Graphene Oxide-Supported Lanthanide Metal–Organic Frameworks with Boosted Stabilities and Detection Sensitivities
Bibliographic record
Abstract
The photoluminescent (PL) properties of lanthanide metal–organic frameworks (Ln-MOFs) are intrinsically subtle to water molecules, which remains the major challenge that severely limits their applications as fluorescent probes in aqueous samples. Herein novel composite fluorescent probes were prepared by growing Ln-MOFs (Tb-MOF, Eu-MOF, and Tb/Eu-MOF) on carboxylated porous graphene oxide (PGO-COOH). The 3D thorny composites presented significantly longer fluorescent lifetimes and higher quantum yields than that of the bare Ln-MOFs and exhibited long-term PL stabilities in aqueous samples up to 15 days. The stable and improved PL properties demonstrated that the highly hybrid composite structures protected the MOF components from the adverse effects of water. Furthermore, the unexpected antenna effect of the PGO-COOH substrate on Ln 3+ was supposed to be another reason for the improved PL properties. The composites present ultralow detection limits as low as 5.6 nM for 2,4-dinitrotoluene and 2.3 nM for dipicolinic acid as turn-off and ratiometric fluorescent probes, respectively, which was attributed to the incoporation of PGO-COOH that dramatically enahnced inner filter effects and effectively protected the energy transfer process in the MOF components from the interference of the surrounding water. This work presents an effective strategy for creating ultrasensitive and stable fluorescent probes based on Ln-MOFs for applications in aqueous samples.
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".