studied to obtain an effective CMC-S-nZVI formulation. Results showed a successful TCE removal by the CMC-S-nZVI but 1,2-DCA was not degraded. TCE degradation by CMC-S-nZVI fitted the first-order kinetic model, with the highest degradation rate constant (0.35 h -1 ) achieved at S/Fe = 0.1 with iron and CMC concentrations of 1 gL -1 and 0.4 wt%, respectively. This CMC-S-nZVI formulation was further tested to examine its interaction with KB-1 in terms of cVOCs dechlorination and microbial population responses. A four-day aged CMC-S-nZVI was also tested to study the effect of aging. Degradation pathways for TCE and 1,2-DCA were proposed, based on the formation of degradation products. For the coupled treatment, an increase in microbial abundance was observed by quantifying DNA concentrations. This demonstrated a synergistic relationship between CMC-S-nZVI and KB-1. Unlike the CMC-S-nZVI only treatment, microcosms containing both CMC-S-nZVI and KB-1 were found to successfully degrade the 1,2-DCA. The coupled treatment degraded TCE and 1,2-DCA at faster rates and generated lesser amounts of vinyl chloride than the KB-1 only treatment, confirming the biostimulatory effect of CMC-S-nZVI. In the KB-1 only treatment with CMC as the sole carbon and energy source, TCE and 1,2-DCA were successfully dechlorinated. Transmission electron microscopy illustrated that CMC-S-nZVI particles were attached to the microbes but did not penetrate the bacterial cells. In summary, synergistic abiotic-biotic dechlorination of TCE and 1,2-DCA was achieved by the combined treatment of CMC-S-nZVI and KB-1, suggesting that multi-contaminant sites can benefit from this approach. Additionally, the four-day aged CMC-S-nZVI performed similar to the freshly-synthesized one, demonstrating that the field-scale remediation can have a more feasible time scale for the preparation and application of these amendments.
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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 | 0.001 |
| 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".