Long-Term Field Study of Microbial Community and Dechlorinating Activity Following Carboxymethyl Cellulose-Stabilized Nanoscale Zero-Valent Iron Injection
Bibliographic record
Abstract
Nanoscale zerovalent iron (nZVI) is an emerging technology for the remediation of contaminated sites. However, there are concerns related to the impact of nZVI on in situ microbial communities. In this study, the microbial community composition at a contaminated site was monitored over two years following the injection of nZVI stabilized with carboxymethyl cellulose (nZVI-CMC). Enhanced dechlorination of chlorinated ethenes to nontoxic ethene was observed long after the expected nZVI oxidation. The abundance of Dehalococcoides (Dhc) and vinyl chloride reductase (vcrA) genes, monitored using qPCR, increased by over an order of magnitude in nZVI-CMC-impacted wells. The entire microbial community was tracked using 16S rRNA gene amplicon pyrosequencing. Following nZVI-CMC injection, a clear shift in microbial community was observed, with most notable increases in the dechlorinating genera Dehalococcoides and Dehalogenimonas. This study suggests that coupled abiotic degradation (i.e., from reaction with nZVI) and biotic degradation fueled by CMC led to the long-term degradation of chlorinated ethenes at this field site. Furthermore, nZVI-CMC addition stimulated dehalogenator growth (e.g., Dehalococcoides) and biotic degradation of chlorinated ethenes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".