Ho(III)-Based Metal–Organic Framework for Water Pollution Treatment: Insights into Sensitive Phosphate Removal and Sensing in Aqueous Solution
Bibliographic record
Abstract
The existence of phosphate in aquatic ecosystems generates eutrophication. As a novel alternative to tackling this problem, this study reported a Ho(III)-based metal–organic framework (MOF) for removing and detecting phosphate molecules. Ho-bpdc MOF shows an outstanding maximum adsorption capacity of 311.9 mg g –1, with high pH (4–10) stability. PSO and Freundlich’s models reveal that the chemical interaction between the phosphate molecules and Ho-bpdc plays the main role during the removal process. The combination of FTIR and XPS characterizations confirmed the possible interaction mechanism involving a ligand-exchanged process. Thus, the formation of Ho–O–P sites was determined due to the relatively strong base character for the phosphate ion. Moreover, Ho-bpdc exhibited outstanding fluorescence properties for the sensing of phosphate molecules. The fluorescence experiments showed a LOD of 2.57 ppm, based on the solid-state emission spectra of Ho-bpdc at different phosphate concentrations (5–100 ppm). Ho-bpdc exhibited a characteristic turn-on effect after the phosphate interaction due to the increments of the electronic π* → π transition of ligands. This work demonstrated the remarkable properties of Ho-bpdc in the adsorption and sensing of phosphate.
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".