Cu-TCPP Metal–Organic Nanosheets Embedded Thin-Film Composite Membranes for Enhanced Cyanide Detection and Removal: A Multifunctional Approach to Water Treatment and Environmental Safety
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Cyanide is highly toxic, with widespread industrial use posing serious environmental risks. Effective materials for detecting and filtering cyanide from water are urgently needed. This study introduces a novel approach utilizing Cu-TCPP (TCPP = o -tetra(4-carboxyphenyl)porphine) metal–organic nanosheets (MONS) embedded in thin-film composite membranes, offering a multifunctional solution for cyanide detection and filtration. Ultrathin Cu-TCPP MONs were synthesized using a surfactant-assisted method featuring highly accessible metal centers that enhance cyanide interaction and detection. The membranes, developed by modifying cellulose acetate (CA) with Cu-TCPP MONs, demonstrated exceptional performance for cyanide removal. The 6% Cu-TCPP/CA membrane exhibited a 2.3-fold increase in pure water permeability and achieved a cyanide removal efficiency of 94.68%, significantly outperforming the pristine 0% Cu-TCPP/CA membrane (Pure Water Permeability (PWP) = 380.83 L m –2 h –1 bar –1; CN – removal = 5.01%). This is the first report describing the detection and removal of CN – in water using the membrane technique in literature. In addition to its removal efficiency, the Cu-TCPP MONs showed remarkable detection capabilities, with a calculated limit of detection of 1.76 × 10 –7 M, surpassing World Health Organization (WHO) and United States Environmental Protection Agency (EPA) safety standards for cyanide levels in water. Additionally, Cu-TCPP MONs, a bioimaging agent with excellent cell viability, were deployed to detect CN – in MiaPaCa-2 cells, detecting concentrations as low as 0.1 ppm.
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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) | 0.001 | 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".