Engineering metal sources to fabricate BiOI@ZIF composites with dual-mechanism adsorption and photocatalysis of industrial dyes
Bibliographic record
Abstract
A sustainable photocatalyst was prepared by a facile room temperature process that was activated by an ultra-low powered, long-life, and toxic-free LED lamp. Herein, variants of ZIF-67 structures were synthesized by varying the cobalt counterions (acetate, chloride, nitrate, and sulfate), which were integrated into BiOI, a visible-light catalyst, to prepare BiOI@ZIFs-67 with synergistic adsorption and catalytic performance. The photocatalytic performance was monitored via decolorization of dyes: Rhodamine B (RhB), methylene blue (MB), and crystal violet (CV). The relative photocatalytic activity was observed: BiOI@ZIF-67-NO 3 > BiOI@ZIF-67-SO 4 > BiOI@ZIF-67-Cl > BiOI@ZIF-67-OAC. The dye removal (%) of BiOI@ZIF-67-NO 3 was observed: MB (56.8 %), RhB (98.3 %), and CV (79.2 %). Compared to pure BiOI, BiOI@ZIF-67-NO 3 exhibited an 8.9-fold increase in its surface area, a 33.5-fold increase in pore volume, and lower photoluminescence (PL) intensity. This variation in molecular structure was reflected by variable dye removal efficiency for each dye: CV =1.17-fold, RhB =1.45-fold, and MB =1.1-fold higher dye removal for the BiOI@ZIF-67-NO 3 composite. Optimal conditions for complete decolorization of RhB are also listed: pH 6.5, BiOI@ZIF-67-NO 3 dose (0.61 g/L, and irradiation time (=52 min). The mechanistic role of superoxide was shown by its inhibition with reduced dye decolorization from complete removal to 76.5 %. The pseudo-first-order kinetic process described a rapid drop in RhB concentration. Moreover, the Langmuir maximum RhB dye adsorption capacity (q max ) for BiOI increased from 13.6 mg/g to 16.9 mg/g for BiOI@ZIF-67-NO 3 . A suppression in the decolorization ca. 2 %–16 % for environmental water versus a control (deionized water). The phytotoxicity of treated, raw, and control water samples were assessed using cress and mung bean seeds, where undetectable toxic effects of treated water were supported by favorable germination index and root length.
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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".