Multifunctional iron-biochar composites for the removal of potentially toxic elements, inherent cations, and hetero-chloride from hydraulic fracturing wastewater
Bibliographic record
Abstract
This paper evaluates a novel sorbent for the removal of potentially toxic elements, inherent cations, and hetero-chloride from hydraulic fracturing wastewater (FWW). A series of iron-biochar (Fe-BC) composites with different Fe/BC impregnation mass ratios (0.5:1, 1:1, and 2:1) were prepared by mixing forestry wood waste-derived BC powder with an aqueous FeCl 3 solution and subsequently pyrolyzing them at 1000 °C in a N 2 -purged tubular furnace. The porosity, surface morphology, crystalline structure, and interfacial chemical behavior of the Fe-BC composites were characterized, revealing that Fe chelated with C O bonds as C O Fe moieties on the BC surface, which were subsequently reduced to a C C bond and nanoscale zerovalent Fe (nZVI) during pyrolysis. The performance of the Fe-BC composites was evaluated for simultaneous removal of potentially toxic elements (Cu(II), Cr(VI), Zn(II), and As(V)), inherent cations (K, Na, Ca, Mg, Ba, and Sr), hetero-chloride (1,1,2-trichlorethane (1,1,2-TCA)), and total organic carbon (TOC) from high-salinity (233 g L −1 total dissolved solids (TDS)) model FWW. By elucidating the removal mechanisms of different contaminants, we demonstrated that Fe-BC (1:1) had an optimal reducing/charge-transfer reactivity owing to the homogenous distribution of nZVI with the highest Fe 0 /Fe 2+ ratio. A lower Fe content in Fe-BC (0.5:1) resulted in a rapid exhaustion of Fe 0 , while a higher Fe content in Fe-BC (2:1) caused severe aggregation and oxidization of Fe 0 , contributing to its complexation/(co-)precipitation with Fe 2+ /Fe 3+ . All of the synthesized Fe-BC composites exhibited a high removal capacity for inherent cations (3.2–7.2 g g −1 ) in FWW through bridging with the C O bonds and cation-π interactions. Overall, this study illustrated the potential efficacy and mechanistic roles of Fe-BC composites for (pre-)treatment of high-salinity and complex FWW.
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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 | 0.000 | 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".